Implementing DeepSeek V4

common/arg.cpp

@@ -2631,7 +2631,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_LOAD_MODE"));
     add_opt(common_arg(
         {"--longhaul"},
-        "stream routed Qwen3.5 MoE, Laguna, or Inkling experts through a bounded CPU or Metal cache",
+        "stream routed Qwen3.5 MoE, DeepSeek V4, Laguna, or Inkling experts through a bounded CPU or Metal cache",
         [](common_params & params) {
             params.load_mode = LLAMA_LOAD_MODE_LONGHAUL;
         }

docs/longhaul.md

@@ -31,7 +31,7 @@ The normal startup warmup is skipped automatically in longhaul mode. Routed expe
 Longhaul currently requires:
 
 - all repeating layers on CPU, or all repeating layers on Metal
-- Qwen3.5 MoE, Laguna, or Inkling architecture
+- Qwen3.5 MoE, DeepSeek V4, Laguna, or Inkling architecture
 - text generation without embeddings or LoRA adapters
 
 CPU mode is available wherever the CPU backend is supported. Metal mode requires
@@ -63,6 +63,14 @@ once, and independent expert slices are read concurrently where the platform
 supports positional reads. CPU and shared Metal buffers are populated directly;
 private Metal buffers use a staged fallback.
 
+DeepSeek V4 keeps its shared expert, router weights, learned routing bias, and
+hash-routing tables resident. Longhaul streams the routed gate, up, and down
+expert banks. DeepSeek V4 Flash selects six routed experts per token; caches
+with fewer than six slots use the existing staged MoE path. The startup log is
+the authoritative source for slot capacity because the bytes per slot depend on
+the GGUF tensor types and shard layout. MTP tensors remain outside the current
+scope.
+
 Inkling keeps its two shared experts resident and streams only the routed
 256-expert banks. Inkling-Small selects six routed experts per token. In the
 seven-shard Q8_0 model, one cache slot across all 40 MoE layers uses about

src/llama-model.cpp

@@ -1358,8 +1358,9 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
         }
 
         if (params.load_mode == LLAMA_LOAD_MODE_LONGHAUL) {
-            if (arch != LLM_ARCH_QWEN35MOE && arch != LLM_ARCH_LAGUNA && arch != LLM_ARCH_INKLING) {
+            if (arch != LLM_ARCH_QWEN35MOE && arch != LLM_ARCH_DEEPSEEK4 &&
-                throw std::runtime_error("longhaul currently requires a qwen35moe, laguna, or inkling model");
+                    arch != LLM_ARCH_LAGUNA && arch != LLM_ARCH_INKLING) {
+                throw std::runtime_error("longhaul currently requires a qwen35moe, deepseek4, laguna, or inkling model");
             }
             if (hparams.n_layer_nextn != 0) {
                 throw std::runtime_error("longhaul does not support MTP tensors");

src/models/deepseek4.cpp

@@ -64,6 +64,8 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) {
 void llama_model_deepseek4::load_arch_tensors(llama_model_loader &) {
     LLAMA_LOAD_LOCALS;
 
+    const int expert_flags = params.load_mode == LLAMA_LOAD_MODE_LONGHAUL ? TENSOR_LONGHAUL : 0;
+
     const int64_t q_lora_rank     = hparams.n_lora_q;
     const int64_t n_ff_exp        = hparams.n_ff_exp;
     const int64_t n_expert_shared = hparams.n_expert_shared;
@@ -136,9 +138,9 @@ void llama_model_deepseek4::load_arch_tensors(llama_model_loader &) {
         }
         layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 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_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, expert_flags);
-        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd,   n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd,   n_expert}, expert_flags);
-        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, expert_flags);
 
