diff --git a/backend/cpp/llama-cpp/Makefile b/backend/cpp/llama-cpp/Makefile
index 8a6bd922c..8964600bf 100644
--- a/backend/cpp/llama-cpp/Makefile
+++ b/backend/cpp/llama-cpp/Makefile
@@ -1,5 +1,5 @@
 
-LLAMA_VERSION?=571d0d540df04f25298d0e159e520d9fc62ed121
+LLAMA_VERSION?=d2a818231effb12b7b20b80b3b8c7756a9a33a04
 LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
 
 CMAKE_ARGS?=
diff --git a/backend/cpp/llama-cpp/patches/0001-add-minimax-m3-support.patch b/backend/cpp/llama-cpp/patches/0001-add-minimax-m3-support.patch
deleted file mode 100644
index c0e66ea50..000000000
--- a/backend/cpp/llama-cpp/patches/0001-add-minimax-m3-support.patch
+++ /dev/null
@@ -1,814 +0,0 @@
-# Vendored from upstream llama.cpp PR #24523 (Preliminary MiniMax-M3 support).
-# Rebased against LLAMA_VERSION 00fa7cb284cbf133fc426733bd64238a3588a33e (also applies cleanly
-# to the later pin 505b1ed15ca80e2a19f12ff4ac365e40fb374053). LLAMA_VERSION is auto-bumped
-# nightly; if a bump rejects this patch, re-vendor from #24523 — or, once #24523 merges
-# upstream, delete this file and bump LLAMA_VERSION normally.
-# See https://github.com/mudler/LocalAI/issues/10820 and PR #10837.
-diff --git a/common/chat.cpp b/common/chat.cpp
-index 22d2ee4..440be9a 100644
---- a/common/chat.cpp
-+++ b/common/chat.cpp
-@@ -2035,6 +2035,191 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha
-     return data;
- }
- 
-+static common_chat_params common_chat_params_init_minimax_m3(const common_chat_template &    tmpl,
-+                                                             const autoparser::generation_params & inputs) {
-+    common_chat_params data;
-+
-+    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs);
-+    data.generation_prompt  = common_chat_template_generation_prompt_impl(tmpl, inputs);
-+    data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
-+    data.supports_thinking  = true;
-+    data.thinking_start_tag = "<mm:think>";
-+    data.thinking_end_tag   = "</mm:think>";
-+
-+    // M3 prefixes every tool tag with the namespace token "]<]minimax[>[";
-+    // params use the parameter name as the tag (<file_path>...</file_path>).
-+    const std::string NS           = "]<]minimax[>[";
-+    const std::string THINK_START  = "<mm:think>";
-+    const std::string THINK_END    = "</mm:think>";
-+    const std::string FC_START     = NS + "<tool_call>";
-+    const std::string FC_END       = NS + "</tool_call>";
-+    const std::string INVOKE_END   = NS + "</invoke>";
-+
-+    data.preserved_tokens   = {
-+        NS,
-+        "<tool_call>",
-+        "</tool_call>",
-+        THINK_START,
-+        THINK_END,
-+    };
-+
-+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
-+    auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
-+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
-+    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
-+
-+    const std::string GEN_PROMPT = data.generation_prompt;
-+
-+    if (inputs.has_continuation()) {
-+        const auto & msg = inputs.continue_msg;
-+
-+        data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
-+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-+            data.generation_prompt += THINK_END + msg.render_content();
-+        }
-+
-+        data.prompt += data.generation_prompt;
-+    }
-+
-+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-+        auto generation_prompt = p.literal(GEN_PROMPT);
-+        auto end = p.end();
-+
-+        auto reasoning = p.eps();
-+        // M3 can emit a bare </mm:think> (no opener) after tool results; keep the opener optional.
