Name and Version
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
version: 4202 (9f91251)
built with cc (Ubuntu 13.2.0-23ubuntu4) 13.2.0 for x86_64-linux-gnu
Operating systems
Linux, Ubuntu
Which llama.cpp modules do you know to be affected?
No response
Problem description & steps to reproduce
For some reason KV cache loads only into CPU RAM, not into GPU VRAM. llama.cpp compiled with CUDA support.
First Bad Commit
No response
Relevant log output
~/llama.cpp$ ./llama-server -m models/Mistral-Large-Instruct-2411-Q8_0.gguf --ctx-size 4096 --flash-attn --cache-type-k q8_0 --cache-type-v q8_0 --mlock --gpu-layers 1 --prio-batch 2 --threads 12 --verbose
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
build: 4202 (9f912511) with cc (Ubuntu 13.2.0-23ubuntu4) 13.2.0 for x86_64-linux-gnu
system info: n_threads = 12, n_threads_batch = 12, total_threads = 32
system_info: n_threads = 12 (n_threads_batch = 12) / 32 | CUDA : ARCHS = 890 | FORCE_CUBLAS = 1 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | AARCH64_REPACK = 1 |
main: HTTP server is listening, hostname: 127.0.0.1, port: 8080, http threads: 31
main: loading model
srv load_model: loading model 'models/Mistral-Large-Instruct-2411-Q8_0.gguf'
llama_load_model_from_file: using device CUDA0 (NVIDIA GeForce RTX 4090) - 22350 MiB free
llama_model_loader: loaded meta data with 40 key-value pairs and 795 tensors from models/Mistral-Large-Instruct-2411-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Mistral Large Instruct 2411
llama_model_loader: - kv 3: general.version str = 2411
llama_model_loader: - kv 4: general.finetune str = Instruct
llama_model_loader: - kv 5: general.basename str = Mistral
llama_model_loader: - kv 6: general.size_label str = Large
llama_model_loader: - kv 7: general.license str = other
llama_model_loader: - kv 8: general.license.name str = mrl
llama_model_loader: - kv 9: general.license.link str = https://mistral.ai/licenses/MRL-0.1.md
llama_model_loader: - kv 10: general.languages arr[str,10] = ["en", "fr", "de", "es", "it", "pt", ...
llama_model_loader: - kv 11: llama.block_count u32 = 88
llama_model_loader: - kv 12: llama.context_length u32 = 131072
llama_model_loader: - kv 13: llama.embedding_length u32 = 12288
llama_model_loader: - kv 14: llama.feed_forward_length u32 = 28672
llama_model_loader: - kv 15: llama.attention.head_count u32 = 96
llama_model_loader: - kv 16: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 17: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 18: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 19: llama.attention.key_length u32 = 128
llama_model_loader: - kv 20: llama.attention.value_length u32 = 128
llama_model_loader: - kv 21: general.file_type u32 = 7
llama_model_loader: - kv 22: llama.vocab_size u32 = 32768
llama_model_loader: - kv 23: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 24: tokenizer.ggml.model str = llama
llama_model_loader: - kv 25: tokenizer.ggml.pre str = default
llama_model_loader: - kv 26: tokenizer.ggml.tokens arr[str,32768] = ["<unk>", "<s>", "</s>", "[INST]", "[...
llama_model_loader: - kv 27: tokenizer.ggml.scores arr[f32,32768] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 28: tokenizer.ggml.token_type arr[i32,32768] = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv 29: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 30: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 31: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 32: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 33: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 34: tokenizer.chat_template str = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv 35: tokenizer.ggml.add_space_prefix bool = false
llama_model_loader: - kv 36: general.quantization_version u32 = 2
llama_model_loader: - kv 37: split.no u16 = 0
llama_model_loader: - kv 38: split.count u16 = 0
llama_model_loader: - kv 39: split.tensors.count i32 = 795
llama_model_loader: - type f32: 177 tensors
llama_model_loader: - type q8_0: 618 tensors
llm_load_vocab: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
llm_load_vocab: special tokens cache size = 771
llm_load_vocab: token to piece cache size = 0.1732 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32768
llm_load_print_meta: n_merges = 0
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 12288
llm_load_print_meta: n_layer = 88
llm_load_print_meta: n_head = 96
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 12
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 28672
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = ?B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 122.61 B
llm_load_print_meta: model size = 121.33 GiB (8.50 BPW)
llm_load_print_meta: general.name = Mistral Large Instruct 2411
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 781 '<0x0A>'
llm_load_print_meta: EOG token = 2 '</s>'
llm_load_print_meta: max token length = 48
llm_load_tensors: tensor 'token_embd.weight' (q8_0) (and 785 others) cannot be used with preferred buffer type CPU_AARCH64, using CPU instead
llm_load_tensors: offloading 1 repeating layers to GPU
llm_load_tensors: offloaded 1/89 layers to GPU
llm_load_tensors: CPU_Mapped model buffer size = 122841.70 MiB
llm_load_tensors: CUDA0 model buffer size = 1402.59 MiB
....................................................................................................
llama_new_context_with_model: n_seq_max = 1
llama_new_context_with_model: n_ctx = 4096
llama_new_context_with_model: n_ctx_per_seq = 4096
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 1
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_kv_cache_init: CPU KV buffer size = 739.50 MiB
llama_kv_cache_init: CUDA0 KV buffer size = 8.50 MiB
llama_new_context_with_model: KV self size = 748.00 MiB, K (q8_0): 374.00 MiB, V (q8_0): 374.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.12 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 614.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 32.01 MiB
llama_new_context_with_model: graph nodes = 2471
llama_new_context_with_model: graph splits = 961 (with bs=512), 3 (with bs=1)
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
srv init: initializing slots, n_slots = 1
slot init: id 0 | task -1 | new slot n_ctx_slot = 4096
slot reset: id 0 | task -1 |
main: model loaded
main: chat template, built_in: 1, chat_example: '[INST] You are a helpful assistant
Hello [/INST]Hi there</s>[INST] How are you? [/INST]'
main: server is listening on http://127.0.0.1:8080 - starting the main loop
que start_loop: processing new tasks
que start_loop: update slots
srv update_slots: all slots are idle
srv kv_cache_cle: clearing KV cache
que start_loop: waiting for new tasks
Name and Version
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
version: 4202 (9f91251)
built with cc (Ubuntu 13.2.0-23ubuntu4) 13.2.0 for x86_64-linux-gnu
Operating systems
Linux, Ubuntu
Which llama.cpp modules do you know to be affected?
No response
Problem description & steps to reproduce
For some reason KV cache loads only into CPU RAM, not into GPU VRAM. llama.cpp compiled with CUDA support.
First Bad Commit
No response
Relevant log output