         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);

tests/CMakeLists.txt

@@ -291,6 +291,22 @@ llama_test(
 set_tests_properties(test-longhaul-cpu-inkling-six-slots PROPERTIES
     FIXTURES_REQUIRED generate-models
 )
+llama_test(
+    test-longhaul
+    NAME test-longhaul-cpu-deepseek4
+    ARGS --cpu-model "${MODEL_DIR}/deepseek4-moe.gguf" 147456 1
+)
+set_tests_properties(test-longhaul-cpu-deepseek4 PROPERTIES
+    FIXTURES_REQUIRED generate-models
+)
+llama_test(
+    test-longhaul
+    NAME test-longhaul-cpu-deepseek4-six-slots
+    ARGS --cpu-model "${MODEL_DIR}/deepseek4-moe.gguf" 884736 6
+)
+set_tests_properties(test-longhaul-cpu-deepseek4-six-slots PROPERTIES
+    FIXTURES_REQUIRED generate-models
+)
 if (APPLE AND GGML_METAL)
     llama_test(
         test-longhaul
@@ -308,6 +324,22 @@ if (APPLE AND GGML_METAL)
     set_tests_properties(test-longhaul-metal-inkling-six-slots PROPERTIES
         FIXTURES_REQUIRED generate-models
     )
+    llama_test(
+        test-longhaul
+        NAME test-longhaul-metal-deepseek4
+        ARGS --metal-model "${MODEL_DIR}/deepseek4-moe.gguf" 147456 1
+    )
+    set_tests_properties(test-longhaul-metal-deepseek4 PROPERTIES
+        FIXTURES_REQUIRED generate-models
+    )
+    llama_test(
+        test-longhaul
+        NAME test-longhaul-metal-deepseek4-six-slots
+        ARGS --metal-model "${MODEL_DIR}/deepseek4-moe.gguf" 884736 6
+    )
+    set_tests_properties(test-longhaul-metal-deepseek4-six-slots PROPERTIES
+        FIXTURES_REQUIRED generate-models
+    )
 endif()
 llama_build_and_test(test-token-cache.cpp)
 target_include_directories(test-token-cache PRIVATE ${PROJECT_SOURCE_DIR}/src)

tests/test-llama-archs.cpp

@@ -60,6 +60,12 @@ static void set_tensor_data(struct ggml_tensor * tensor, void * userdata) {
             tmp[i] = ggml_fp32_to_fp16(dis(gen));
         }
         ggml_backend_tensor_set(tensor, tmp.data(), 0, ggml_nbytes(tensor));
+    } else if (tensor->type == GGML_TYPE_I32 && strstr(tensor->name, "ffn_gate_tid2eid") != nullptr) {
+        std::vector<int32_t> tmp(ne);
+        for (int64_t i = 0; i < ne; i++) {
+            tmp[i] = i % tensor->ne[0];
+        }
+        ggml_backend_tensor_set(tensor, tmp.data(), 0, ggml_nbytes(tensor));
     } else {
         GGML_ABORT("fatal error");
     }
@@ -110,6 +116,10 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
         n_embd = 128;
         n_head = 1;
         n_ff   = 192;
+    } else if (arch == LLM_ARCH_DEEPSEEK4) {
+        n_embd = 64;
+        n_head = 4;
+        n_ff   = 96;
     } else if (arch == LLM_ARCH_NEMOTRON_H || arch == LLM_ARCH_NEMOTRON_H_MOE) {
         n_layer = 3;
     } else if (arch == LLM_ARCH_CHAMELEON) {
@@ -158,6 +168,9 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
         }
         ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, n_head_per_layer);
         ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head_per_layer);
+    } else if (arch == LLM_ARCH_DEEPSEEK4) {
+        ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT,    n_head);
+        ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, uint32_t(1));
     } else {
         ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, n_head);
         ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head);
@@ -174,6 +187,10 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
         ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT,       uint32_t(64));
         ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH_MLA,   uint32_t(192));
         ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, uint32_t(128));
+    } else if (arch == LLM_ARCH_DEEPSEEK4) {
+        ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH,   n_embd_head);
+        ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH, n_embd_head);
+        ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT,   n_embd_head/2);
     } else if (arch == LLM_ARCH_MINIMAX_M3) {
         // partial rotary: n_rot must not exceed the indexer key length (64)
         ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT,       uint32_t(64));
@@ -183,7 +200,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
     ms.add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,      1e-5f);
     ms.add_kv(LLM_KV_ATTENTION_GROUPNORM_EPS,          1e-5f);
     ms.add_kv(LLM_KV_ATTENTION_GROUPNORM_GROUPS,       uint32_t(8));
-    ms.add_kv(LLM_KV_ATTENTION_Q_LORA_RANK,            uint32_t(512));
+    ms.add_kv(LLM_KV_ATTENTION_Q_LORA_RANK,            arch == LLM_ARCH_DEEPSEEK4 ? uint32_t(32) : uint32_t(512));
     ms.add_kv(LLM_KV_ATTENTION_KV_LORA_RANK,           uint32_t(512));
     ms.add_kv(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, uint32_t(8));
     ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW,         n_ctx/8);
@@ -226,15 +243,31 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
     if (moe) {
         ms.add_kv(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, n_ff);
         ms.add_kv(LLM_KV_INTERLEAVE_MOE_LAYER_STEP,  uint32_t(2));
-        ms.add_kv(LLM_KV_EXPERT_COUNT,               arch == LLM_ARCH_INKLING ? uint32_t(8) : uint32_t(2));
+        ms.add_kv(LLM_KV_EXPERT_COUNT,               arch == LLM_ARCH_INKLING || arch == LLM_ARCH_DEEPSEEK4 ? uint32_t(8) : uint32_t(2));
-        ms.add_kv(LLM_KV_EXPERT_USED_COUNT,          arch == LLM_ARCH_INKLING ? uint32_t(6) : uint32_t(1));
+        ms.add_kv(LLM_KV_EXPERT_USED_COUNT,          arch == LLM_ARCH_INKLING || arch == LLM_ARCH_DEEPSEEK4 ? uint32_t(6) : uint32_t(1));
         ms.add_kv(LLM_KV_EXPERT_SHARED_COUNT,        arch == LLM_ARCH_INKLING ? uint32_t(2) : uint32_t(1));
-        ms.add_kv(LLM_KV_EXPERT_GATING_FUNC,         uint32_t(2)); // sigmoid
+        ms.add_kv(LLM_KV_EXPERT_GATING_FUNC,         arch == LLM_ARCH_DEEPSEEK4 ? uint32_t(4) : uint32_t(2)); // sqrtsoftplus or sigmoid
         ms.add_kv(LLM_KV_EXPERT_GROUP_SCALE,         1.0f);
         ms.add_kv(LLM_KV_EXPERTS_PER_GROUP,          uint32_t(1));
-        if (arch == LLM_ARCH_INKLING) {
+        if (arch == LLM_ARCH_INKLING || arch == LLM_ARCH_DEEPSEEK4) {
             ms.add_kv(LLM_KV_EXPERT_WEIGHTS_SCALE, 1.0f);
         }
+        if (arch == LLM_ARCH_DEEPSEEK4) {
+            ms.add_kv(LLM_KV_EXPERT_WEIGHTS_NORM, true);
+        }
+    }
+
+    if (arch == LLM_ARCH_DEEPSEEK4) {
+        ms.add_kv(LLM_KV_SWIGLU_CLAMP_EXP,                  10.0f);
+        ms.add_kv(LLM_KV_SWIGLU_CLAMP_SHEXP,                10.0f);
+        ms.add_kv(LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT,      uint32_t(2));
+        ms.add_kv(LLM_KV_ATTENTION_OUTPUT_LORA_RANK,        uint32_t(16));
+        ms.add_kv(LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE, 160000.0f);
+        ms.add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS,         std::vector<uint32_t>{0, 4});
+        ms.add_kv(LLM_KV_HYPER_CONNECTION_COUNT,           uint32_t(4));
+        ms.add_kv(LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS, uint32_t(2));
+        ms.add_kv(LLM_KV_HYPER_CONNECTION_EPSILON,         1.0e-6f);
+        ms.add_kv(LLM_KV_HASH_LAYER_COUNT,                  uint32_t(1));
     }
 
     if (arch == LLM_ARCH_INKLING) {
@@ -266,6 +299,19 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
     ms.add_kv(LLM_KV_WKV_HEAD_SIZE,             n_embd/n_head);
     ms.add_kv(LLM_KV_SHORTCONV_L_CACHE,         uint32_t(3));
 
+    if (arch == LLM_ARCH_DEEPSEEK4) {
+        ggml_init_params params = {
+            /*.mem_size   =*/ ggml_tensor_overhead(),
+            /*.mem_buffer =*/ nullptr,
+            /*.no_alloc   =*/ true,
+        };
+        ggml_context_ptr tensor_ctx(ggml_init(params));
+        ggml_tensor * tid2eid = ggml_new_tensor_2d(
+            tensor_ctx.get(), GGML_TYPE_I32, 6, n_vocab);
+        ggml_set_name(tid2eid, "blk.0.ffn_gate_tid2eid.weight");
+        gguf_add_tensor(ms.gguf_ctx, tid2eid);
+    }
+
     for (uint32_t il = 0; il < n_layer; il++) {
         ggml_tensor t;
         memset(&t, 0, sizeof(ggml_tensor));
@@ -370,6 +416,7 @@ static bool moe_mandatory(const llm_arch arch) {
         case LLM_ARCH_DEEPSEEK:
         case LLM_ARCH_DEEPSEEK2:
         case LLM_ARCH_DEEPSEEK32:
+        case LLM_ARCH_DEEPSEEK4:
         case LLM_ARCH_GLM4_MOE:
         case LLM_ARCH_GLM_DSA:
         case LLM_ARCH_EXAONE_MOE:
@@ -450,10 +497,6 @@ static bool arch_supported(const llm_arch arch) {
     if (arch == LLM_ARCH_DEEPSEEK2OCR) {
         return false;
     }
-    if (arch == LLM_ARCH_DEEPSEEK4) {
-        return false;
-    }
-
     // FIXME: these hit scheduler/view-backed-output issues with WebGPU on CI.
 #ifdef GGML_USE_WEBGPU
     if (arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA) {
@@ -515,15 +558,11 @@ static int save_models(const llm_arch target_arch, const size_t seed, const ggml
             const std::string path = dir + "/" + llm_arch_name(arch) + (moe ? "-moe.gguf" : "-dense.gguf");
             LOG_INF("%s: Saving %s model (%s) to %s...\n", __func__, llm_arch_name(arch), moe ? "MoE" : "dense", path.c_str());
             const bool private_fixture =
-                arch == LLM_ARCH_LAGUNA || arch == LLM_ARCH_INKLING;
+                arch == LLM_ARCH_DEEPSEEK4 || arch == LLM_ARCH_LAGUNA || arch == LLM_ARCH_INKLING;
             if (llama_model_saver_supports_arch(arch) && !private_fixture) {
                 llama_model_save_to_file(model_and_ctx.first.get(), path.c_str());
             } else {
-                // Laguna and Inkling are not supported by the production
+                // Preserve exact fixture metadata for architectures the production saver cannot round-trip yet.
-                // model saver yet.
-                // Preserve the exact synthetic fixture metadata and attach the
-                // initialized model tensors so longhaul can be tested from a
-                // real file without broadening the saver API.
                 gguf_context_ptr fixture(gguf_init_empty());
                 // Model initialization normalizes the caller's GGUF context,
                 // so regenerate the exact private metadata for the file.
@@ -669,7 +708,8 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg
                     FILE * file = tmpfile(); // Can be null on Windows without administrator privileges.
                     // FIXME: when adding a tensor to a gguf_context a copy is made, this changes the pointer which the meta backend
                     //     in turn uses to map the tensors to their simple equivalents - this is fundamentally incompatible
-                    if (file != nullptr && llama_model_saver_supports_arch(arch) && dc.split_mode != LLAMA_SPLIT_MODE_TENSOR) {
+                    if (file != nullptr && llama_model_saver_supports_arch(arch) && arch != LLM_ARCH_DEEPSEEK4 &&
+                            dc.split_mode != LLAMA_SPLIT_MODE_TENSOR) {
                         GGML_ASSERT(model_and_ctx_dev.first && model_and_ctx_dev.second);
                         llama_model_saver ms = llama_model_saver(model_and_ctx_dev.first.get());
                         ms.add_kv_from_model();

tools/cli/README.md

@@ -61,7 +61,7 @@
 | `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_MMAP) |
 | `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available<br/>(env: LLAMA_ARG_DIO) |
 | `-lm, --load-mode MODE` | model loading mode (default: mmap)<br/>- none: no special loading mode<br/>- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>- mlock: force system to keep model in RAM rather than swapping or compressing<br/>- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing<br/>- dio: use DirectIO if available<br/>- longhaul: stream routed MoE experts through a bounded cache<br/><br/>(env: LLAMA_ARG_LOAD_MODE) |
-| `--longhaul` | stream routed Qwen3.5 MoE, Laguna, or Inkling experts through a bounded CPU or Metal cache<br/>(env: LLAMA_ARG_LONGHAUL) |
+| `--longhaul` | stream routed Qwen3.5 MoE, DeepSeek V4, Laguna, or Inkling experts through a bounded CPU or Metal cache<br/>(env: LLAMA_ARG_LONGHAUL) |
 | `--longhaul-cache N` | longhaul expert cache size in GiB<br/>(env: LLAMA_ARG_LONGHAUL_CACHE) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |

tools/completion/README.md

@@ -144,7 +144,7 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_MMAP) |
 | `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available<br/>(env: LLAMA_ARG_DIO) |
 | `-lm, --load-mode MODE` | model loading mode (default: mmap)<br/>- none: no special loading mode<br/>- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>- mlock: force system to keep model in RAM rather than swapping or compressing<br/>- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing<br/>- dio: use DirectIO if available<br/>- longhaul: stream routed MoE experts through a bounded cache<br/><br/>(env: LLAMA_ARG_LOAD_MODE) |
-| `--longhaul` | stream routed Qwen3.5 MoE, Laguna, or Inkling experts through a bounded CPU or Metal cache<br/>(env: LLAMA_ARG_LONGHAUL) |
+| `--longhaul` | stream routed Qwen3.5 MoE, DeepSeek V4, Laguna, or Inkling experts through a bounded CPU or Metal cache<br/>(env: LLAMA_ARG_LONGHAUL) |
 | `--longhaul-cache N` | longhaul expert cache size in GiB<br/>(env: LLAMA_ARG_LONGHAUL_CACHE) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |

tools/server/README.md

@@ -78,7 +78,7 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_MMAP) |
 | `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available<br/>(env: LLAMA_ARG_DIO) |
 | `-lm, --load-mode MODE` | model loading mode (default: mmap)<br/>- none: no special loading mode<br/>- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>- mlock: force system to keep model in RAM rather than swapping or compressing<br/>- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing<br/>- dio: use DirectIO if available<br/>- longhaul: stream routed MoE experts through a bounded cache<br/><br/>(env: LLAMA_ARG_LOAD_MODE) |
-| `--longhaul` | stream routed Qwen3.5 MoE, Laguna, or Inkling experts through a bounded CPU or Metal cache<br/>(env: LLAMA_ARG_LONGHAUL) |
+| `--longhaul` | stream routed Qwen3.5 MoE, DeepSeek V4, Laguna, or Inkling experts through a bounded CPU or Metal cache<br/>(env: LLAMA_ARG_LONGHAUL) |
 | `--longhaul-cache N` | longhaul expert cache size in GiB<br/>(env: LLAMA_ARG_LONGHAUL_CACHE) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |