metal : support virtual devices (#18919 )

* metal : support virtual devices * cont : manage buffer type context memory * metal : add events * cont : implement cpy_tensor_async
model-conversion : add debug option to conversion script (#19265 )
2026-05-21 16:34:05 +00:00 · 2026-02-02 14:29:44 +02:00 · 2026-02-02 11:29:57 +01:00 · 2026-02-02 10:00:05 +01:00 · 2026-02-02 08:51:25 +02:00 · 2026-02-02 08:38:55 +02:00
51 changed files with 1623 additions and 538 deletions
--- a/1085
+++ b/1085
--- a/2
+++ b/2
@@ -1,6 +1,6 @@
 MIT License

-Copyright (c) 2023-2024 The ggml authors
+Copyright (c) 2023-2026 The ggml authors

 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
--- a/common/ngram-map.cpp
+++ b/common/ngram-map.cpp
@@ -7,6 +7,18 @@
 #include <cstdio>
 #include <sstream>

+// prime number used for LCG hash function (32 bit), it is near (sqrt(5) - 1)/2 * 2^32.
+#define LCG_FACTOR 2654435761UL
+
+// Compute the LCG hash of a n-gram of size len at offset start.
+static uint32_t common_ngram_map_hash(const llama_tokens & tokens, size_t start, size_t len) {
+    uint32_t hash = 0;
+    for (size_t i = 0; i < len; ++i) {
+        hash = hash * LCG_FACTOR + tokens[start + i];
+    }
+    return hash;
+}
+
 // Print the values of a sublist of `llama_tokens & inp` to a string in the form [v0, v1, v2, ...].
 static std::string common_tokens_to_str(const llama_tokens & inp, size_t start, size_t length) {
    std::ostringstream oss;
@@ -115,6 +127,100 @@ llama_tokens common_ngram_simple_draft(
 // maximum number of counted values of a ngram map value.
 #define COMMON_NGRAM_MAX_VALUE_COUNT 16380

+void common_ngram_map_begin(
+    common_ngram_map & map, const llama_tokens & tokens) {
+    size_t size_begin = tokens.size();
+
+    LOG_DBG("%s: begin, idx_last_draft=%zu, new begin=%zu, #keys=%zu\n", __func__,
+            map.idx_last_check, size_begin, map.keys.size());
+
+    size_t count_map_entries_upd = 0;
+    if (!map.key_map.empty() && size_begin < map.idx_last_check) {
+        if (map.show_key_map_stats) {
+            // Print statistics of hash map map_key.
+            size_t count_nonzero = 0;
+            uint32_t min_idx = UINT32_MAX;
+            uint32_t max_idx = 0;
+            for (size_t i = 0; i < map.key_map.size(); ++i) {
+                uint32_t key_idx = map.key_map[i];
+                if (key_idx != 0) {
+                    ++count_nonzero;
+                    if (key_idx < min_idx) min_idx = key_idx;
+                    if (key_idx > max_idx) max_idx = key_idx;
+                }
+            }
+            if (count_nonzero == 0) {
+                min_idx = 0;
+            }
+            LOG_INF("%s: key_map stats: entries=%zu, min_idx=%u, max_idx=%u, key_map_last_idx=%u\n",
+                    __func__, count_nonzero, min_idx, max_idx, map.key_map_last_idx);
+        }
+
+        // Update the map from hash to key index (clear outdated entries).
+        for (size_t i = 0; i < map.key_map.size(); ++i) {
+            uint32_t key_idx = map.key_map[i];
+            if (key_idx >= map.size_last_begin) {
+                map.key_map[i] = 0;
+                count_map_entries_upd++;
+            }
+        }
+        map.key_map_last_idx = (map.size_last_begin > 0) ? map.size_last_begin - 1 : 0;
+    }
+
+    if (size_begin < map.idx_last_check && !map.keys.empty()) {
+        // The next token generation will start at index size_begin.
+        // The tokens between map.size_last_begin and size_begin are no longer valid.
+        //
+        // Refresh map: Remove all entries with index >= map.size_last_begin.
+        size_t count_keys = map.keys.size();
+        size_t count_keys_del = 0;
+        size_t count_values_del = 0;
+        for (int32_t i = map.keys.size() - 1; i >= 0; --i) {
+            common_ngram_map_key & key = map.keys[i];
+            if (key.key_idx >= map.size_last_begin) {
+                // Delete the key.
+                LOG_DBG("%s: delete key %d at index %zu (>= size_last_begin=%zu)\n", __func__, i, key.key_idx, map.size_last_begin);
+                map.keys.erase(map.keys.begin() + i);
+                count_keys_del++;
+                continue;
+            }
+            if (map.key_only) {
+                continue;
+            }
+
+            // Check the indices of the values.
+            for (int16_t j = COMMON_NGRAM_MAX_VALUES - 1; j >= 0; --j) {
+                common_ngram_map_value & value = key.values[j];
+                if (value.value_idx >= map.size_last_begin) {
+                    // Delete the value.
+                    count_values_del++;
+
+                    // Move all values after this value to the left.
+                    for (uint16_t k = j; k < COMMON_NGRAM_MAX_VALUES - 1; ++k) {
+                        key.values[k] = key.values[k + 1];
+                    }
+                    // Clear the last value.
+                    key.values[COMMON_NGRAM_MAX_VALUES - 1].value_idx = 0;
+                    key.values[COMMON_NGRAM_MAX_VALUES - 1].value_num = 0;
+                }
+            }
+            if (key.values[0].value_idx == 0) {
+                // No values left, delete the key.
+                LOG_DBG("%s: delete key %d at index %zu (no values left)\n", __func__, i, key.key_idx);
+                map.keys.erase(map.keys.begin() + i);
+                count_keys_del++;
+            }
+        }
+
+        LOG_INF("%s: refresh map: idx_last_draft=%zu, new begin=%zu, #keys_checked=%zu, #keys_del=%zu, #values_del=%zu, #hashes_upd=%zu\n", __func__,
+                map.idx_last_check, size_begin,
+                count_keys, count_keys_del, count_values_del, count_map_entries_upd);
+    }
+
+    map.idx_last_check = (map.size_last_begin > 0) ? map.size_last_begin - 1 : 0;
+    map.size_last_begin = size_begin;
+}
+
 void common_ngram_map_draft(common_ngram_map & map,
        const llama_tokens & inp, llama_token sampled,
        llama_tokens & draft) {
@@ -129,6 +235,10 @@ void common_ngram_map_draft(common_ngram_map & map,
    if (cur_len < static_cast<size_t>(2 * n + m)) {
        return;
    }
+    if (cur_len >= static_cast<size_t>(UINT32_MAX)) {
+        // key_map uses uint32_t instead of size_t.
+        GGML_ABORT("%s: cur_len exceeds UINT32_MAX: %zu", __func__, cur_len);
+    }

    // Only check every check_rate tokens to save compute
    // i.e., perform check if (cur_len - idx_last_check) >= check_rate
@@ -147,24 +257,92 @@ void common_ngram_map_draft(common_ngram_map & map,

    // search for the key in the map
    size_t match_pos = 0;
-    for (size_t j = cur_len - n - m - 1; j > 0; --j) {
-        bool match = true;
-        for (size_t k = 0; k < n; ++k) {
-            if (inp[j + k] != key_tokens[k]) {
-                match = false;
-                break;
+    if (map.size_last_begin > cur_len) {
+        GGML_ABORT("%s: map.size_last_begin > cur_len: %zu > %zu", __func__, map.size_last_begin, cur_len);
+    }
+    if (!map.key_map.empty()) {
+        // Search for the key in the map key_map from hash of ngrams to index of ngram.
+        uint32_t idx_hash = (common_ngram_map_hash(key_tokens, 0, n) % map.key_map.size());
+        uint32_t idx_key = map.key_map[idx_hash];
+        if (idx_key != 0 && idx_key < cur_len - n - m - 1) {
+            // Check if the key matches the key at idx_key (because of possible collisions).
+            bool match = true;
+            for (size_t k = 0; k < n; ++k) {
+                if (inp[idx_key + k] != key_tokens[k]) {
+                    match = false;
+                    break;
+                }
+            }
+            LOG_DBG("%s: key hash %x -> idx_key %d: match %d\n", __func__, idx_hash, idx_key, match ? 1 : 0);
+            if (match) {
+                match_pos = idx_key;
            }
        }
-        if (match) {
-           match_pos = j;
-           break;
+    }
+    if (match_pos == 0 && map.size_last_begin > (size_t) (n + m + 1)) {
+        // Search for the key in [1, map.size_last_begin - n - m -1], descending.
+        for (size_t j = map.size_last_begin - n - m - 1; j > map.key_map_last_idx; --j) {
+            // Check if the key matches the key.
+            bool match = true;
+            for (size_t k = 0; k < n; ++k) {
+                if (inp[j + k] != key_tokens[k]) {
+                    match = false;
+                    break;
+                }
+            }
+            if (match) {
+               match_pos = j;
+               break;
+            }
+        }
+    }
+    if (match_pos == 0) {
+        // In case of a reasoning chat, the part after size_last_begin may be deleted/reordered later.
+        //
+        // Search in [size_last_begin, cur_len - n - m - 1], descending.
+        for (size_t j = cur_len - n - m - 1; j > map.size_last_begin && j > map.key_map_last_idx; --j) {
+            bool match = true;
+            for (size_t k = 0; k < n; ++k) {
+                if (inp[j + k] != key_tokens[k]) {
+                    match = false;
+                    break;
+                }
+            }
+            if (match) {
+               match_pos = j;
+               break;
+            }
        }
    }
    if (match_pos > 0) {
-        LOG_INF("%s: cur_len = %zu, n = %d, m = %d, sz_tkns = %zu, sampled = %d, match_pos = %zu\n", __func__,
+        LOG_DBG("%s: cur_len = %zu, n = %d, m = %d, sz_tkns = %zu, sampled = %d, match_pos = %zu\n", __func__,
            cur_len, n, m, key_tokens.size(), sampled, match_pos);
    }

+    if (!map.key_map.empty()) {
+        // Add hashes of new ngrams in key_map.
+        //
+        // Use the same order as above.
+        if (map.size_last_begin > (size_t) (n + m + 1)) {
+            for (size_t j = map.size_last_begin - n - m - 1; j > map.key_map_last_idx; --j) {
+                // compute hash and store index of ngram at idx j in the map.
+                uint32_t idx_hash = (common_ngram_map_hash(inp, j, n) % map.key_map.size());
+                if (map.key_map[idx_hash] == 0) {
+                    map.key_map[idx_hash] = j; // collisions may occur
+                }
+            }
+        }
+
+        for (size_t j = cur_len - n - m - 1; j > map.size_last_begin && j > map.key_map_last_idx; --j) {
+            // compute hash and store index of ngram at idx j in the map.
+            uint32_t idx_hash = (common_ngram_map_hash(inp, j, n) % map.key_map.size());
+            if (map.key_map[idx_hash] == 0) {
+                map.key_map[idx_hash] = j;
+            }
+        }
+        map.key_map_last_idx = std::max(static_cast<uint32_t>(cur_len - n - m - 1), map.key_map_last_idx);
+    }
+
    if (match_pos == 0) {
        return;
    }
@@ -215,8 +393,8 @@ void common_ngram_map_draft(common_ngram_map & map,
            draft.push_back(inp[match_pos + n + i]);
        }

-        LOG_INF("%s: key_offset = %zu, key_num = %d, draft.size = %zu\n", __func__,
-                key_offset, curr_key.key_num, draft.size());
+        LOG_DBG("%s: key_idx = %zu, key_offset = %zu, key_num = %d, draft.size = %zu\n", __func__,
+                curr_key.key_idx, key_offset, curr_key.key_num, draft.size());

        map.last_draft_created   = false;
        map.last_draft_key_idx   = key_offset;
@@ -318,7 +496,7 @@ void common_ngram_map_draft(common_ngram_map & map,
        }
    }

-    if (sum_occur > 0 && max_occur < 3 * sum_occur) {
+    if (sum_occur > 0 && max_occur < 2 * sum_occur) {
        // The most frequent value is not much more frequent than the other values.
        // We do not use the draft.
        return;
--- a/common/ngram-map.h
+++ b/common/ngram-map.h
@@ -9,6 +9,8 @@
 // 2. ngram_map: lookup of n-grams followed by m-grams in token history using a map.
 //    The map is a vector of key n-grams, and for each key n-gram there is a list of value m-grams.
 //
+// ref: https://github.com/ggml-org/llama.cpp/pull/18471
+//

 #include "llama.h"
 #include "common.h"
@@ -51,10 +53,13 @@ llama_tokens common_ngram_simple_draft(
 // maximum number of m-gram values stored for each key n-gram.
 #define COMMON_NGRAM_MAX_VALUES 4

+// number of entries in the (optional, size 0 to disable) map from ngram-hash to ngram-index.
+#define COMMON_NGRAM_HASH_MAP_SIZE 262144
+
 // statistics of a m-gram after a known n-gram
 struct common_ngram_map_value {
-    size_t   value_idx = 0;  // index of value m-gram in token-history (0 if unused)
-    uint16_t value_num = 0;  // number of occurences of this value m-gram after the key n-gram (0 in an unused values-slot)
+    size_t   value_idx =  0;  // index of value m-gram in token-history (0 if unused)
+    uint16_t value_num =  0;  // number of occurences of this value m-gram after the key n-gram (0 in an unused values-slot)
    int16_t n_accepted = -1;  // number of accepted tokens at last draft (-1 if unused)
 };

@@ -74,23 +79,43 @@ struct common_ngram_map {

    bool key_only;       // true if only key n-grams are used, no values.

-    // first draft: vector only, no map.
    std::vector<common_ngram_map_key> keys; // key n-grams which occur several times in token-history
    uint16_t check_rate; // check for speculative decoding without draft model for each check_rate token
    uint16_t min_hits;   // minimum number of key hits to consider a draft

+    bool     show_key_map_stats = false; // true, if statitics of the key_map should be printed.
+
    common_ngram_map(uint16_t sz_key, uint16_t sz_value, bool only_keys,
                     uint16_t check_rate, uint16_t min_hits)
        : size_key(sz_key), size_value(sz_value), key_only(only_keys),
-          check_rate(check_rate), min_hits(min_hits) {}
+          check_rate(check_rate), min_hits(min_hits) {
+        key_map.resize(COMMON_NGRAM_HASH_MAP_SIZE); // 2^18 hash entries, 0 entries if key_map shouldn't be used
+    }
+
+    // In reasoning chats the previous reasoning block will be removed from context history.
+    // A rebuild of the ngram map is needed after that.
+
+    size_t   size_last_begin      = 0; // number of tokens at previous start of generation

    bool     last_draft_created   = false; // true if a draft was created at last call.
-    size_t   last_draft_key_idx   = 0; // index of last key used for draft generation.
+    size_t   last_draft_key_idx   = 0; // index of last key used for draft generation (0 = no draft)
    uint16_t last_draft_value_idx = 0; // index of last value used for draft generation.

    size_t   idx_last_check       = 0; // index of last check in context history
+
+    // optional map "hash to ngram-index" for faster lookup of n-grams. map is empty if unused.
+    //
+    // uint32_t instead of size_t (size of current histories is << UINT32_MAX)
+    std::vector<uint32_t> key_map;              // key_map[hash] = index of ngram in context window
+    uint32_t              key_map_last_idx = 0; // index of the last ngram added to key_map
 };

+// Initialize the n-gram map with the given token history.
+// map:                the ngram map to initialize.
+// tokens:             the token history to base the map on.
+void common_ngram_map_begin(
+    common_ngram_map & map,
+    const llama_tokens & tokens);

 // Searches for the n-gram in the history and checks whether a draft sequence should be generated.
 // map:                the ngram map to search in.
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -124,9 +124,9 @@ struct common_speculative_state {
    // TODO: track performance of most recent calls
    const bool gen_perf = true; // whether to generate performance stats.

-    // TODO: rename to t_draft_us
-    // TODO: add t_begin_us, t_accept_us
-    int64_t gen_duration_us = 0; // total time spent in this implementation in microseconds.
+    int64_t t_begin_us  = 0; // total time spent in refresh of this implementation in microseconds.
+    int64_t t_draft_us  = 0; // total time spent in generating drafts in this implementation in microseconds.
+    int64_t t_accept_us = 0; // total time spent in accumulation of this implementation in microseconds.

    common_speculative_state(enum common_speculative_type type) : type(type) {}

@@ -499,7 +499,7 @@ struct common_speculative_state_ngram_map_k : public common_speculative_state {
        : common_speculative_state(type), map(std::move(map)) {}

    void begin(const llama_tokens & prompt) override {
-        GGML_UNUSED(prompt);
+        common_ngram_map_begin(map, prompt);
    }

    void draft(
@@ -951,7 +951,12 @@ void common_speculative_begin(common_speculative * spec, const llama_tokens & pr
    }

    for (auto & impl : spec->impls) {
+        const int64_t t_start_us = impl->gen_perf ? ggml_time_us() : 0;
+
        impl->begin(prompt);
+
+        const int64_t t_now_us = impl->gen_perf ? ggml_time_us() : 0;
+        impl->t_begin_us += t_now_us - t_start_us; // accumulate duration for this refresh
    }
 }

@@ -973,7 +978,7 @@ llama_tokens common_speculative_draft(
            const int64_t t_now_us = impl->gen_perf ? ggml_time_us() : 0;

            impl->drafts_call_count++;
-            impl->gen_duration_us += t_now_us - t_start_us; // accumulate duration for this implementation
+            impl->t_draft_us += t_now_us - t_start_us; // accumulate duration for this implementation
        }

        if (!result.empty()) {
@@ -1001,12 +1006,15 @@ void common_speculative_accept(common_speculative * spec, uint16_t n_accepted) {

    GGML_ASSERT(impl);

+    const int64_t t_start_us = impl->gen_perf ? ggml_time_us() : 0;
    if (n_accepted > 0) {
        impl->drafts_accepted_count++;
        impl->drafts_accepted_tokens += n_accepted;
    }

    impl->accept(n_accepted);
+    const int64_t t_now_us = impl->gen_perf ? ggml_time_us() : 0;
+    impl->t_accept_us += t_now_us - t_start_us; // accumulate duration for this acculumulation
 }

 void common_speculative_print_stats(const common_speculative * spec) {
@@ -1018,13 +1026,14 @@ void common_speculative_print_stats(const common_speculative * spec) {
        std::string str_perf;
        if (impl->gen_perf) {
            std::ostringstream oss;
-            oss << std::fixed << std::setprecision(3) << impl->gen_duration_us / 1000.0;
-            str_perf = ", dur = " + oss.str() + " ms";
+            oss << std::fixed << std::setprecision(3) << impl->t_begin_us / 1000.0 << ", ";
+            oss << std::fixed << std::setprecision(3) << impl->t_draft_us / 1000.0 << ", ";
+            oss << std::fixed << std::setprecision(3) << impl->t_accept_us / 1000.0;
+            str_perf = ", dur(b,g,a) = " + oss.str() + " ms";
        } else {
            str_perf = "";
        }

-        // TODO: report time for begin() and accept()
        LOG_INF("statistics %s: #calls = %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n",
                common_speculative_type_to_str(impl->type).c_str(),
                impl->drafts_call_count,
--- a/docs/multimodal/minicpmo2.6.md
+++ b/docs/multimodal/minicpmo2.6.md
@@ -9,7 +9,7 @@ Download [MiniCPM-o-2_6](https://huggingface.co/openbmb/MiniCPM-o-2_6) PyTorch m
 ### Build llama.cpp
 Readme modification time: 20250206

-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)

 Clone llama.cpp:
 ```bash
--- a/docs/multimodal/minicpmo4.0.md
+++ b/docs/multimodal/minicpmo4.0.md
@@ -8,11 +8,11 @@ Download [MiniCPM-o-4](https://huggingface.co/openbmb/MiniCPM-o-4) PyTorch model
 ### Build llama.cpp
 Readme modification time: 20250206

-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)

 Clone llama.cpp:
 ```bash
-git clone https://github.com/ggerganov/llama.cpp
+git clone https://github.com/ggml-org/llama.cpp
 cd llama.cpp
 ```

--- a/docs/multimodal/minicpmv2.5.md
+++ b/docs/multimodal/minicpmv2.5.md
@@ -8,7 +8,7 @@ Download [MiniCPM-Llama3-V-2_5](https://huggingface.co/openbmb/MiniCPM-Llama3-V-
 ### Build llama.cpp
 Readme modification time: 20250206

-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)

 Clone llama.cpp:
 ```bash
--- a/docs/multimodal/minicpmv2.6.md
+++ b/docs/multimodal/minicpmv2.6.md
@@ -8,7 +8,7 @@ Download [MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) PyTorch m
 ### Build llama.cpp
 Readme modification time: 20250206

-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)

 Clone llama.cpp:
 ```bash
--- a/docs/multimodal/minicpmv4.0.md
+++ b/docs/multimodal/minicpmv4.0.md
@@ -8,11 +8,11 @@ Download [MiniCPM-V-4](https://huggingface.co/openbmb/MiniCPM-V-4) PyTorch model
 ### Build llama.cpp
 Readme modification time: 20250731

-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)

 Clone llama.cpp:
 ```bash
-git clone https://github.com/ggerganov/llama.cpp
+git clone https://github.com/ggml-org/llama.cpp
 cd llama.cpp
 ```

--- a/docs/multimodal/minicpmv4.5.md
+++ b/docs/multimodal/minicpmv4.5.md
@@ -8,11 +8,11 @@ Download [MiniCPM-V-4_5](https://huggingface.co/openbmb/MiniCPM-V-4_5) PyTorch m
 ### Build llama.cpp
 Readme modification time: 20250826

-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)

 Clone llama.cpp:
 ```bash
-git clone https://github.com/ggerganov/llama.cpp
+git clone https://github.com/ggml-org/llama.cpp
 cd llama.cpp
 ```

--- a/docs/speculative.md
+++ b/docs/speculative.md
@@ -6,7 +6,7 @@ llama.cpp supports speculative decoding, a technique that can significantly acce

 ## Implementations

-The `llama-server` application supports several implementations of speculative decoding:
+The `llama-server` application supports several implementations of speculative decoding. An implementation with draft model can be mixed with an implementation without draft model.

 ### Draft Model (`draft`)

@@ -32,12 +32,21 @@ An example to use this approach can be the rewriting of source code by a LLM.

 This implementation looks for the last n-gram in history that matches the current n-gram and creates a draft using the m tokens following the matched n-gram. It is the simplest self-speculative approach with minimal overhead.

+```
+llama-server [...] --spec-type ngram-simple --draft-max 64
+```
+
 #### n-gram Map Key (`ngram-map-k`)

-This implementation looks for the current n-gram of size n (called the _key_) in the token history. If the key n-gram is followed by the same m tokens (called the _mgram_) multiple times, it creates a draft using these m tokens. This approach requires a minimum number of occurrences (argument `--spec-ngram-min-hits`) before generating drafts.
+This implementation looks for the current n-gram of size n (called the _key_) in the token history. If the key n-gram is followed by the same m tokens (called the _mgram_) multiple times, it creates a draft using these m tokens. This approach requires a minimum number of occurrences (argument `--spec-ngram-min-hits`, default is 1) before generating drafts.

 The number of accepted tokens is stored for each used n-gram.

+**Example:**
+```
+llama-server [...] --spec-type ngram-map-k --draft-max 64
+```
+
 #### n-gram Map Key-4-Values (`ngram-map-k4v`)

 This experimental implementation looks for the current n-gram of size n (called the _key_) in the token history. For each key, up to four _values_ (n-grams of size m, called _mgrams_) are tracked. An internal statistic counts the occurrences of each mgram after the key n-gram. If one mgram is significantly more frequent than the others, it is used as the draft.
@@ -45,17 +54,65 @@ This experimental implementation looks for the current n-gram of size n (called
 The number of accepted tokens is stored for each used n-gram.

 **Example:** Server options to be used if there are a lot of longer repetitions.
-```bash
-llama-server [...] --spec-type ngram-map-k4v --spec-ngram-size-n 8 --spec-ngram-size-m 8 --spec-ngram-min-hits 2
+```
+llama-server [...] --spec-type ngram-map-k4v --spec-ngram-size-n 8 --spec-ngram-size-m 8 --spec-ngram-min-hits 2 --draft-max 64
 ```

+### n-gram Mod (`ngram-mod`)
+
+Add basic ngram hasher for speculative decoding:
+
+- For each ngram, compute a hash using LCG
+- For each computed hash, store the next token
+- During speculation, iteratively compute the rolling hash of the last n tokens and pick the next token from the storage
+
+Some characteristics:
+
+- Lightweight (~16 MB)
+- Constant memory and complexity
+- Can generate variable draft lengths (i.e. m is not fixed)
+
+Currently, a single hash pool is shared across all server slots, so different requests can benefit from each other.
+
+**Sample usage:**
+
+```
+# notes:
+# - small `n` are not recommended
+# - MoEs require long drafts
+# - dense models: can reduce `--draft-min` and `--draft-max`
+
+llama-server ... --spec-type ngram-mod --spec-ngram-size-n 24 --draft-min 48 --draft-max 64
+```
+
+Applications:
+
+- Iterating over a block of text/code (e.g. in llama.vim)
+- Reasoning models (when they have to repeat their thinking in the final answer)
+- Summarization
+
+Example Video:
+
+- See #19164
+
+### Differences between ngram-simple, ngram-map and ngram-mod
+
+- ngram-simple looks for a previous matching n-gram and inserts the following m-gram.
+- ngram-map-k looks for a previous matching n-gram and inserts the following m-gram but uses an internal hash-map of n-grams in the current context window.
+- ngram-mod uses a hash pool which is shared across all server slots. The hash pool is a map from n-gram hash to the next token (not the next m-gram as in ngram-map).

 ## Command-Line Options

 If a draft model is combined with a draftless decoding the draftless decoding has higher precedence.

 ```
--spec-type [none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v]
+--draft, --draft-n, --draft-max N       number of tokens to draft for speculative decoding (default: 16)
+                                        (env: LLAMA_ARG_DRAFT_MAX)
+--draft-min, --draft-n-min N            minimum number of draft tokens to use for speculative decoding
+                                        (default: 0)
+                                        (env: LLAMA_ARG_DRAFT_MIN)
+[...]
+--spec-type [none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
                                        type of speculative decoding to use when no draft model is provided
                                        (default: none)
 --spec-ngram-size-n N                   ngram size N for ngram-simple/ngram-map speculative decoding, length
@@ -78,6 +135,7 @@ Specifies a type of speculative decoding without draft model.
 | `ngram-simple` | Use simple n-gram pattern matching |
 | `ngram-map-k` | Use n-gram pattern matching with n-gram-keys |
 | `ngram-map-k4v` | Use n-gram pattern matching with n-gram-keys and up to four m-gram values (experimental) |
+| `ngram-mod` | Use basic ngram hasher for speculative decoding with shared pool |

 **Example:** Server-instance used to refactor source code.
 ```bash
@@ -112,9 +170,15 @@ statistics ngram_simple: #calls = 15, #gen drafts = 5, #acc drafts = 5, #gen tok
 statistics draft: #calls = 10, #gen drafts = 10, #acc drafts = 10, #gen tokens = 110, #acc tokens = 98
 ```

+```
+draft acceptance rate = 0.70312 (   90 accepted /   128 generated)
+statistics ngram_mod: #calls = 810, #gen drafts = 15, #acc drafts = 15, #gen tokens = 960, #acc tokens = 730, dur(b,g,a) = 0.149, 0.347, 0.005 ms
+```
+
 - `#calls`: number of calls of this implementations
 - `#gen drafts`: number of drafts generated by this implementation
 - `#acc drafts`: number of drafts accepted (partially) by the main model
 - `#gen tokens`: number of tokens generated by this implementation (including rejected tokens)
 - `#acc tokens`: number of tokens accepted by the main model
+- `dur(b,g,a): durations of begin (new prompt), generation and accumulation (process acceptance).

--- a/examples/deprecation-warning/README.md
+++ b/examples/deprecation-warning/README.md
@@ -1,7 +1,7 @@
 # Migration notice for binary filenames

 > [!IMPORTANT]
-[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggerganov/llama.cpp/pull/7809)
+[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggml-org/llama.cpp/pull/7809)

 This migration was important, but it is a breaking change that may not always be immediately obvious to users.

--- a/examples/deprecation-warning/deprecation-warning.cpp
+++ b/examples/deprecation-warning/deprecation-warning.cpp
@@ -28,7 +28,7 @@ int main(int argc, char** argv) {
    fprintf(stdout, "\n");
    fprintf(stdout, "WARNING: The binary '%s' is deprecated.\n", filename.c_str());
    fprintf(stdout, " Please use '%s' instead.\n", replacement_filename.c_str());
-    fprintf(stdout, " See https://github.com/ggerganov/llama.cpp/tree/master/examples/deprecation-warning/README.md for more information.\n");
+    fprintf(stdout, " See https://github.com/ggml-org/llama.cpp/tree/master/examples/deprecation-warning/README.md for more information.\n");
    fprintf(stdout, "\n");

    return EXIT_FAILURE;
--- a/examples/json_schema_to_grammar.py
+++ b/examples/json_schema_to_grammar.py
@@ -402,7 +402,7 @@ class SchemaConverter:
            Transforms a regular expression pattern into a GBNF rule.

            Input: https://json-schema.org/understanding-json-schema/reference/regular_expressions
-            Output: https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md
+            Output: https://github.com/ggml-org/llama.cpp/blob/master/grammars/README.md

            Unsupported features: negative/positive lookaheads, greedy/non-greedy modifiers.

--- a/examples/model-conversion/Makefile
+++ b/examples/model-conversion/Makefile
@@ -33,11 +33,14 @@ DEVICE ?= auto
 causal-convert-model-bf16: OUTTYPE=bf16
 causal-convert-model-bf16: causal-convert-model

+causal-convert-model-debug: DEBUG=--debug
+causal-convert-model-debug: causal-convert-model
+
 causal-convert-model:
 	$(call validate_model_path,causal-convert-model)
 	@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \
 	METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
-	./scripts/causal/convert-model.sh
+	./scripts/causal/convert-model.sh $(DEBUG)

 causal-convert-mm-model-bf16: OUTTYPE=bf16
 causal-convert-mm-model-bf16: MM_OUTTYPE=f16
--- a/examples/model-conversion/scripts/causal/convert-model.sh
+++ b/examples/model-conversion/scripts/causal/convert-model.sh
@@ -4,12 +4,17 @@ set -e

 # Parse command line arguments
 MMPROJ=""
+DEBUG=""
 while [[ $# -gt 0 ]]; do
    case $1 in
        --mmproj)
            MMPROJ="--mmproj"
            shift
            ;;
+        --debug)
+            DEBUG="1"
+            shift
+            ;;
        *)
            shift
            ;;
@@ -28,7 +33,12 @@ echo "Data  type: ${TYPE}"
 echo "Converted model path:: ${CONVERTED_MODEL}"
 echo "Metadata override: ${METADATA_OVERRIDE}"

-CMD_ARGS=("python" "../../convert_hf_to_gguf.py" "--verbose")
+if [[ -n "$DEBUG" ]]; then
+    CMD_ARGS=("python" "-m" "pdb")
+else
+    CMD_ARGS=("python")
+fi
+CMD_ARGS+=("../../convert_hf_to_gguf.py" "--verbose")
 CMD_ARGS+=("${MODEL_PATH}")
 CMD_ARGS+=("--outfile" "${CONVERTED_MODEL}")
 CMD_ARGS+=("--outtype" "${TYPE}")
--- a/ggml/include/ggml-cann.h
+++ b/ggml/include/ggml-cann.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -6,7 +6,7 @@
 // This documentation is still a work in progress.
 // If you wish some specific topics to be covered, feel free to drop a comment:
 //
-//   https://github.com/ggerganov/whisper.cpp/issues/40
+//   https://github.com/ggml-org/whisper.cpp/issues/40
 //
 // ## Overview
 //
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -258,6 +258,7 @@ void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor *
    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");

    if (backend->iface.set_tensor_async == NULL) {
+        ggml_backend_synchronize(backend);
        ggml_backend_tensor_set(tensor, data, offset, size);
    } else {
        backend->iface.set_tensor_async(backend, tensor, data, offset, size);
@@ -271,6 +272,7 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten
    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");

    if (backend->iface.get_tensor_async == NULL) {
+        ggml_backend_synchronize(backend);
        ggml_backend_tensor_get(tensor, data, offset, size);
    } else {
        backend->iface.get_tensor_async(backend, tensor, data, offset, size);
--- a/ggml/src/ggml-cann/acl_tensor.cpp
+++ b/ggml/src/ggml-cann/acl_tensor.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
--- a/ggml/src/ggml-cann/acl_tensor.h
+++ b/ggml/src/ggml-cann/acl_tensor.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
--- a/ggml/src/ggml-cann/aclnn_ops.h
+++ b/ggml/src/ggml-cann/aclnn_ops.h
@@ -1,5 +1,5 @@
 /**
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
--- a/ggml/src/ggml-cann/common.h
+++ b/ggml/src/ggml-cann/common.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
--- a/ggml/src/ggml-metal/CMakeLists.txt
+++ b/ggml/src/ggml-metal/CMakeLists.txt
@@ -71,7 +71,7 @@ else()
        #       disabling fast math is needed in order to pass tests/test-backend-ops
        # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
        # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
-        #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
+        #       ref: https://github.com/ggml-org/whisper.cpp/issues/1720
        # note: adding -g causes segmentation fault during compile
        #set(XC_FLAGS -fno-fast-math -fno-inline -g)
        set(XC_FLAGS -fno-fast-math -fno-inline)
--- a/ggml/src/ggml-metal/ggml-metal-context.h
+++ b/ggml/src/ggml-metal/ggml-metal-context.h
@@ -15,14 +15,22 @@ typedef struct ggml_metal * ggml_metal_t;
 ggml_metal_t ggml_metal_init(ggml_metal_device_t dev);
 void ggml_metal_free(ggml_metal_t ctx);

+const char * ggml_metal_get_name(ggml_metal_t ctx);
+
 void ggml_metal_synchronize(ggml_metal_t ctx);

 void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
 void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
+bool ggml_metal_cpy_tensor_async(ggml_metal_t ctx_src, ggml_metal_t ctx_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);

 enum ggml_status ggml_metal_graph_compute (ggml_metal_t ctx, struct ggml_cgraph * gf);
 void             ggml_metal_graph_optimize(ggml_metal_t ctx, struct ggml_cgraph * gf);

+void ggml_metal_event_record(ggml_metal_t ctx, ggml_metal_event_t ev);
+void ggml_metal_event_wait  (ggml_metal_t ctx, ggml_metal_event_t ev);
+
+ggml_metal_event_t ggml_metal_get_ev_cpy(ggml_metal_t ctx);
+
 void ggml_metal_set_n_cb            (ggml_metal_t ctx, int n_cb);
 void ggml_metal_set_abort_callback  (ggml_metal_t ctx, ggml_abort_callback abort_callback, void * user_data);
 bool ggml_metal_supports_family     (ggml_metal_t ctx, int family);
--- a/ggml/src/ggml-metal/ggml-metal-context.m
+++ b/ggml/src/ggml-metal/ggml-metal-context.m
@@ -24,9 +24,13 @@ struct ggml_metal_command_buffer {
 };

 struct ggml_metal {
+    char name[128];
+
    ggml_metal_device_t  dev;
    ggml_metal_library_t lib;

+    ggml_metal_event_t ev_cpy; // for async copies
+
    dispatch_queue_t d_queue;

    // additional, inference-time compiled pipelines
@@ -117,7 +121,11 @@ ggml_metal_t ggml_metal_init(ggml_metal_device_t dev) {
        }
    }

-    //const struct ggml_metal_device_props * props_dev = ggml_metal_device_get_props(dev);
+    res->ev_cpy = ggml_metal_device_event_init(dev);
+
+    const struct ggml_metal_device_props * props_dev = ggml_metal_device_get_props(dev);
+
+    snprintf(res->name, sizeof(res->name), "%s", props_dev->name);

    res->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);

@@ -206,9 +214,15 @@ void ggml_metal_free(ggml_metal_t ctx) {

    dispatch_release(ctx->d_queue);

+    ggml_metal_device_event_free(ctx->dev, ctx->ev_cpy);
+
    free(ctx);
 }

+const char * ggml_metal_get_name(ggml_metal_t ctx) {
+    return ctx->name;
+}
+
 void ggml_metal_synchronize(ggml_metal_t ctx) {
    // wait for any backend operations to finish
    if (ctx->cmd_buf_last) {
@@ -273,8 +287,8 @@ void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor,
        // wrap the source data into a Metal buffer
        id<MTLDevice> device = ggml_metal_device_get_obj(ctx->dev);
        id<MTLBuffer> buf_src = [device newBufferWithBytes:data
-                                                         length:size
-                                                        options:MTLResourceStorageModeShared];
+                                                    length:size
+                                                   options:MTLResourceStorageModeShared];

        GGML_ASSERT(buf_src);

@@ -316,9 +330,9 @@ void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * te
    @autoreleasepool {
        id<MTLDevice> device = ggml_metal_device_get_obj(ctx->dev);
        id<MTLBuffer> buf_dst = [device newBufferWithBytesNoCopy:data
-                                                               length:size
-                                                              options:MTLResourceStorageModeShared
-                                                          deallocator:nil];
+                                                          length:size
+                                                         options:MTLResourceStorageModeShared
+                                                     deallocator:nil];

        GGML_ASSERT(buf_dst);

@@ -356,6 +370,49 @@ void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * te
    }
 }

+bool ggml_metal_cpy_tensor_async(ggml_metal_t ctx_src, ggml_metal_t ctx_dst, const struct ggml_tensor * src, struct ggml_tensor * dst) {
+    @autoreleasepool {
+        struct ggml_metal_buffer_id bid_src = ggml_metal_get_buffer_id(src);
+        struct ggml_metal_buffer_id bid_dst = ggml_metal_get_buffer_id(dst);
+
+        if (bid_src.metal == nil || bid_dst.metal == nil) {
+            return false;
+        }
+
+        // queue the copy operation into the Metal context
+        // this will be queued at the end, after any currently ongoing GPU operations
+        id<MTLCommandQueue> queue = ggml_metal_device_get_queue(ctx_src->dev);
+        id<MTLCommandBuffer> cmd_buf = [queue commandBuffer];
+        id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
+
+        [encoder copyFromBuffer:bid_src.metal
+                   sourceOffset:bid_src.offs
+                       toBuffer:bid_dst.metal
+              destinationOffset:bid_dst.offs
+                           size:ggml_nbytes(src)];
+
+        [encoder endEncoding];
+
+        ggml_metal_event_t ev_cpy = ggml_metal_get_ev_cpy(ctx_src);
+        ggml_metal_event_record(ctx_src, ev_cpy);
+
+        [cmd_buf commit];
+
+        // do not wait here for completion
+        //[cmd_buf waitUntilCompleted];
+
+        // instead, remember a reference to the command buffer and wait for it later if needed
+        [ctx_src->cmd_bufs_ext addObject:cmd_buf];
+        ctx_src->cmd_buf_last = cmd_buf;
+
+        [cmd_buf retain];
+
+        ggml_metal_event_wait(ctx_dst, ev_cpy);
+
+        return true;
+    }
+}
+
 enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * gf) {
    // number of nodes encoded by the main thread (empirically determined)
    const int n_main = 64;
@@ -530,6 +587,42 @@ void ggml_metal_graph_optimize(ggml_metal_t ctx, struct ggml_cgraph * gf) {
    //printf("%s: graph optimize took %.3f ms\n", __func__, (ggml_time_us() - t_start) / 1000.0);
 }

+void ggml_metal_event_record(ggml_metal_t ctx, ggml_metal_event_t ev) {
+    @autoreleasepool {
+        id<MTLCommandQueue> queue = ggml_metal_device_get_queue(ctx->dev);
+        id<MTLCommandBuffer> cmd_buf = [queue commandBuffer];
+
+        ggml_metal_event_encode_signal(ev, cmd_buf);
+
+        [cmd_buf commit];
+
+        [ctx->cmd_bufs_ext addObject:cmd_buf];
+        ctx->cmd_buf_last = cmd_buf;
+
+        [cmd_buf retain];
+    }
+}
+
+void ggml_metal_event_wait(ggml_metal_t ctx, ggml_metal_event_t ev) {
+    @autoreleasepool {
+        id<MTLCommandQueue> queue = ggml_metal_device_get_queue(ctx->dev);
+        id<MTLCommandBuffer> cmd_buf = [queue commandBuffer];
+
+        ggml_metal_event_encode_wait(ev, cmd_buf);
+
+        [cmd_buf commit];
+
+        [ctx->cmd_bufs_ext addObject:cmd_buf];
+        ctx->cmd_buf_last = cmd_buf;
+
+        [cmd_buf retain];
+    }
+}
+
+ggml_metal_event_t ggml_metal_get_ev_cpy(ggml_metal_t ctx) {
+    return ctx->ev_cpy;
+}
+
 void ggml_metal_set_n_cb(ggml_metal_t ctx, int n_cb) {
    if (ctx->n_cb != n_cb) {
        ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_COMMAND_BUFFERS);
--- a/ggml/src/ggml-metal/ggml-metal-device.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
@@ -17,10 +17,12 @@ struct ggml_metal_device_deleter {

 typedef std::unique_ptr<ggml_metal_device, ggml_metal_device_deleter> ggml_metal_device_ptr;

-ggml_metal_device_t ggml_metal_device_get(void) {
-    static ggml_metal_device_ptr ctx { ggml_metal_device_init() };
+ggml_metal_device_t ggml_metal_device_get(int device) {
+    static std::vector<ggml_metal_device_ptr> devs;

-    return ctx.get();
+    devs.emplace_back(ggml_metal_device_init(device));
+
+    return devs.back().get();
 }

 struct ggml_metal_pipelines {
--- a/ggml/src/ggml-metal/ggml-metal-device.h
+++ b/ggml/src/ggml-metal/ggml-metal-device.h
@@ -205,7 +205,9 @@ void ggml_metal_rsets_free(ggml_metal_rsets_t rsets);
 //

 struct ggml_metal_device_props {
+    int device;
    char name[128];
+    char desc[128];

    size_t max_buffer_size;
    size_t max_working_set_size;
@@ -224,11 +226,15 @@ struct ggml_metal_device_props {
    int op_offload_min_batch_size;
 };

-ggml_metal_device_t ggml_metal_device_init(void);
+typedef struct ggml_metal_event * ggml_metal_event_t;
+
+void ggml_metal_event_encode_signal(ggml_metal_event_t ev, ggml_metal_cmd_buf_t cmd_buf);
+void ggml_metal_event_encode_wait  (ggml_metal_event_t ev, ggml_metal_cmd_buf_t cmd_buf);
+
+ggml_metal_device_t ggml_metal_device_init(int device);
 void ggml_metal_device_free(ggml_metal_device_t dev);

-// return a singleton that is automatically destroyed when the program exits
-ggml_metal_device_t ggml_metal_device_get(void);
+ggml_metal_device_t ggml_metal_device_get(int device);

 void * ggml_metal_device_get_obj  (ggml_metal_device_t dev); // id<MTLDevice>
 void * ggml_metal_device_get_queue(ggml_metal_device_t dev); // id<MTLCommandQueue>
@@ -240,6 +246,10 @@ void ggml_metal_device_rsets_rm (ggml_metal_device_t dev, ggml_metal_rset_t rset

 void ggml_metal_device_rsets_keep_alive(ggml_metal_device_t dev);

+ggml_metal_event_t ggml_metal_device_event_init(ggml_metal_device_t dev);
+void ggml_metal_device_event_free(ggml_metal_device_t dev, ggml_metal_event_t ev);
+void ggml_metal_device_event_synchronize(ggml_metal_device_t dev, ggml_metal_event_t ev);
+
 void ggml_metal_device_get_memory(ggml_metal_device_t dev, size_t * free, size_t * total);
 bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_tensor * op);

--- a/ggml/src/ggml-metal/ggml-metal-device.m
+++ b/ggml/src/ggml-metal/ggml-metal-device.m
@@ -24,9 +24,6 @@
 static const NSInteger MTLGPUFamilyMetal3_GGML = 5001;
 static const NSInteger MTLGPUFamilyMetal4_GGML = 5002;

-// virtual address for GPU memory allocations
-static atomic_uintptr_t g_addr_device = 0x000000400ULL;
-
 #if !GGML_METAL_EMBED_LIBRARY
 // Here to assist with NSBundle Path Hack
@interface GGMLMetalClass : NSObject
@@ -523,6 +520,9 @@ struct ggml_metal_device {
    ggml_metal_library_t library;

    struct ggml_metal_device_props props;
+
+    // virtual address for GPU memory allocations
+    atomic_uintptr_t addr_virt;
 };

 //
@@ -618,7 +618,7 @@ void ggml_metal_rsets_free(ggml_metal_rsets_t rsets) {
    free(rsets);
 }

-ggml_metal_device_t ggml_metal_device_init(void) {
+ggml_metal_device_t ggml_metal_device_init(int device) {
    ggml_metal_device_t dev = calloc(1, sizeof(struct ggml_metal_device));

    assert(dev != NULL);
@@ -632,6 +632,9 @@ ggml_metal_device_t ggml_metal_device_init(void) {
                GGML_LOG_ERROR("%s: error: failed to create command queue\n", __func__);
            }

+            dev->addr_virt = 0x000000400ULL;
+
+            dev->props.device = device;
            dev->props.has_simdgroup_reduction  = [dev->mtl_device supportsFamily:MTLGPUFamilyApple7];
            dev->props.has_simdgroup_reduction |= [dev->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML];

@@ -792,7 +795,8 @@ ggml_metal_device_t ggml_metal_device_init(void) {
                dev->props.max_working_set_size   = dev->mtl_device.maxBufferLength;
            }

-            strncpy(dev->props.name, [[dev->mtl_device name] UTF8String], sizeof(dev->props.name) - 1);
+            snprintf(dev->props.name, sizeof(dev->props.name), "%s%d", "MTL", device);
+            snprintf(dev->props.desc, sizeof(dev->props.desc), "%s", [[dev->mtl_device name] UTF8String]);

            dev->library = ggml_metal_library_init(dev);
            if (!dev->library) {
@@ -922,6 +926,59 @@ void ggml_metal_device_rsets_keep_alive(ggml_metal_device_t dev) {
    atomic_store_explicit(&dev->rsets->d_loop, 2*dev->rsets->keep_alive_s, memory_order_relaxed);
 }

+struct ggml_metal_event {
+    void * obj; // id<MTLEvent>
+
+    atomic_int value;
+};
+
+void ggml_metal_event_encode_signal(ggml_metal_event_t ev, ggml_metal_cmd_buf_t cmd_buf_raw) {
+    id<MTLEvent> event = (id<MTLEvent>)ev->obj;
+
+    id<MTLCommandBuffer> cmd_buf = (id<MTLCommandBuffer>) cmd_buf_raw;
+
+    [cmd_buf encodeSignalEvent:event value:atomic_fetch_add_explicit(&ev->value, 1, memory_order_relaxed) + 1];
+}
+
+void ggml_metal_event_encode_wait(ggml_metal_event_t ev, ggml_metal_cmd_buf_t cmd_buf_raw) {
+    id<MTLEvent> event = (id<MTLEvent>)ev->obj;
+
+    id<MTLCommandBuffer> cmd_buf = (id<MTLCommandBuffer>) cmd_buf_raw;
+
+    [cmd_buf encodeWaitForEvent:event value:atomic_load_explicit(&ev->value, memory_order_relaxed)];
+}
+
+ggml_metal_event_t ggml_metal_device_event_init(ggml_metal_device_t dev) {
+    id<MTLEvent> event = [dev->mtl_device newEvent];
+
+    ggml_metal_event_t ev = calloc(1, sizeof(struct ggml_metal_event));
+
+    ev->obj = (__bridge void *)event;
+    ev->value = 0;
+
+    return ev;
+}
+
+void ggml_metal_device_event_free(ggml_metal_device_t dev, ggml_metal_event_t ev) {
+    id<MTLEvent> event = ev->obj;
+    [event release];
+
+    free(ev);
+
+    GGML_UNUSED(dev);
+}
+
+void ggml_metal_device_event_synchronize(ggml_metal_device_t dev, ggml_metal_event_t ev) {
+    @autoreleasepool {
+        id<MTLEvent> event = ev->obj;
+
+        id<MTLCommandBuffer> cmd_buf = [dev->mtl_queue commandBuffer];
+        [cmd_buf encodeWaitForEvent:event value:atomic_load_explicit(&ev->value, memory_order_relaxed)];
+        [cmd_buf commit];
+        [cmd_buf waitUntilCompleted];
+    }
+}
+
 void ggml_metal_device_get_memory(ggml_metal_device_t dev, size_t * free, size_t * total) {
    if (@available(macOS 10.12, iOS 16.0, *)) {
        *total = dev->mtl_device.recommendedMaxWorkingSetSize;
@@ -1344,8 +1401,8 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size,
        res->all_data = ggml_metal_host_malloc(size_aligned);
        res->is_shared = true;
    } else {
-        // use virtual address from g_addr_device counter
-        res->all_data = (void *) atomic_fetch_add_explicit(&g_addr_device, size_aligned, memory_order_relaxed);
+        // use virtual address
+        res->all_data = (void *) atomic_fetch_add_explicit(&dev->addr_virt, size_aligned, memory_order_relaxed);
        res->is_shared = false;
    }
    res->all_size = size_aligned;
--- a/ggml/src/ggml-metal/ggml-metal.cpp
+++ b/ggml/src/ggml-metal/ggml-metal.cpp
@@ -7,11 +7,12 @@
 #include "ggml-metal-context.h"
 #include "ggml-metal-ops.h"

-// globals
+#define GGML_METAL_NAME "MTL"
+#define GGML_METAL_MAX_DEVICES 16

-// initialized in ggml_backend_metal_reg
-static ggml_backend_reg    g_ggml_metal_reg;
-static ggml_backend_device g_ggml_metal_device;
+// number of Metal devices
+// note: can be overriden with GGML_METAL_DEVICES env to simulate virtual devices
+static int g_devices = 1;

 ////////////////////////////////////////////////////////////////////////////////
 // backend interface
@@ -165,10 +166,28 @@ static ggml_backend_buffer_i ggml_backend_metal_buffer_private_i = {
    /* .reset           = */ NULL,
 };

+static bool ggml_backend_buffer_is_metal(ggml_backend_buffer_t buffer) {
+    return buffer->iface.free_buffer == ggml_backend_metal_buffer_shared_free_buffer ||
+           buffer->iface.free_buffer == ggml_backend_metal_buffer_private_free_buffer;
+}
+
 //
 // buffer types
 //

+struct ggml_backend_metal_buffer_type {
+    int device;
+    std::string name;
+};
+
+struct ggml_backend_metal_buffer_type_deleter {
+    void operator()(ggml_backend_metal_buffer_type * ctx) const {
+        delete ctx;
+    }
+};
+
+typedef std::unique_ptr<ggml_backend_metal_buffer_type, ggml_backend_metal_buffer_type_deleter> ggml_backend_metal_buffer_type_ptr;
+
 // common method for allocating shread or private Metal buffers
 static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size, bool shared) {
    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)buft->device->context;
@@ -218,9 +237,9 @@ static size_t ggml_backend_metal_buffer_type_get_alloc_size(ggml_backend_buffer_
 // default (shared) buffer type

 static const char * ggml_backend_metal_buffer_type_shared_get_name(ggml_backend_buffer_type_t buft) {
-    return "Metal";
+    ggml_backend_metal_buffer_type * ctx = (ggml_backend_metal_buffer_type *)buft->context;

-    GGML_UNUSED(buft);
+    return ctx->name.c_str();
 }

 static ggml_backend_buffer_t ggml_backend_metal_buffer_type_shared_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
@@ -249,29 +268,54 @@ static bool ggml_backend_metal_buffer_type_shared_is_host(ggml_backend_buffer_ty
    GGML_UNUSED(buft);
 }

-static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_shared(void) {
-    static ggml_backend_buffer_type ggml_backend_buffer_type_metal = {
-        /* .iface = */ {
-            /* .get_name         = */ ggml_backend_metal_buffer_type_shared_get_name,
-            /* .alloc_buffer     = */ ggml_backend_metal_buffer_type_shared_alloc_buffer,
-            /* .get_alignment    = */ ggml_backend_metal_buffer_type_shared_get_alignment,
-            /* .get_max_size     = */ ggml_backend_metal_buffer_type_shared_get_max_size,
-            /* .get_alloc_size   = */ ggml_backend_metal_buffer_type_shared_get_alloc_size,
-            /* .is_host          = */ ggml_backend_metal_buffer_type_shared_is_host,
-        },
-        /* .device  = */ &g_ggml_metal_device,
-        /* .context = */ NULL,
-    };
+static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_shared(int device) {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);

-    return &ggml_backend_buffer_type_metal;
+    static std::vector<ggml_backend_buffer_type> bufts;
+    static std::vector<ggml_backend_metal_buffer_type_ptr> ctxs;
+
+    static bool initialized = false;
+    if (!initialized) {
+        bufts.reserve(g_devices);
+        ctxs.reserve(g_devices);
+
+        for (int i = 0; i < g_devices; ++i) {
+            ggml_backend_metal_buffer_type * raw_ctx =
+                new ggml_backend_metal_buffer_type {
+                    /* .device = */ i,
+                    /* .name   = */ GGML_METAL_NAME + std::to_string(i),
+                };
+            ctxs.emplace_back(raw_ctx);
+
+            ggml_backend_buffer_type buft = {
+                /* .iface = */ {
+                    /* .get_name         = */ ggml_backend_metal_buffer_type_shared_get_name,
+                    /* .alloc_buffer     = */ ggml_backend_metal_buffer_type_shared_alloc_buffer,
+                    /* .get_alignment    = */ ggml_backend_metal_buffer_type_shared_get_alignment,
+                    /* .get_max_size     = */ ggml_backend_metal_buffer_type_shared_get_max_size,
+                    /* .get_alloc_size   = */ ggml_backend_metal_buffer_type_shared_get_alloc_size,
+                    /* .is_host          = */ ggml_backend_metal_buffer_type_shared_is_host,
+                },
+                /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_metal_reg(), i),
+                /* .context = */ raw_ctx,
+            };
+
+            bufts.emplace_back(buft);
+        }
+
+        initialized = true;
+    }
+
+    return &bufts[device];
 }

 // default (private) buffer type

 static const char * ggml_backend_metal_buffer_type_private_get_name(ggml_backend_buffer_type_t buft) {
-    return "Metal_Private";
+    ggml_backend_metal_buffer_type * ctx = (ggml_backend_metal_buffer_type *)buft->context;

-    GGML_UNUSED(buft);
+    return ctx->name.c_str();
 }

 static ggml_backend_buffer_t ggml_backend_metal_buffer_type_private_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
@@ -300,29 +344,53 @@ static bool ggml_backend_metal_buffer_type_private_is_host(ggml_backend_buffer_t
    GGML_UNUSED(buft);
 }

-static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_private(void) {
-    static ggml_backend_buffer_type ggml_backend_buffer_type_metal = {
-        /* .iface = */ {
-            /* .get_name         = */ ggml_backend_metal_buffer_type_private_get_name,
-            /* .alloc_buffer     = */ ggml_backend_metal_buffer_type_private_alloc_buffer,
-            /* .get_alignment    = */ ggml_backend_metal_buffer_type_private_get_alignment,
-            /* .get_max_size     = */ ggml_backend_metal_buffer_type_private_get_max_size,
-            /* .get_alloc_size   = */ ggml_backend_metal_buffer_type_private_get_alloc_size,
-            /* .is_host          = */ ggml_backend_metal_buffer_type_private_is_host,
-        },
-        /* .device  = */ &g_ggml_metal_device,
-        /* .context = */ NULL,
-    };
+static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_private(int device) {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);

-    return &ggml_backend_buffer_type_metal;
+    static std::vector<ggml_backend_buffer_type> bufts;
+    static std::vector<ggml_backend_metal_buffer_type_ptr> ctxs;
+
+    static bool initialized = false;
+    if (!initialized) {
+        bufts.reserve(g_devices);
+        ctxs.reserve(g_devices);
+
+        for (int i = 0; i < g_devices; ++i) {
+            ggml_backend_metal_buffer_type * raw_ctx = new ggml_backend_metal_buffer_type{
+                /* .device = */ i,
+                /* .name   = */ GGML_METAL_NAME + std::to_string(i) + "_Private"
+            };
+            ctxs.emplace_back(raw_ctx);
+
+            ggml_backend_buffer_type buft = {
+                /* .iface = */ {
+                    /* .get_name         = */ ggml_backend_metal_buffer_type_private_get_name,
+                    /* .alloc_buffer     = */ ggml_backend_metal_buffer_type_private_alloc_buffer,
+                    /* .get_alignment    = */ ggml_backend_metal_buffer_type_private_get_alignment,
+                    /* .get_max_size     = */ ggml_backend_metal_buffer_type_private_get_max_size,
+                    /* .get_alloc_size   = */ ggml_backend_metal_buffer_type_private_get_alloc_size,
+                    /* .is_host          = */ ggml_backend_metal_buffer_type_private_is_host,
+                },
+                /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_metal_reg(), i),
+                /* .context = */ raw_ctx,
+            };
+
+            bufts.emplace_back(buft);
+        }
+
+        initialized = true;
+    }
+
+    return &bufts[device];
 }

 // mapped buffer type

 static const char * ggml_backend_metal_buffer_type_mapped_get_name(ggml_backend_buffer_type_t buft) {
-    return "Metal_Mapped";
+    ggml_backend_metal_buffer_type * ctx = (ggml_backend_metal_buffer_type *)buft->context;

-    GGML_UNUSED(buft);
+    return ctx->name.c_str();
 }

 static ggml_backend_buffer_t ggml_backend_metal_buffer_type_mapped_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
@@ -352,31 +420,55 @@ static bool ggml_backend_metal_buffer_type_mapped_is_host(ggml_backend_buffer_ty
    GGML_UNUSED(buft);
 }

-static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_mapped(void) {
-    // note: not obvious, but this buffer type still needs to implement .alloc_buffer:
-    //       https://github.com/ggml-org/llama.cpp/pull/15832#discussion_r2333177099
-    static ggml_backend_buffer_type ggml_backend_buffer_type_mapped_metal = {
-        /* .iface = */ {
-            /* .get_name         = */ ggml_backend_metal_buffer_type_mapped_get_name,
-            /* .alloc_buffer     = */ ggml_backend_metal_buffer_type_mapped_alloc_buffer,
-            /* .get_alignment    = */ ggml_backend_metal_buffer_type_mapped_get_alignment,
-            /* .get_max_size     = */ ggml_backend_metal_buffer_type_mapped_get_max_size,
-            /* .get_alloc_size   = */ ggml_backend_metal_buffer_type_mapped_get_alloc_size,
-            /* .is_host          = */ ggml_backend_metal_buffer_type_mapped_is_host,
-        },
-        /* .device  = */ &g_ggml_metal_device,
-        /* .context = */ NULL,
-    };
+static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_mapped(int device) {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);

-    return &ggml_backend_buffer_type_mapped_metal;
+    static std::vector<ggml_backend_buffer_type> bufts;
+    static std::vector<ggml_backend_metal_buffer_type_ptr> ctxs;
+
+    static bool initialized = false;
+    if (!initialized) {
+        bufts.reserve(g_devices);
+        ctxs.reserve(g_devices);
+
+        for (int i = 0; i < g_devices; ++i) {
+            ggml_backend_metal_buffer_type * raw_ctx = new ggml_backend_metal_buffer_type{
+                /* .device = */ i,
+                /* .name   = */ GGML_METAL_NAME + std::to_string(i) + "_Mapped"
+            };
+            ctxs.emplace_back(raw_ctx);
+
+            // note: not obvious, but this buffer type still needs to implement .alloc_buffer:
+            //       https://github.com/ggml-org/llama.cpp/pull/15832#discussion_r2333177099
+            ggml_backend_buffer_type buft = {
+                /* .iface = */ {
+                    /* .get_name         = */ ggml_backend_metal_buffer_type_mapped_get_name,
+                    /* .alloc_buffer     = */ ggml_backend_metal_buffer_type_mapped_alloc_buffer,
+                    /* .get_alignment    = */ ggml_backend_metal_buffer_type_mapped_get_alignment,
+                    /* .get_max_size     = */ ggml_backend_metal_buffer_type_mapped_get_max_size,
+                    /* .get_alloc_size   = */ ggml_backend_metal_buffer_type_mapped_get_alloc_size,
+                    /* .is_host          = */ ggml_backend_metal_buffer_type_mapped_is_host,
+                },
+                /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_metal_reg(), i),
+                /* .context = */ raw_ctx,
+            };
+
+            bufts.emplace_back(buft);
+        }
+
+        initialized = true;
+    }
+
+    return &bufts[device];
 }

 // backend

 static const char * ggml_backend_metal_name(ggml_backend_t backend) {
-    return "Metal";
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;

-    GGML_UNUSED(backend);
+    return ggml_metal_get_name(ctx);
 }

 static void ggml_backend_metal_free(ggml_backend_t backend) {
@@ -409,12 +501,24 @@ static void ggml_backend_metal_get_tensor_async(ggml_backend_t backend, const gg
 }

 static bool ggml_backend_metal_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) {
-    return false;
+    if (!ggml_backend_is_metal(backend_src) || !ggml_backend_is_metal(backend_dst)) {
+        return false;
+    }

-    GGML_UNUSED(backend_src);
-    GGML_UNUSED(backend_dst);
-    GGML_UNUSED(src);
-    GGML_UNUSED(dst);
+    if (!ggml_backend_buffer_is_metal(src->buffer) || !ggml_backend_buffer_is_metal(dst->buffer)) {
+        return false;
+    }
+
+    ggml_metal_t ctx_src = (ggml_metal_t)backend_src->context;
+    ggml_metal_t ctx_dst = (ggml_metal_t)backend_dst->context;
+
+    //ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer;
+    //ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer;
+
+    //ggml_metal_buffer_t buf_ctx_src = (ggml_metal_buffer_t)buf_src->context;
+    //ggml_metal_buffer_t buf_ctx_dst = (ggml_metal_buffer_t)buf_dst->context;
+
+    return ggml_metal_cpy_tensor_async(ctx_src, ctx_dst, src, dst);
 }

 static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
@@ -423,6 +527,20 @@ static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend,
    return ggml_metal_graph_compute(ctx, cgraph);
 }

+static void ggml_backend_metal_event_record(ggml_backend_t backend, ggml_backend_event_t event) {
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;
+    ggml_metal_event_t ev = (ggml_metal_event_t)event->context;
+
+    ggml_metal_event_record(ctx, ev);
+}
+
+static void ggml_backend_metal_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;
+    ggml_metal_event_t ev = (ggml_metal_event_t)event->context;
+
+    ggml_metal_event_wait(ctx, ev);
+}
+
 static void ggml_backend_metal_graph_optimize(ggml_backend_t backend, ggml_cgraph * cgraph) {
    ggml_metal_t ctx = (ggml_metal_t)backend->context;

@@ -435,7 +553,6 @@ static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
    ggml_metal_t ctx = (ggml_metal_t)backend->context;

    ggml_metal_set_n_cb(ctx, n_cb);
-
 }

 static ggml_backend_i ggml_backend_metal_i = {
@@ -450,12 +567,8 @@ static ggml_backend_i ggml_backend_metal_i = {
    /* .graph_plan_update       = */ NULL,
    /* .graph_plan_compute      = */ NULL,
    /* .graph_compute           = */ ggml_backend_metal_graph_compute,
-
-    // the events API is needed only for multi-GPU setups, so likely no need to implement it for Metal
-    // in any case, these docs seem relevant if we ever decide to implement it:
-    // https://developer.apple.com/documentation/metal/mtlcommandbuffer#Synchronizing-Passes-with-Events
-    /* .event_record            = */ NULL,
-    /* .event_wait              = */ NULL,
+    /* .event_record            = */ ggml_backend_metal_event_record,
+    /* .event_wait              = */ ggml_backend_metal_event_wait,
    /* .graph_optimize          = */ ggml_backend_metal_graph_optimize,
 };

@@ -519,15 +632,17 @@ void ggml_backend_metal_capture_next_compute(ggml_backend_t backend) {
 // backend device

 static const char * ggml_backend_metal_device_get_name(ggml_backend_dev_t dev) {
-    return "Metal";
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;

-    GGML_UNUSED(dev);
+    const ggml_metal_device_props * props_dev = ggml_metal_device_get_props(ctx_dev);
+
+    return props_dev->name;
 }

 static const char * ggml_backend_metal_device_get_description(ggml_backend_dev_t dev) {
    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;

-    return ggml_metal_device_get_props(ctx_dev)->name;
+    return ggml_metal_device_get_props(ctx_dev)->desc;
 }

 static void ggml_backend_metal_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
@@ -550,14 +665,14 @@ static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, ggml_bac
    ggml_backend_metal_device_get_memory(dev, &props->memory_free, &props->memory_total);

    props->caps = {
-        /* .async                 = */ true,
-        /* .host_buffer           = */ false,
-        /* .buffer_from_host_ptr  = */ true,
-        /* .events                = */ false,
+        /* .async                = */ true,
+        /* .host_buffer          = */ false,
+        /* .buffer_from_host_ptr = */ true,
+        /* .events               = */ true,
    };
 }

-static ggml_backend_t ggml_backend_metal_device_init(ggml_backend_dev_t dev, const char * params) {
+static ggml_backend_t ggml_backend_metal_device_init_backend(ggml_backend_dev_t dev, const char * params) {
    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;

    ggml_metal_t ctx = ggml_metal_init(ctx_dev);
@@ -587,7 +702,7 @@ static ggml_backend_buffer_type_t ggml_backend_metal_device_get_buffer_type(ggml

    const ggml_metal_device_props * props_dev = ggml_metal_device_get_props(ctx_dev);

-    return props_dev->use_shared_buffers ? ggml_backend_metal_buffer_type_shared() : ggml_backend_metal_buffer_type_private();
+    return props_dev->use_shared_buffers ? ggml_backend_metal_buffer_type_shared(props_dev->device) : ggml_backend_metal_buffer_type_private(props_dev->device);
 }

 static ggml_backend_buffer_t ggml_backend_metal_device_buffer_mapped(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
@@ -595,7 +710,9 @@ static ggml_backend_buffer_t ggml_backend_metal_device_buffer_mapped(ggml_backen

    ggml_metal_buffer_t res = ggml_metal_buffer_map(ctx_dev, ptr, size, max_tensor_size);

-    return ggml_backend_buffer_init(ggml_backend_metal_buffer_type_mapped(), ggml_backend_metal_buffer_shared_i, res, size);
+    const ggml_metal_device_props * props_dev = ggml_metal_device_get_props(ctx_dev);
+
+    return ggml_backend_buffer_init(ggml_backend_metal_buffer_type_mapped(props_dev->device), ggml_backend_metal_buffer_shared_i, res, size);
 }

 static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
@@ -606,9 +723,10 @@ static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const

 static bool ggml_backend_metal_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
    return
+        buft->device == dev && (
        buft->iface.get_name == ggml_backend_metal_buffer_type_shared_get_name ||
        buft->iface.get_name == ggml_backend_metal_buffer_type_private_get_name ||
-        buft->iface.get_name == ggml_backend_metal_buffer_type_mapped_get_name;
+        buft->iface.get_name == ggml_backend_metal_buffer_type_mapped_get_name);

    GGML_UNUSED(dev);
 }
@@ -632,45 +750,97 @@ static bool ggml_backend_metal_device_offload_op(ggml_backend_dev_t dev, const g
            get_op_batch_size(op) >= ggml_metal_device_get_props(ctx_dev)->op_offload_min_batch_size;
 }

+static ggml_backend_event_t ggml_backend_metal_device_event_new(ggml_backend_dev_t dev) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;
+
+    ggml_metal_event_t event = ggml_metal_device_event_init(ctx_dev);
+    GGML_ASSERT(event);
+
+    ggml_backend_event_t ev = new ggml_backend_event {
+        /* .device  = */ dev,
+        /* .context = */ event,
+    };
+
+    return ev;
+}
+
+static void ggml_backend_metal_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;
+
+    ggml_metal_event_t ev = (ggml_metal_event_t)event->context;
+
+    ggml_metal_device_event_free(ctx_dev, ev);
+
+    delete event;
+}
+
+static void ggml_backend_metal_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;
+
+    ggml_metal_event_t evt = (ggml_metal_event_t)event->context;
+
+    ggml_metal_device_event_synchronize(ctx_dev, evt);
+}
+
 static ggml_backend_device_i ggml_backend_metal_device_i = {
    /* .get_name             = */ ggml_backend_metal_device_get_name,
    /* .get_description      = */ ggml_backend_metal_device_get_description,
    /* .get_memory           = */ ggml_backend_metal_device_get_memory,
    /* .get_type             = */ ggml_backend_metal_device_get_type,
    /* .get_props            = */ ggml_backend_metal_device_get_props,
-    /* .init_backend         = */ ggml_backend_metal_device_init,
+    /* .init_backend         = */ ggml_backend_metal_device_init_backend,
    /* .get_buffer_type      = */ ggml_backend_metal_device_get_buffer_type,
    /* .get_host_buffer_type = */ NULL,
    /* .buffer_from_host_ptr = */ ggml_backend_metal_device_buffer_mapped,
    /* .supports_op          = */ ggml_backend_metal_device_supports_op,
    /* .supports_buft        = */ ggml_backend_metal_device_supports_buft,
    /* .offload_op           = */ ggml_backend_metal_device_offload_op,
-    /* .event_new            = */ NULL,
-    /* .event_free           = */ NULL,
-    /* .event_synchronize    = */ NULL,
+    /* .event_new            = */ ggml_backend_metal_device_event_new,
+    /* .event_free           = */ ggml_backend_metal_device_event_free,
+    /* .event_synchronize    = */ ggml_backend_metal_device_event_synchronize,
 };

 // backend registry

+struct ggml_backend_metal_reg {
+    std::vector<ggml_backend_dev_t> devices;
+};
+
+typedef struct ggml_backend_metal_reg * ggml_backend_metal_reg_t;
+
+static ggml_backend_metal_reg_t ggml_backend_metal_reg_init(void) {
+    ggml_backend_metal_reg_t ctx = new struct ggml_backend_metal_reg;
+
+    return ctx;
+}
+
+static void ggml_backend_metal_reg_free(ggml_backend_metal_reg_t ctx) {
+    delete ctx;
+}
+
+struct ggml_backend_metal_reg_deleter {
+    void operator()(ggml_backend_metal_reg_t ctx) {
+        ggml_backend_metal_reg_free(ctx);
+    }
+};
+
+typedef std::unique_ptr<struct ggml_backend_metal_reg, ggml_backend_metal_reg_deleter> ggml_backend_metal_reg_ptr;
+
 static const char * ggml_backend_metal_reg_get_name(ggml_backend_reg_t reg) {
-    return "Metal";
+    return GGML_METAL_NAME;

    GGML_UNUSED(reg);
 }

 static size_t ggml_backend_metal_reg_device_count(ggml_backend_reg_t reg) {
-    return 1;
-
-    GGML_UNUSED(reg);
+    ggml_backend_metal_reg_t ctx = (ggml_backend_metal_reg_t)reg->context;
+    return ctx->devices.size();
 }

 static ggml_backend_dev_t ggml_backend_metal_reg_device_get(ggml_backend_reg_t reg, size_t index) {
-    GGML_ASSERT(index == 0);
-
-    return &g_ggml_metal_device;
-
-    GGML_UNUSED(reg);
-    GGML_UNUSED(index);
+    ggml_backend_metal_reg_t ctx = (ggml_backend_metal_reg_t)reg->context;
+    GGML_ASSERT(index < ctx->devices.size());
+    return ctx->devices[index];
 }

 static ggml_backend_feature g_ggml_backend_metal_features[] = {
@@ -698,27 +868,67 @@ static void * ggml_backend_metal_get_proc_address(ggml_backend_reg_t reg, const

 static ggml_backend_reg_i ggml_backend_metal_reg_i = {
    /* .get_name         = */ ggml_backend_metal_reg_get_name,
-    /* .device_count     = */ ggml_backend_metal_reg_device_count,
-    /* .device_get       = */ ggml_backend_metal_reg_device_get,
+    /* .get_device_count = */ ggml_backend_metal_reg_device_count,
+    /* .get_device       = */ ggml_backend_metal_reg_device_get,
    /* .get_proc_address = */ ggml_backend_metal_get_proc_address,
 };

-ggml_backend_reg_t ggml_backend_metal_reg(void) {
-    {
-        g_ggml_metal_reg = {
-            /* .api_version = */ GGML_BACKEND_API_VERSION,
-            /* .iface       = */ ggml_backend_metal_reg_i,
-            /* .context     = */ NULL,
-        };
+static ggml_backend_dev_t ggml_backend_metal_device_init(ggml_backend_reg_t reg, int device) {
+    return new ggml_backend_device {
+        /* .iface   = */ ggml_backend_metal_device_i,
+        /* .reg     = */ reg,
+        /* .context = */ ggml_metal_device_get(device),
+    };
+}

-        g_ggml_metal_device = {
-            /* .iface   = */ ggml_backend_metal_device_i,
-            /* .reg     = */ &g_ggml_metal_reg,
-            /* .context = */ ggml_metal_device_get(),
-        };
+static void ggml_backend_metal_device_free(ggml_backend_dev_t dev) {
+    delete dev;
+}
+
+struct ggml_backend_device_deleter {
+    void operator()(ggml_backend_dev_t ctx) {
+        ggml_backend_metal_device_free(ctx);
+    }
+};
+
+typedef std::unique_ptr<ggml_backend_device, ggml_backend_device_deleter> ggml_backend_device_ptr;
+
+ggml_backend_reg_t ggml_backend_metal_reg(void) {
+    static ggml_backend_reg reg;
+    static bool initialized = false;
+
+    {
+        static std::mutex mutex;
+        std::lock_guard<std::mutex> lock(mutex);
+
+        const char * env = getenv("GGML_METAL_DEVICES");
+        if (env) {
+            g_devices = atoi(env);
+        }
+
+        static std::vector<ggml_backend_device_ptr> devs;
+
+        if (!initialized) {
+            static ggml_backend_metal_reg_ptr reg_ctx(ggml_backend_metal_reg_init());
+
+            for (int i = 0; i < g_devices; ++i) {
+                auto * dev = ggml_backend_metal_device_init(&reg, i);
+                devs.emplace_back(dev);
+
+                reg_ctx->devices.push_back(dev);
+            }
+
+            reg = {
+                /* .api_version = */ GGML_BACKEND_API_VERSION,
+                /* .iface       = */ ggml_backend_metal_reg_i,
+                /* .context     = */ reg_ctx.get(),
+            };
+        }
+
+        initialized = true;
    }

-    return &g_ggml_metal_reg;
+    return &reg;
 }

 GGML_BACKEND_DL_IMPL(ggml_backend_metal_reg)
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -3740,7 +3740,7 @@ static enum ggml_status ggml_backend_opencl_buffer_init_tensor(ggml_backend_buff
        // Reuse extra of the parent tensor. The offset of this view tensor
        // becomes `extra->offset + view_offs` and needs to be calculated when
        // it is used. This changes is needed because of the change to
-        // ggml_alloc.c in https://github.com/ggerganov/llama.cpp/pull/7640.
+        // ggml_alloc.c in https://github.com/ggml-org/llama.cpp/pull/7640.
        // `buffer` passed in here will always be `tensor->buffer`. It is OK
        // to allocate extras from the same buffer context for ordinary
        // intermediate tensors. But for views into kv cache tensors, doing so
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -3390,7 +3390,7 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor


    // mmvq and mmq need the __dp4a instruction which is available for gen12+
-    // Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e
+    // Workaround in https://github.com/ggml-org/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e
    use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS);
 #ifdef SYCL_USE_XMX
    use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE);
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -330,7 +330,7 @@ void string_to_spv_func(std::string name, std::string in_path, std::string out_p
        std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", target_env, in_path, "-o", out_path};
    #endif

-    // disable spirv-opt for coopmat shaders for https://github.com/ggerganov/llama.cpp/issues/10734
+    // disable spirv-opt for coopmat shaders for https://github.com/ggml-org/llama.cpp/issues/10734
    // disable spirv-opt for bf16 shaders for https://github.com/ggml-org/llama.cpp/issues/15344
    // disable spirv-opt for rope shaders for https://github.com/ggml-org/llama.cpp/issues/16860
    if (!coopmat && name.find("bf16") == std::string::npos && name.find("rope") == std::string::npos) {
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -6562,7 +6562,7 @@ static void ggml_compute_backward(
        case GGML_OP_DIAG_MASK_INF: {
            if (src0_needs_grads) {
                /* ggml_diag_mask_inf_impl() shouldn't be here */
-                /* ref:  https://github.com/ggerganov/llama.cpp/pull/4203#discussion_r1412377992 */
+                /* ref:  https://github.com/ggml-org/llama.cpp/pull/4203#discussion_r1412377992 */
                const int n_past = ((const int32_t *) tensor->op_params)[0];
                ggml_add_or_set(ctx, cgraph, isrc0, ggml_diag_mask_zero_impl(ctx, grad, n_past, false));
            }
--- a/src/llama-chat.cpp
+++ b/src/llama-chat.cpp
@@ -233,7 +233,7 @@ int32_t llm_chat_apply_template(
    llm_chat_template tmpl,
    const std::vector<const llama_chat_message *> & chat,
    std::string & dest, bool add_ass) {
-    // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
+    // Taken from the research: https://github.com/ggml-org/llama.cpp/issues/5527
    std::stringstream ss;
    if (tmpl == LLM_CHAT_TEMPLATE_CHATML) {
        // chatml template
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -317,6 +317,7 @@ llama_context::llama_context(
                auto dev_type = ggml_backend_dev_type(ggml_backend_get_device(backend.get()));
                if (dev_type == GGML_BACKEND_DEVICE_TYPE_CPU) {
                    // ignore CPU backend
+                    // TODO: should we ignore ACCEL types too?
                    continue;
                }
                auto * dev = ggml_backend_get_device(backend.get());
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -195,7 +195,7 @@ struct llama_hparams {
    uint32_t n_deepstack_layers = 0;

    // needed by encoder-decoder models (e.g. T5, FLAN-T5)
-    // ref: https://github.com/ggerganov/llama.cpp/pull/8141
+    // ref: https://github.com/ggml-org/llama.cpp/pull/8141
    llama_token dec_start_token_id = LLAMA_TOKEN_NULL;
    uint32_t    dec_n_layer        = 0;

--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -90,7 +90,7 @@ static_assert(std::is_trivially_copyable<llm_symbol>::value, "llm_symbol is not
 //
 // SPM tokenizer
 // original implementation:
-// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
+// https://github.com/ggml-org/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
 //

 struct llm_bigram_spm {
@@ -285,7 +285,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                    // original regex from tokenizer.json
                    //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",

-                    // adapted: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2080233989
+                    // adapted: https://github.com/ggml-org/llama.cpp/pull/6920#issuecomment-2080233989
                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
                };
                break;
@@ -2390,7 +2390,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {

        // maintain a list of tokens that cause end-of-generation
        // this is currently determined based on the token text, which is obviously not ideal
-        // ref: https://github.com/ggerganov/llama.cpp/issues/9606
+        // ref: https://github.com/ggml-org/llama.cpp/issues/9606
        special_eog_ids.clear();

        if (special_fim_pad_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_fim_pad_id) == 0) {
@@ -3079,7 +3079,7 @@ std::vector<llama_token> llama_vocab::impl::tokenize(
 }

 int32_t llama_vocab::impl::token_to_piece(llama_token token, char * buf, int32_t length, int32_t lstrip, bool special) const {
-    // ref: https://github.com/ggerganov/llama.cpp/pull/7587#discussion_r1620983843
+    // ref: https://github.com/ggml-org/llama.cpp/pull/7587#discussion_r1620983843
    static const int attr_special = LLAMA_TOKEN_ATTR_UNKNOWN | LLAMA_TOKEN_ATTR_CONTROL;
    const llama_token_attr attr = token_get_attr(token);
    if (!special && (attr & attr_special)) {
--- a/src/models/deepseek2.cpp
+++ b/src/models/deepseek2.cpp
@@ -14,7 +14,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
    const uint32_t kv_lora_rank = hparams.n_lora_kv;

    // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
-    // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
+    // See https://github.com/ggml-org/llama.cpp/discussions/7416 for detailed explanation.
    // And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]

    // first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor
--- a/tests/test-autorelease.cpp
+++ b/tests/test-autorelease.cpp
@@ -1,4 +1,4 @@
-// ref: https://github.com/ggerganov/llama.cpp/issues/4952#issuecomment-1892864763
+// ref: https://github.com/ggml-org/llama.cpp/issues/4952#issuecomment-1892864763

 #include <cstdio>
 #include <string>
--- a/tools/cvector-generator/pca.hpp
+++ b/tools/cvector-generator/pca.hpp
@@ -290,7 +290,7 @@ static void power_iteration(
    ggml_gallocr_free(allocr);

    // TODO @ngxson : The output vector is randomly inverted
-    // Solution: https://github.com/ggerganov/llama.cpp/pull/8069#issuecomment-2185328171
+    // Solution: https://github.com/ggml-org/llama.cpp/pull/8069#issuecomment-2185328171
 }

 static void run_pca(
--- a/tools/export-lora/export-lora.cpp
+++ b/tools/export-lora/export-lora.cpp
@@ -190,7 +190,7 @@ struct lora_merge_ctx {
        gguf_set_val_u32(ctx_out, "general.file_type", LLAMA_FTYPE_MOSTLY_F16);

        // check if all lora adapters have the same tensors
-        // TODO: remove this when we can support merging subset of adapters. Ref: https://github.com/ggerganov/llama.cpp/pull/8607#discussion_r1686027777
+        // TODO: remove this when we can support merging subset of adapters. Ref: https://github.com/ggml-org/llama.cpp/pull/8607#discussion_r1686027777
        static const char * err_no_subset_adapter = "Input adapters do not have the same list of tensors. This is not yet supported. Please merge the adapter one-by-one instead of merging all at once.";
        if (adapters.size() > 1) {
            for (size_t i = 1; i < adapters.size(); ++i) {
--- a/tools/perplexity/README.md
+++ b/tools/perplexity/README.md
@@ -29,7 +29,7 @@ In addition to the KL divergence the following statistics are calculated with `-
 * Mean change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse.
 * Pearson correlation coefficient of the "correct" token probabilites between models.
 * Percentiles of change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse. Can be used to judge noise vs. quality loss from quantization. If the percentiles are symmetric then the quantization is essentially just adding noise. If the negative values are significantly larger than the positive values then this indicates that the model is actually becoming worse from the quantization.
-* The root mean square of the change in token probabilities. If you were to assume that the quantization simply causes Gaussian noise on the token probabilities then this would be the standard deviation of said noise. The uncertainty on the value is calculated that the change in token probabilities follows a Gaussian distribution. Related discussion: https://github.com/ggerganov/llama.cpp/discussions/2875 .
+* The root mean square of the change in token probabilities. If you were to assume that the quantization simply causes Gaussian noise on the token probabilities then this would be the standard deviation of said noise. The uncertainty on the value is calculated that the change in token probabilities follows a Gaussian distribution. Related discussion: https://github.com/ggml-org/llama.cpp/discussions/2875 .
 * Same top p: Percentage of how often the token was assigned the highest probabilites by both models. The uncertainty is calculated from the Gaussian approximation of the binomial distribution.

 ## LLaMA 3 8b Scoreboard
--- a/tools/server/public_legacy/index-new.html
+++ b/tools/server/public_legacy/index-new.html
@@ -1096,7 +1096,7 @@ return html`
          </section>
          <footer>
            <p><${ModelGenerationInfo} /></p>
-            <p>Powered By <a href="https://github.com/ggerganov/llama.cpp#readme" target="_blank">llama.cpp</a> and <a href="https://ggml.ai/" target="_blank">ggml.ai</a></p>
+            <p>Powered By <a href="https://github.com/ggml-org/llama.cpp#readme" target="_blank">llama.cpp</a> and <a href="https://ggml.ai/" target="_blank">ggml.ai</a></p>
          </footer>
        </div>
      `;
--- a/tools/server/public_legacy/index.html
+++ b/tools/server/public_legacy/index.html
@@ -1281,7 +1281,7 @@

          <footer>
            <p><${ModelGenerationInfo} /></p>
-            <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
+            <p>Powered by <a href="https://github.com/ggml-org/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
          </footer>
        </div>
      `;
--- a/tools/server/public_legacy/theme-mangotango.css
+++ b/tools/server/public_legacy/theme-mangotango.css
@@ -1,5 +1,5 @@
 /* Author: Yazan Agha-Schrader */
-/* Inspiration from llama.cpp logo/banner https://github.com/ggerganov/llama.cpp#readme */
+/* Inspiration from llama.cpp logo/banner https://github.com/ggml-org/llama.cpp#readme */

 .theme-mangotango {

--- a/tools/server/themes/buttons-top/index.html
+++ b/tools/server/themes/buttons-top/index.html
@@ -1032,7 +1032,7 @@

          <footer>
            <p><${ModelGenerationInfo} /></p>
-            <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
+            <p>Powered by <a href="https://github.com/ggml-org/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
          </footer>
        </div>
      `;
--- a/tools/server/themes/wild/index.html
+++ b/tools/server/themes/wild/index.html
@@ -1036,7 +1036,7 @@

          <footer>
            <p><${ModelGenerationInfo} /></p>
-            <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
+            <p>Powered by <a href="https://github.com/ggml-org/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
          </footer>
        </div>
      `;
Author	SHA1	Message	Date
Georgi Gerganov	6fdddb4987	metal : support virtual devices (#18919 ) * metal : support virtual devices * cont : manage buffer type context memory * metal : add events * cont : implement cpy_tensor_async	2026-02-02 14:29:44 +02:00
Daniel Bevenius	6156ae5111	model-conversion : add debug option to conversion script (#19265 ) This commit adds a debug option to the model conversion script to enable using the Python debugger (pdb) during model conversion. The motivation for this is that I've found myself adding this a few times now and it would be quicker to have this flag as an option and a makefile target/recipe for it.	2026-02-02 11:29:57 +01:00
Johannes Gäßler	59377a6c87	ggml-backend: fix async set/get fallback sync (#19179 )	2026-02-02 10:00:05 +01:00
Georgi Gerganov	1239267cc4	authors : update (#19263 ) [no ci]	2026-02-02 08:51:25 +02:00
Christian Kastner	7a4ca3cbd9	docs : Minor cleanups (#19252 ) * Update old URLs to github.com/ggml-org/ * Bump copyrights	2026-02-02 08:38:55 +02:00
Sascha Rogmann	b4d05a3d2f	spec : various improvements ton ngram-map + docs (#19253 ) * spec: ngram-map and reasoning chats * spec: add t_begin and t_accept * ngram-map : add internal hash map * docs : update ngram-map, add ngram-mod * docs : fix ngram-map-k * docs : differences between implementations	2026-02-02 08:26:58 +02:00