-+        if (extract_reasoning && inputs.enable_thinking) {
-+            reasoning = p.optional(p.optional(p.literal(THINK_START)) + p.reasoning(p.until(THINK_END)) + THINK_END);
-+        } else if (extract_reasoning) {
-+            reasoning = p.optional(p.optional(p.literal(THINK_START)) + p.until(THINK_END) + p.literal(THINK_END));
-+        }
-+
-+        if (has_response_format) {
-+            auto response_format = p.rule("response-format",
-+                p.literal("```json") + p.space() +
-+                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) +
-+                p.space() + p.literal("```"));
-+            return generation_prompt + reasoning + response_format + end;
-+        }
-+
-+        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
-+            return generation_prompt + reasoning + p.content(p.rest()) + end;
-+        }
-+
-+        auto tool_choice = p.choice();
-+        foreach_function(inputs.tools, [&](const json & tool) {
-+            const auto & function = tool.at("function");
-+            std::string  name     = function.at("name");
-+            auto params   = function.contains("parameters") ? function.at("parameters") : json::object();
-+            const auto & props    = params.contains("properties") ? params.at("properties") : json::object();
-+
-+            std::set<std::string> required;
-+            if (params.contains("required")) {
-+                params.at("required").get_to(required);
-+            }
-+
-+            auto schema_info = common_schema_info();
-+            schema_info.resolve_refs(params);
-+
-+            std::vector<common_peg_parser> required_parsers;
-+            std::vector<common_peg_parser> optional_parsers;
-+            for (const auto & [param_name, param_schema] : props.items()) {
-+                bool is_required = required.find(param_name) != required.end();
-+                bool is_string   = schema_info.resolves_to_string(param_schema);
-+
-+                const std::string p_close = NS + "</" + param_name + ">";
-+
-+                auto arg = p.tool_arg(
-+                    p.tool_arg_open(
-+                        p.literal(NS + "<") +
-+                        p.tool_arg_name(p.literal(param_name)) +
-+                        p.literal(">")) +
-+                    (is_string
-+                         ? p.ac(p.tool_arg_string_value(p.until(p_close)) +
-+                                p.tool_arg_close(p.literal(p_close)), p_close)
-+                         : p.tool_arg_json_value(p.schema(p.json(),
-+                                                          "tool-" + name + "-arg-" + param_name + "-schema",
-+                                                          param_schema, false)) +
-+                           p.tool_arg_close(p.literal(p_close))));
-+
-+                auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg);
-+                if (is_required) {
-+                    required_parsers.push_back(named_arg);
-+                } else {
-+                    optional_parsers.push_back(named_arg);
-+                }
-+            }
-+
-+            common_peg_parser args_seq = p.eps();
-+            for (size_t i = 0; i < required_parsers.size(); i++) {
-+                if (i > 0) {
-+                    args_seq = args_seq + p.space();
-+                }
-+                args_seq = args_seq + required_parsers[i];
-+            }
-+
-+            if (!optional_parsers.empty()) {
-+                common_peg_parser any_opt = p.choice();
-+                for (const auto & opt : optional_parsers) {
-+                    any_opt |= opt;
-+                }
-+                args_seq = args_seq + p.repeat(p.space() + any_opt, 0, -1);
-+            }
-+
-+            common_peg_parser invoke_body = args_seq;
-+            auto func_parser = p.tool(
-+                p.tool_open(p.literal(NS + "<invoke name=\"") +
-+                            p.tool_name(p.literal(name)) + p.literal("\">")) +
-+                p.space() + invoke_body + p.space() +
-+                p.tool_close(p.literal(INVOKE_END)));
-+
-+            tool_choice |= p.rule("tool-" + name, func_parser);
-+        });
-+
-+        auto require_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-+
-+        common_peg_parser tool_calls = p.eps();
-+        if (inputs.parallel_tool_calls) {
-+            tool_calls = p.trigger_rule("tool-call",
-+                p.literal(FC_START) + p.space() + tool_choice +
-+                p.zero_or_more(p.space() + tool_choice) + p.space() + p.literal(FC_END));
-+        } else {
-+            tool_calls = p.trigger_rule("tool-call",
-+                p.literal(FC_START) + p.space() + tool_choice + p.space() + p.literal(FC_END));
-+        }
-+
-+        if (!require_tools) {
-+            tool_calls = p.optional(tool_calls);
-+        }
-+
-+        auto content_before_tools = p.content(p.until(FC_START));
-+        return generation_prompt + reasoning + content_before_tools + tool_calls + end;
-+    });
-+
-+    data.parser = parser.save();
-+
-+    if (include_grammar) {
-+        data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
-+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
-+            foreach_function(inputs.tools, [&](const json & tool) {
-+                const auto & function = tool.at("function");
-+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
-+                builder.resolve_refs(schema);
-+            });
-+            if (has_response_format) {
-+                auto schema = inputs.json_schema;
-+                builder.resolve_refs(schema);
-+            }
-+            parser.build_grammar(builder, data.grammar_lazy);
-+        });
-+
-+        data.grammar_triggers = {
-+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, FC_START },
-+        };
-+    }
-+
-+    return data;
-+}
-+
- // Cohere2 MoE (a.k.a. "North Code") parser.
- //
- // The assistant turn is fully marker-wrapped:
-@@ -2612,6 +2797,15 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
-         return common_chat_params_init_gigachat_v3(tmpl, params);
-     }
- 
-+    // MiniMax-M3: the namespace token "]<]minimax[>[" collides with the autoparser's
-+    // markup delimiters, so detect the template and use a dedicated parser.
-+    if (src.find("]<]minimax[>[") != std::string::npos &&
-+        src.find("<tool_call>") != std::string::npos &&
-+        src.find("<invoke name=") != std::string::npos) {
-+        LOG_DBG("Using specialized template: MiniMax-M3\n");
-+        return common_chat_params_init_minimax_m3(tmpl, params);
-+    }
-+
-     // DeepSeek V3.2 format detection: template defines dsml_token and uses it for tool calls.
-     // The template source contains the token as a variable assignment, not as a literal in markup.
-     if (src.find("dsml_token") != std::string::npos &&
-diff --git a/conversion/__init__.py b/conversion/__init__.py
-index 02ea638..71de528 100644
---- a/conversion/__init__.py
-+++ b/conversion/__init__.py
-@@ -155,6 +155,8 @@ TEXT_MODEL_MAP: dict[str, str] = {
-     "MiniCPMForCausalLM": "minicpm",
-     "MiniCPMV4_6ForConditionalGeneration": "minicpm",
-     "MiniMaxM2ForCausalLM": "minimax",
-+    "MiniMaxM3SparseForCausalLM": "minimax",
-+    "MiniMaxM3SparseForConditionalGeneration": "minimax",
-     "Ministral3ForCausalLM": "mistral3",
-     "Mistral3ForConditionalGeneration": "mistral3",
-     "MistralForCausalLM": "llama",
-diff --git a/conversion/base.py b/conversion/base.py
-index 0421aa4..224481a 100644
---- a/conversion/base.py
-+++ b/conversion/base.py
-@@ -1154,7 +1154,8 @@ class TextModel(ModelBase):
-                 or "projector." in name or "pre_mm_projector_norm" in name \
-                 or "image_newline" in name or "view_seperator" in name \
-                 or "patch_embed" in name or "patch_embedding" in name \
--                or "patch_merger." in name or "model.connector." in name:
-+                or "patch_merger." in name or "patch_merge_mlp" in name \
-+                or "model.connector." in name:
-             return None
- 
-         return super().filter_tensors(item)
-@@ -1201,7 +1202,7 @@ class TextModel(ModelBase):
-             self.gguf_writer.add_embedding_length(n_embd)
-             logger.info(f"gguf: embedding length = {n_embd}")
- 
--        if (n_ff := self.find_hparam(["prefix_dense_intermediate_size", "intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None:
-+        if (n_ff := self.find_hparam(["prefix_dense_intermediate_size", "dense_intermediate_size", "intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None:
-             self.gguf_writer.add_feed_forward_length(n_ff)
-             logger.info(f"gguf: feed forward length = {n_ff}")
- 
-diff --git a/conversion/minimax.py b/conversion/minimax.py
-index 4857775..4f637f5 100644
---- a/conversion/minimax.py
-+++ b/conversion/minimax.py
-@@ -52,3 +52,67 @@ class MiniMaxM2Model(TextModel):
-             return
- 
-         yield from super().modify_tensors(data_torch, name, bid)
-+
-+
-+@ModelBase.register("MiniMaxM3SparseForCausalLM", "MiniMaxM3SparseForConditionalGeneration")
-+class MiniMaxM3Model(TextModel):
-+    # Text-only MiniMax-M3: MiniMax-M2 GQA + DeepSeek-V3 shared/leading-dense experts (swigluoai).
-+    model_arch = gguf.MODEL_ARCH.MINIMAXM3
-+    _experts_cache: dict[int, dict[str, Tensor]] = {}
-+
-+    def set_gguf_parameters(self):
-+        # feed_forward_length comes from dense_intermediate_size (base); experts use intermediate_size.
-+        super().set_gguf_parameters()
-+
-+        self.gguf_writer.add_expert_feed_forward_length(self.find_hparam(["intermediate_size"]))
-+        self.gguf_writer.add_rope_dimension_count(self.find_hparam(["rotary_dim"]))
-+        self.gguf_writer.add_expert_shared_count(self.find_hparam(["n_shared_experts"]))
-+        self.gguf_writer.add_expert_weights_scale(self.find_hparam(["routed_scaling_factor"]))
-+        self.gguf_writer.add_expert_weights_norm(True)
-+
-+        # leading dense layers: moe_layer_freq (ints) or mlp_layer_types (Transformers 5.12, strings)
-+        moe_layer_freq = self.find_hparam(["moe_layer_freq", "mlp_layer_types"])
-+        n_dense = 0
-+        for v in moe_layer_freq:
-+            if v == 0 or v == "dense":
-+                n_dense += 1
-+            else:
-+                break
-+        self.gguf_writer.add_leading_dense_block_count(n_dense)
-+
-+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-+        # index_* (sparse-attn indexer) tensors are preserved but unused; the loader skips them
-+        if name.startswith("language_model."):
-+            name = name[len("language_model."):]
-+
-+        # Gemma-style (1+w) RMSNorm: bake +1 in so llama.cpp can use plain RMSNorm
-+        if name.endswith("norm.weight"):
-+            data_torch = data_torch + 1.0
-+
-+        # merge routed experts (w1/w2/w3); shared_experts.* passes through to *_shexp
-+        if "block_sparse_moe.experts." in name:
-+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-+            assert bid is not None
-+
-+            expert_cache = self._experts_cache.setdefault(bid, {})
-+            expert_cache[name] = data_torch
-+            expert_weights = ["w1", "w2", "w3"]
-+
-+            if len(expert_cache) < n_experts * len(expert_weights):
-+                return
-+
-+            for w_name in expert_weights:
-+                datas: list[Tensor] = []
-+                for xid in range(n_experts):
-+                    ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
-+                    datas.append(expert_cache[ename])
-+                    del expert_cache[ename]
-+
-+                data_torch = torch.stack(datas, dim=0)
-+                merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
-+                yield from super().modify_tensors(data_torch, merged_name, bid)
-+
-+            del self._experts_cache[bid]
-+            return
-+
-+        yield from super().modify_tensors(data_torch, name, bid)
-diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
-index 869e436..760e3dd 100644
---- a/gguf-py/gguf/constants.py
-+++ b/gguf-py/gguf/constants.py
-@@ -525,6 +525,7 @@ class MODEL_ARCH(IntEnum):
-     APERTUS          = auto()
-     COGVLM           = auto()
-     MINIMAXM2        = auto()
-+    MINIMAXM3        = auto()
-     RND1             = auto()
-     PANGU_EMBED      = auto()
-     MISTRAL3         = auto()
-@@ -613,6 +614,10 @@ class MODEL_TENSOR(IntEnum):
-     MOE_LATENT_UP        = auto() # nemotron 3 super
-     ATTN_Q_NORM          = auto()
-     ATTN_K_NORM          = auto()
-+    ATTN_INDEX_Q         = auto() # minimax-m3 sparse-attn indexer (unused)
-+    ATTN_INDEX_K         = auto()
-+    ATTN_INDEX_Q_NORM    = auto()
-+    ATTN_INDEX_K_NORM    = auto()
-     LAYER_OUT_NORM       = auto()
-     LAYER_OUT_SCALE      = auto()
-     PER_LAYER_TOKEN_EMBD = auto() # gemma3n
-@@ -1105,6 +1110,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
-     MODEL_ARCH.GROVEMOE:         "grovemoe",
-     MODEL_ARCH.APERTUS:          "apertus",
-     MODEL_ARCH.MINIMAXM2:        "minimax-m2",
-+    MODEL_ARCH.MINIMAXM3:        "minimax-m3",
-     MODEL_ARCH.COGVLM:           "cogvlm",
-     MODEL_ARCH.RND1:             "rnd1",
-     MODEL_ARCH.PANGU_EMBED:      "pangu-embedded",
-@@ -1163,6 +1169,10 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
-     MODEL_TENSOR.ATTN_GATE:                 "blk.{bid}.attn_gate",
-     MODEL_TENSOR.ATTN_Q_NORM:               "blk.{bid}.attn_q_norm",
-     MODEL_TENSOR.ATTN_K_NORM:               "blk.{bid}.attn_k_norm",
-+    MODEL_TENSOR.ATTN_INDEX_Q:              "blk.{bid}.attn_index_q",
-+    MODEL_TENSOR.ATTN_INDEX_K:              "blk.{bid}.attn_index_k",
-+    MODEL_TENSOR.ATTN_INDEX_Q_NORM:         "blk.{bid}.attn_index_q_norm",
-+    MODEL_TENSOR.ATTN_INDEX_K_NORM:         "blk.{bid}.attn_index_k_norm",
-     MODEL_TENSOR.ATTN_OUT_NORM:             "blk.{bid}.attn_output_norm",
-     MODEL_TENSOR.ATTN_POST_NORM:            "blk.{bid}.post_attention_norm",
-     MODEL_TENSOR.FFN_GATE_INP:              "blk.{bid}.ffn_gate_inp",
-@@ -4102,6 +4112,30 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
-         MODEL_TENSOR.FFN_UP_EXP,
-         MODEL_TENSOR.FFN_EXP_PROBS_B,
-     ],
-+    MODEL_ARCH.MINIMAXM3: [
-+        MODEL_TENSOR.TOKEN_EMBD,
-+        MODEL_TENSOR.OUTPUT_NORM,
-+        MODEL_TENSOR.OUTPUT,
-+        MODEL_TENSOR.ATTN_NORM,
-+        MODEL_TENSOR.ATTN_Q,
-+        MODEL_TENSOR.ATTN_Q_NORM,
-+        MODEL_TENSOR.ATTN_K,
-+        MODEL_TENSOR.ATTN_K_NORM,
-+        MODEL_TENSOR.ATTN_V,
-+        MODEL_TENSOR.ATTN_OUT,
-+        MODEL_TENSOR.FFN_NORM,
-+        MODEL_TENSOR.FFN_GATE_INP,
-+        MODEL_TENSOR.FFN_EXP_PROBS_B,
-+        MODEL_TENSOR.FFN_GATE_EXP,
-+        MODEL_TENSOR.FFN_DOWN_EXP,
-+        MODEL_TENSOR.FFN_UP_EXP,
-+        MODEL_TENSOR.FFN_GATE_SHEXP,
-+        MODEL_TENSOR.FFN_DOWN_SHEXP,
-+        MODEL_TENSOR.FFN_UP_SHEXP,
-+        MODEL_TENSOR.FFN_GATE,
-+        MODEL_TENSOR.FFN_DOWN,
-+        MODEL_TENSOR.FFN_UP,
-+    ],
-     MODEL_ARCH.COGVLM: [
-         MODEL_TENSOR.TOKEN_EMBD,
-         MODEL_TENSOR.OUTPUT_NORM,
-@@ -4128,6 +4162,10 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
-         MODEL_TENSOR.ATTN_Q_NORM,
-         MODEL_TENSOR.ATTN_K,
-         MODEL_TENSOR.ATTN_K_NORM,
-+        MODEL_TENSOR.ATTN_INDEX_Q,
-+        MODEL_TENSOR.ATTN_INDEX_K,
-+        MODEL_TENSOR.ATTN_INDEX_Q_NORM,
-+        MODEL_TENSOR.ATTN_INDEX_K_NORM,
-         MODEL_TENSOR.ATTN_V,
-         MODEL_TENSOR.ATTN_OUT,
-         MODEL_TENSOR.FFN_NORM,
-diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
-index 9efb36f..a62040b 100644
---- a/gguf-py/gguf/tensor_mapping.py
-+++ b/gguf-py/gguf/tensor_mapping.py
-@@ -717,6 +717,22 @@ class TensorNameMap:
-             "model.layers.{bid}.attention.key_layernorm",                     # apertus
-         ),
- 
-+        MODEL_TENSOR.ATTN_INDEX_Q: (
-+            "model.layers.{bid}.self_attn.index_q_proj",                      # minimax-m3 (sparse-attn indexer)
-+        ),
-+
-+        MODEL_TENSOR.ATTN_INDEX_K: (
-+            "model.layers.{bid}.self_attn.index_k_proj",                      # minimax-m3
-+        ),
-+
-+        MODEL_TENSOR.ATTN_INDEX_Q_NORM: (
-+            "model.layers.{bid}.self_attn.index_q_norm",                      # minimax-m3
-+        ),
-+
-+        MODEL_TENSOR.ATTN_INDEX_K_NORM: (
-+            "model.layers.{bid}.self_attn.index_k_norm",                      # minimax-m3
-+        ),
-+
-         MODEL_TENSOR.ROPE_FREQS: (
-             "encoder.layers.{bid}.self_attention.rotary_emb.inv_freq",  # persimmon
-         ),
-diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
-index b890e66..cb8bfc8 100644
---- a/src/llama-arch.cpp
-+++ b/src/llama-arch.cpp
-@@ -125,6 +125,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
-     { LLM_ARCH_GROVEMOE,         "grovemoe"         },
-     { LLM_ARCH_APERTUS,          "apertus"          },
-     { LLM_ARCH_MINIMAX_M2,       "minimax-m2"       },
-+    { LLM_ARCH_MINIMAX_M3,       "minimax-m3"       },
-     { LLM_ARCH_COGVLM,           "cogvlm"           },
-     { LLM_ARCH_RND1,             "rnd1"             },
-     { LLM_ARCH_PANGU_EMBED,      "pangu-embedded"   },
-@@ -395,6 +396,10 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
-     { LLM_TENSOR_ATTN_POST_NORM,                         "blk.%d.post_attention_norm" },
-     { LLM_TENSOR_ATTN_Q_NORM,                            "blk.%d.attn_q_norm" },
-     { LLM_TENSOR_ATTN_K_NORM,                            "blk.%d.attn_k_norm" },
-+    { LLM_TENSOR_ATTN_INDEX_Q,                           "blk.%d.attn_index_q" },
-+    { LLM_TENSOR_ATTN_INDEX_K,                           "blk.%d.attn_index_k" },
-+    { LLM_TENSOR_ATTN_INDEX_Q_NORM,                      "blk.%d.attn_index_q_norm" },
-+    { LLM_TENSOR_ATTN_INDEX_K_NORM,                      "blk.%d.attn_index_k_norm" },
-     { LLM_TENSOR_ATTN_GATE,                              "blk.%d.attn_gate" },
-     { LLM_TENSOR_FFN_POST_NORM,                          "blk.%d.post_ffw_norm" },
-     { LLM_TENSOR_FFN_POST_NORM_1,                        "blk.%d.post_ffw_norm_1" },
-@@ -761,6 +766,11 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
-     {LLM_TENSOR_FFN_NORM_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-     {LLM_TENSOR_ATTN_Q_NORM,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-     {LLM_TENSOR_ATTN_K_NORM,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-+    // minimax-m3 sparse-attn indexer: unused (GGML_OP_NONE) so the loader skips it
-+    {LLM_TENSOR_ATTN_INDEX_Q,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
-+    {LLM_TENSOR_ATTN_INDEX_K,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
-+    {LLM_TENSOR_ATTN_INDEX_Q_NORM,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
-+    {LLM_TENSOR_ATTN_INDEX_K_NORM,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
-     {LLM_TENSOR_LAYER_OUT_NORM,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-     {LLM_TENSOR_LAYER_OUT_SCALE,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-     {LLM_TENSOR_ATTN_Q_A_NORM,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
-@@ -998,6 +1008,7 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
-         case LLM_ARCH_LFM2:
-         case LLM_ARCH_LFM2MOE:
-         case LLM_ARCH_MINIMAX_M2:
-+        case LLM_ARCH_MINIMAX_M3:
-         case LLM_ARCH_MISTRAL4:
-         case LLM_ARCH_KIMI_LINEAR:
-             return false;
-diff --git a/src/llama-arch.h b/src/llama-arch.h
-index a4f5091..2d50ead 100644
---- a/src/llama-arch.h
-+++ b/src/llama-arch.h
-@@ -144,6 +144,7 @@ enum llm_arch {
-     LLM_ARCH_TALKIE,
-     LLM_ARCH_MELLUM,
-     LLM_ARCH_EAGLE3,
-+    LLM_ARCH_MINIMAX_M3,
-     LLM_ARCH_DFLASH,
-     LLM_ARCH_UNKNOWN,
- };
-@@ -429,6 +430,10 @@ enum llm_tensor {
-     LLM_TENSOR_FFN_LATENT_UP,
-     LLM_TENSOR_ATTN_Q_NORM,
-     LLM_TENSOR_ATTN_K_NORM,
-+    LLM_TENSOR_ATTN_INDEX_Q,      // minimax-m3 sparse-attn indexer (unused)
-+    LLM_TENSOR_ATTN_INDEX_K,
-+    LLM_TENSOR_ATTN_INDEX_Q_NORM,
-+    LLM_TENSOR_ATTN_INDEX_K_NORM,
-     LLM_TENSOR_LAYER_OUT_NORM,
-     LLM_TENSOR_LAYER_OUT_SCALE,
-     LLM_TENSOR_POST_ATTN_NORM,
-diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
-index c8ecb0a..4c2c286 100644
---- a/src/llama-graph.cpp
-+++ b/src/llama-graph.cpp
-@@ -1719,6 +1719,16 @@ ggml_tensor * llm_graph_context::build_ffn(
-                 cur = ggml_reglu(ctx0, cur);
-                 cb(cur, "ffn_reglu", il);
-             } break;
-+        case LLM_FFN_SWIGLU_OAI:
-+            {
-+                // clamped SwiGLU: parallel gate path (cur=gate, tmp=up)
-+                GGML_ASSERT(gate && type_gate == LLM_FFN_PAR);
-+                constexpr float alpha = 1.702f;
-+                constexpr float limit = 7.0f;
-+                cur = ggml_swiglu_oai(ctx0, cur, tmp, alpha, limit);
-+                cb(cur, "ffn_swiglu_oai", il);
-+                type_gate = LLM_FFN_SEQ; // gate*up already fused; skip the par multiply
-+            } break;
-         default:
-             GGML_ABORT("fatal error");
-     }
-diff --git a/src/llama-graph.h b/src/llama-graph.h
-index c84cb6a..806ce7b 100644
---- a/src/llama-graph.h
-+++ b/src/llama-graph.h
-@@ -54,6 +54,7 @@ enum llm_ffn_op_type : int {
-     LLM_FFN_SWIGLU,
-     LLM_FFN_GEGLU,
-     LLM_FFN_REGLU,
-+    LLM_FFN_SWIGLU_OAI,
-     LLM_FFN_SWIGLU_OAI_MOE,
- };
- 
-diff --git a/src/llama-model.cpp b/src/llama-model.cpp
-index d874813..7bb71c0 100644
---- a/src/llama-model.cpp
-+++ b/src/llama-model.cpp
-@@ -280,6 +280,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
-             return new llama_model_apertus(params);
-         case LLM_ARCH_MINIMAX_M2:
-             return new llama_model_minimax_m2(params);
-+        case LLM_ARCH_MINIMAX_M3:
-+            return new llama_model_minimax_m3(params);
-         case LLM_ARCH_COGVLM:
-             return new llama_model_cogvlm(params);
-         case LLM_ARCH_PANGU_EMBED:
-@@ -807,6 +809,7 @@ const char * llm_type_name(llm_type type) {
-         case LLM_TYPE_310B_A15B:     return "310B.A15B";
-         case LLM_TYPE_355B_A32B:     return "355B.A32B";
-         case LLM_TYPE_397B_A17B:     return "397B.A17B";
-+        case LLM_TYPE_428B_A23B:     return "428B.A23B";
-         case LLM_TYPE_685B_A37B:     return "685B.A37B";
-         case LLM_TYPE_744B_A40B:     return "744B.A40B";
-         case LLM_TYPE_E2B:           return "E2B";
-@@ -2532,6 +2535,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
-         case LLM_ARCH_GROVEMOE:
-         case LLM_ARCH_APERTUS:
-         case LLM_ARCH_MINIMAX_M2:
-+        case LLM_ARCH_MINIMAX_M3:
-         case LLM_ARCH_COGVLM:
-         case LLM_ARCH_PANGU_EMBED:
-         case LLM_ARCH_AFMOE:
-diff --git a/src/llama-model.h b/src/llama-model.h
-index 45b054c..540e0d2 100644
---- a/src/llama-model.h
-+++ b/src/llama-model.h
-@@ -139,6 +139,7 @@ enum llm_type {
-     LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
-     LLM_TYPE_355B_A32B, // GLM-4.5
-     LLM_TYPE_397B_A17B, // Qwen3.5
-+    LLM_TYPE_428B_A23B, // MiniMax M3
-     LLM_TYPE_685B_A37B, // DeepSeek V3.2
-     LLM_TYPE_744B_A40B, // GLM-5
-     LLM_TYPE_E2B,
-diff --git a/src/models/minimax-m3.cpp b/src/models/minimax-m3.cpp
-new file mode 100644
-index 0000000..137852a
---- /dev/null
-+++ b/src/models/minimax-m3.cpp
-@@ -0,0 +1,197 @@
-+#include "models.h"
-+
-+// MiniMax-M3, text-only: MiniMax-M2 GQA (per-head QK-norm, partial rotary) + DeepSeek-V3
-+// leading-dense/routed/shared experts (swigluoai). Sparse attn -> dense; vision + MTP dropped.
-+
-+void llama_model_minimax_m3::load_arch_hparams(llama_model_loader & ml) {
-+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
-+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
-+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
-+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
-+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
-+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func);
-+
-+    switch (hparams.n_layer()) {
-+        case 60: type = LLM_TYPE_428B_A23B; break;
-+        default: type = LLM_TYPE_UNKNOWN;
-+    }
-+}
-+
-+void llama_model_minimax_m3::load_arch_tensors(llama_model_loader &) {
-+    LLAMA_LOAD_LOCALS;
-+    const int64_t n_expert_shared = hparams.n_expert_shared;
-+    const int64_t n_ff_exp        = hparams.n_ff_exp;
-+
-+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-+
-+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-+
-+    for (int i = 0; i < n_layer; ++i) {
-+        auto & layer = layers[i];
-+
-+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
-+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-+
-+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-+        // per-head QK-norm (one head_dim vector)
-+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-+
-+        // sparse-attn indexer (unused): GGML_OP_NONE -> loader skips; NOT_REQUIRED -> older GGUFs still load;
-+        // SKIP_IF_VIRTUAL -> no-file loader (test-llama-archs) skips them too
-+        const int64_t n_index_head = 4;    // sparse_num_index_heads
-+        const int64_t d_index      = 128;  // sparse_index_dim
-+        const int idx_flags = TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL;
-+        create_tensor(tn(LLM_TENSOR_ATTN_INDEX_Q,      "weight", i), {n_embd, n_index_head * d_index}, idx_flags);
-+        create_tensor(tn(LLM_TENSOR_ATTN_INDEX_K,      "weight", i), {n_embd, d_index},                idx_flags);
-+        create_tensor(tn(LLM_TENSOR_ATTN_INDEX_Q_NORM, "weight", i), {d_index},                        idx_flags);
-+        create_tensor(tn(LLM_TENSOR_ATTN_INDEX_K_NORM, "weight", i), {d_index},                        idx_flags);
-+
-+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-+
-+        if (i < (int) hparams.n_layer_dense_lead) {
-+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-+        } else {
-+            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,    "weight", i), {n_embd, n_expert}, 0);
-+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias",   i), {n_expert}, 0);
-+            layer.ffn_gate_exps   = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS,   "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
-+            layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS,   "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
-+            layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,     "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
-+
-+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
-+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-+        }
-+    }
-+}
-+
-+std::unique_ptr<llm_graph_context> llama_model_minimax_m3::build_arch_graph(const llm_graph_params & params) const {
-+    return std::make_unique<graph>(*this, params);
-+}
-+
-+llama_model_minimax_m3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-+    const int64_t n_embd_head = hparams.n_embd_head_v();
-+
-+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
-+    // partial rotary: head_dim != n_rot, so don't assert n_embd_head == n_rot
-+
-+    ggml_tensor * cur;
-+    ggml_tensor * inpL;
-+
-+    inpL = build_inp_embd(model.tok_embd);
-+
-+    ggml_tensor * inp_pos = build_inp_pos();
-+    auto inp_attn = build_attn_inp_kv();
-+    ggml_tensor * inp_out_ids = build_inp_out_ids();
-+
-+    for (int il = 0; il < n_layer; ++il) {
-+        ggml_tensor * inpSA = inpL;
-+
-+        // self-attention
-+        {
-+            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-+            cb(cur, "attn_norm", il);
-+
-+            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
-+                    n_embd_head, n_head, n_head_kv, il);
-+
-+            // per-head QK RMSNorm (weights include Gemma +1)
-+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-+            cb(Qcur, "Qcur_normed", il);
-+            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-+            cb(Kcur, "Kcur_normed", il);
-+
-+            Qcur = ggml_rope_ext(
-+                ctx0, Qcur, inp_pos, nullptr,
-+                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-+                ext_factor, attn_factor, beta_fast, beta_slow
-+                );
-+            Kcur = ggml_rope_ext(
-+                ctx0, Kcur, inp_pos, nullptr,
-+                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-+                ext_factor, attn_factor, beta_fast, beta_slow
-+                );
-+
-+            cb(Qcur, "Qcur", il);
-+            cb(Kcur, "Kcur", il);
-+            cb(Vcur, "Vcur", il);
-+
-+            cur = build_attn(inp_attn,
-+                    model.layers[il].wo, NULL, model.layers[il].wo_s,
-+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-+        }
-+
-+        if (il == n_layer - 1 && inp_out_ids) {
-+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-+        }
-+
-+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-+        cb(ffn_inp, "ffn_inp", il);
-+
-+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
-+        cb(cur, "ffn_norm", il);
-+
-+        if ((uint32_t) il < hparams.n_layer_dense_lead) {
-+            // leading dense
-+            cur = build_ffn(cur,
-+                    model.layers[il].ffn_up,   NULL, NULL,
-+                    model.layers[il].ffn_gate, NULL, NULL,
-+                    model.layers[il].ffn_down, NULL, NULL,
-+                    NULL,
-+                    LLM_FFN_SWIGLU_OAI, LLM_FFN_PAR, il);
-+            cb(cur, "ffn_out", il);
-+        } else {
-+            // routed experts
-+            ggml_tensor * moe_out = build_moe_ffn(cur,
-+                    model.layers[il].ffn_gate_inp,
-+                    model.layers[il].ffn_up_exps,
-+                    model.layers[il].ffn_gate_exps,
-+                    model.layers[il].ffn_down_exps,
-+                    model.layers[il].ffn_exp_probs_b,
-+                    n_expert, n_expert_used,
-+                    LLM_FFN_SWIGLU_OAI_MOE, hparams.expert_weights_norm,
-+                    hparams.expert_weights_scale,
-+                    (llama_expert_gating_func_type) hparams.expert_gating_func,
-+                    il);
-+            cb(moe_out, "ffn_moe_out", il);
-+
-+            // shared expert
-+            ggml_tensor * ffn_shexp = build_ffn(cur,
-+                    model.layers[il].ffn_up_shexp,   NULL, NULL,
-+                    model.layers[il].ffn_gate_shexp, NULL, NULL,
-+                    model.layers[il].ffn_down_shexp, NULL, NULL,
-+                    NULL,
-+                    LLM_FFN_SWIGLU_OAI, LLM_FFN_PAR, il);
-+            cb(ffn_shexp, "ffn_shexp", il);
-+
-+            cur = ggml_add(ctx0, moe_out, ffn_shexp);
-+            cb(cur, "ffn_out", il);
-+        }
-+
-+        cur = ggml_add(ctx0, cur, ffn_inp);
-+
-+        cur = build_cvec(cur, il);
-+        cb(cur, "l_out", il);
-+
-+        // input for next layer
-+        inpL = cur;
-+    }
-+
-+    cur = inpL;
-+
-+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
-+    cb(cur, "result_norm", -1);
-+    res->t_embd = cur;
-+
-+    // lm_head
-+    cur = build_lora_mm(model.output, cur, model.output_s);
-+    cb(cur, "result_output", -1);
-+    res->t_logits = cur;
-+
-+    ggml_build_forward_expand(gf, cur);
-+}
-diff --git a/src/models/models.h b/src/models/models.h
-index 7a52e7b..5e2a826 100644
---- a/src/models/models.h
-+++ b/src/models/models.h
-@@ -1870,6 +1870,17 @@ struct llama_model_minimax_m2 : public llama_model_base {
-     std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
- };
- 
-+struct llama_model_minimax_m3 : public llama_model_base {
-+    llama_model_minimax_m3(const struct llama_model_params & params) : llama_model_base(params) {}
-+    void load_arch_hparams(llama_model_loader & ml) override;
-+    void load_arch_tensors(llama_model_loader & ml) override;
-+
-+    struct graph : public llm_graph_context {
-+        graph(const llama_model & model, const llm_graph_params & params);
-+    };
-+
-+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
-+};
- 
- struct llama_model_cogvlm : public llama_model_base {
-     llama_model_cogvlm(const struct llama_model_params & params) : llama_model_base(params) {}
-diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp
-index f39abe7..2085f43 100644
---- a/tests/test-llama-archs.cpp
-+++ b/tests/test-llama-archs.cpp
-@@ -352,6 +352,7 @@ static bool moe_mandatory(const llm_arch arch) {
-         case LLM_ARCH_LLADA_MOE:
-         case LLM_ARCH_GROVEMOE:
-         case LLM_ARCH_MINIMAX_M2:
-+        case LLM_ARCH_MINIMAX_M3:
-         case LLM_ARCH_RND1:
-         case LLM_ARCH_PADDLEOCR:
-         case LLM_ARCH_MIMO2: