Eval bug: Major performance drop since b7406 #18258
Description
Name and Version
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 2 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
Device 1: NVIDIA GeForce RTX 2070 SUPER, compute capability 7.5, VMM: yes
load_backend: loaded CUDA backend from D:\llamacpp\llama-b7495-bin-win-cuda-12.4-x64\ggml-cuda.dll
load_backend: loaded RPC backend from D:\llamacpp\llama-b7495-bin-win-cuda-12.4-x64\ggml-rpc.dll
load_backend: loaded CPU backend from D:\llamacpp\llama-b7495-bin-win-cuda-12.4-x64\ggml-cpu-haswell.dll
version: 7495 (4117ae5)
built with Clang 19.1.5 for Windows x86_64
Operating systems
Windows
GGML backends
CUDA
Hardware
Ryzen 7 3700x
RTX 3090
Models
fully offload to a 3090
Qwen3-VL-32B-Thinking.Q4_K_M_TEXT_DRAFT:
- llama-server.exe -m D:/lm_studio/mradermacher/Qwen3-VL-32B-Thinking-GGUF/Qwen3-VL-32B-Thinking.Q4_K_M.gguf --jinja -c 20000 -ngl 999 -fa on --temp 1.0 --top-k 40 --top-p 1.0 --min-p 0.05 --repeat-penalty 1.0 --no-mmap --cache-type-k q8_0 --cache-type-v q8_0 --no-warmup --main-gpu 0 -md D:/lm_studio/lmstudio-community/Qwen3-0.6B-GGUF/Qwen3-0.6B-Q8_0.gguf --cache-type-k-draft q8_0 --cache_type-v-draft q8_0 --device-draft CUDA0 --split-mode none
Magistral-Small-2509-UD-Q5_K_XL:
- llama-server.exe -m D:\lm_studio\unsloth\Magistral-Small-2509-GGUF\Magistral-Small-2509-UD-Q5_K_XL.gguf --jinja -c 85000 -ngl 999 -fa on --temp 0.7 --top-k 40 --top-p 0.95 --min-p 0.01 --repeat-penalty 1.1 --no-mmap --split-mode none --main-gpu 0 --cache-type-k q8_0 --cache-type-v q8_0 --no-warmup
Found similar result for Mistral-Small-3.2-24B-Instruct-2506-UD-Q5_K_XL but I do not recorded t/s for it.
Problem description & steps to reproduce
Lately I have major performance reduction on these 2 models: Qwen3-VL-32B-Thinking.Q4_K_M_TEXT_DRAFT and Magistral-Small-2509-UD-Q5_K_XL.
I have downloaded numerous binary build and located where the reduction in performance started.
Below are what I found:
b7406 :
- Qwen3-VL-32B-Thinking.Q4_K_M_TEXT_DRAFT - 45 t/s
- Magistral-Small-2509-UD-Q5_K_XL - 45 t/s
b7410 :
- Qwen3-VL-32B-Thinking.Q4_K_M_TEXT_DRAFT - 10 t/s
- Magistral-Small-2509-UD-Q5_K_XL - 20 t/s
b7495:
- Qwen3-VL-32B-Thinking.Q4_K_M_TEXT_DRAFT - failed to load, log below
- Magistral-Small-2509-UD-Q5_K_XL - 20 t/s
First Bad Commit
b7410 is when the performance started.
I am not sure when the loading problem started for Qwen3-VL-32B-Thinking.Q4_K_M.
Relevant log output
log for Qwen3-VL-32B-Thinking.Q4_K_M with draft that fail to load on b7495:
Working Dir: D:/llamacpp/llama-b7495-bin-win-cuda-12.4-x64
Executing Command: llama-server.exe -m D:/lm_studio/mradermacher/Qwen3-VL-32B-Thinking-GGUF/Qwen3-VL-32B-Thinking.Q4_K_M.gguf --jinja -c 20000 -ngl 999 -fa on --temp 1.0 --top-k 40 --top-p 1.0 --min-p 0.05 --repeat-penalty 1.0 --no-mmap --cache-type-k q8_0 --cache-type-v q8_0 --no-warmup --main-gpu 0 -md D:/lm_studio/lmstudio-community/Qwen3-0.6B-GGUF/Qwen3-0.6B-Q8_0.gguf --cache-type-k-draft q8_0 --cache_type-v-draft q8_0 --device-draft CUDA0 --split-mode none
================================================================================
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 2 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
Device 1: NVIDIA GeForce RTX 2070 SUPER, compute capability 7.5, VMM: yes
load_backend: loaded CUDA backend from D:\llamacpp\llama-b7495-bin-win-cuda-12.4-x64\ggml-cuda.dll
load_backend: loaded RPC backend from D:\llamacpp\llama-b7495-bin-win-cuda-12.4-x64\ggml-rpc.dll
load_backend: loaded CPU backend from D:\llamacpp\llama-b7495-bin-win-cuda-12.4-x64\ggml-cpu-haswell.dll
main: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true
build: 7495 (4117ae555) with Clang 19.1.5 for Windows x86_64
system info: n_threads = 8, n_threads_batch = 8, total_threads = 16
system_info: n_threads = 8 (n_threads_batch = 8) / 16 | CUDA : ARCHS = 500,610,700,750,800,860,890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
init: using 15 threads for HTTP server
start: binding port with default address family
main: loading model
srv load_model: loading model 'D:/lm_studio/mradermacher/Qwen3-VL-32B-Thinking-GGUF/Qwen3-VL-32B-Thinking.Q4_K_M.gguf'
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: projected to use 21416 MiB of device memory vs. 24575 MiB of free device memory
llama_params_fit_impl: will leave 1887 >= 1024 MiB of free device memory, no changes needed
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.46 seconds
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3090) (0000:0b:00.0) - 23304 MiB free
llama_model_loader: loaded meta data with 39 key-value pairs and 707 tensors from D:/lm_studio/mradermacher/Qwen3-VL-32B-Thinking-GGUF/Qwen3-VL-32B-Thinking.Q4_K_M.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 = qwen3vl
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Qwen3 VL 32B Thinking
llama_model_loader: - kv 3: general.finetune str = Thinking
llama_model_loader: - kv 4: general.basename str = Qwen3-VL
llama_model_loader: - kv 5: general.size_label str = 32B
llama_model_loader: - kv 6: general.license str = apache-2.0
llama_model_loader: - kv 7: general.tags arr[str,1] = ["image-text-to-text"]
llama_model_loader: - kv 8: qwen3vl.block_count u32 = 64
llama_model_loader: - kv 9: qwen3vl.context_length u32 = 262144
llama_model_loader: - kv 10: qwen3vl.embedding_length u32 = 5120
llama_model_loader: - kv 11: qwen3vl.feed_forward_length u32 = 25600
llama_model_loader: - kv 12: qwen3vl.attention.head_count u32 = 64
llama_model_loader: - kv 13: qwen3vl.attention.head_count_kv u32 = 8
llama_model_loader: - kv 14: qwen3vl.rope.freq_base f32 = 5000000.000000
llama_model_loader: - kv 15: qwen3vl.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 16: qwen3vl.attention.key_length u32 = 128
llama_model_loader: - kv 17: qwen3vl.attention.value_length u32 = 128
llama_model_loader: - kv 18: qwen3vl.rope.dimension_sections arr[i32,4] = [24, 20, 20, 0]
llama_model_loader: - kv 19: qwen3vl.n_deepstack_layers u32 = 3
llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 21: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 25: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 26: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 27: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 28: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 29: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 30: general.quantization_version u32 = 2
llama_model_loader: - kv 31: general.file_type u32 = 15
llama_model_loader: - kv 32: general.url str = https://huggingface.co/mradermacher/Q...
llama_model_loader: - kv 33: mradermacher.quantize_version str = 2
llama_model_loader: - kv 34: mradermacher.quantized_by str = mradermacher
llama_model_loader: - kv 35: mradermacher.quantized_at str = 2025-10-30T18:34:42+01:00
llama_model_loader: - kv 36: mradermacher.quantized_on str = nico1
llama_model_loader: - kv 37: general.source.url str = https://huggingface.co/Qwen/Qwen3-VL-...
llama_model_loader: - kv 38: mradermacher.convert_type str = hf
llama_model_loader: - type f32: 257 tensors
llama_model_loader: - type q4_K: 385 tensors
llama_model_loader: - type q6_K: 65 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 18.40 GiB (4.82 BPW)
load: printing all EOG tokens:
load: - 151643 ('<|endoftext|>')
load: - 151645 ('<|im_end|>')
load: - 151662 ('<|fim_pad|>')
load: - 151663 ('<|repo_name|>')
load: - 151664 ('<|file_sep|>')
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch = qwen3vl
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 5120
print_info: n_embd_inp = 20480
print_info: n_layer = 64
print_info: n_head = 64
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 8
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 25600
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 40
print_info: rope scaling = linear
print_info: freq_base_train = 5000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 262144
print_info: rope_yarn_log_mul= 0.0000
print_info: rope_finetuned = unknown
print_info: mrope sections = [24, 20, 20, 0]
print_info: model type = 32B
print_info: model params = 32.76 B
print_info: general.name = Qwen3 VL 32B Thinking
print_info: vocab type = BPE
print_info: n_vocab = 151936
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 63 repeating layers to GPU
load_tensors: offloaded 65/65 layers to GPU
load_tensors: CPU model buffer size = 417.30 MiB
load_tensors: CUDA0 model buffer size = 18423.65 MiB
.
....
...
....
...
...
....
....
...
...
....
...
...
....
...
...
....
...
....
...
....
...
...
...
....
...
...
...
....
.
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 4
llama_context: n_ctx = 20224
llama_context: n_ctx_seq = 20224
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = true
llama_context: freq_base = 5000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (20224) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 2.32 MiB
llama_kv_cache: CUDA0 KV buffer size = 2686.00 MiB
llama_kv_cache: size = 2686.00 MiB ( 20224 cells, 64 layers, 4/1 seqs), K (q8_0): 1343.00 MiB, V (q8_0): 1343.00 MiB
llama_context: CUDA0 compute buffer size = 306.75 MiB
llama_context: CUDA_Host compute buffer size = 49.52 MiB
llama_context: graph nodes = 2247
llama_context: graph splits = 2
srv load_model: loading draft model 'D:/lm_studio/lmstudio-community/Qwen3-0.6B-GGUF/Qwen3-0.6B-Q8_0.gguf'
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: projected to use 2078 MiB of device memory vs. 24575 MiB of free device memory
llama_params_fit_impl: cannot fulfill margin of 1024 MiB, need to reduce device memory by 1216 MiB
llama_params_fit_impl: context size set by user to 20224 -> no change
llama_params_fit_impl: filling dense layers back-to-front:
llama_params_fit_impl: - CUDA0 (NVIDIA GeForce RTX 3090): 8 layers, 861 MiB used, 1024 MiB free
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took -3.75 seconds
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3090) (0000:0b:00.0) - 1886 MiB free
llama_model_loader: loaded meta data with 27 key-value pairs and 311 tensors from D:/lm_studio/lmstudio-community/Qwen3-0.6B-GGUF/Qwen3-0.6B-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 = qwen3
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Qwen3 0.6B
llama_model_loader: - kv 3: general.basename str = Qwen3
llama_model_loader: - kv 4: general.size_label str = 0.6B
llama_model_loader: - kv 5: qwen3.block_count u32 = 28
llama_model_loader: - kv 6: qwen3.context_length u32 = 32768
llama_model_loader: - kv 7: qwen3.embedding_length u32 = 1024
llama_model_loader: - kv 8: qwen3.feed_forward_length u32 = 3072
llama_model_loader: - kv 9: qwen3.attention.head_count u32 = 16
llama_model_loader: - kv 10: qwen3.attention.head_count_kv u32 = 8
llama_model_loader: - kv 11: qwen3.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 12: qwen3.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 13: qwen3.attention.key_length u32 = 128
llama_model_loader: - kv 14: qwen3.attention.value_length u32 = 128
llama_model_loader: - kv 15: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 16: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 17: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 19: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 22: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 23: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 24: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>...
llama_model_loader: - kv 25: general.quantization_version u32 = 2
llama_model_loader: - kv 26: general.file_type u32 = 7
llama_model_loader: - type f32: 113 tensors
llama_model_loader: - type q8_0: 198 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 761.80 MiB (8.50 BPW)
load: printing all EOG tokens:
load: - 151643 ('<|endoftext|>')
load: - 151645 ('<|im_end|>')
load: - 151662 ('<|fim_pad|>')
load: - 151663 ('<|repo_name|>')
load: - 151664 ('<|file_sep|>')
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch = qwen3
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 32768
print_info: n_embd = 1024
print_info: n_embd_inp = 1024
print_info: n_layer = 28
print_info: n_head = 16
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 2
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 3072
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 32768
print_info: rope_yarn_log_mul= 0.0000
print_info: rope_finetuned = unknown
print_info: model type = 0.6B
print_info: model params = 751.63 M
print_info: general.name = Qwen3 0.6B
print_info: vocab type = BPE
print_info: n_vocab = 151936
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 7 repeating layers to GPU
load_tensors: offloaded 8/29 layers to GPU
load_tensors: CUDA0 model buffer size = 269.28 MiB
load_tensors: CUDA_Host model buffer size = 492.52 MiB
................................................
.............
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 20224
llama_context: n_ctx_seq = 20224
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = true
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (20224) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CPU KV buffer size = 881.34 MiB
llama_kv_cache: CUDA0 KV buffer size = 293.78 MiB
llama_kv_cache: size = 1175.12 MiB ( 20224 cells, 28 layers, 1/1 seqs), K (q8_0): 587.56 MiB, V (q8_0): 587.56 MiB
llama_context: CUDA0 compute buffer size = 298.75 MiB
llama_context: CUDA_Host compute buffer size = 43.51 MiB
llama_context: graph nodes = 987
llama_context: graph splits = 275 (with bs=512), 2 (with bs=1)
srv load_model: initializing slots, n_slots = 4
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 20224
llama_context: n_ctx_seq = 20224
llama_context: n_batch = 20224
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = true
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (20224) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CPU KV buffer size = 881.34 MiB
llama_kv_cache: CUDA0 KV buffer size = 293.78 MiB
llama_kv_cache: size = 1175.12 MiB ( 20224 cells, 28 layers, 1/1 seqs), K (q8_0): 587.56 MiB, V (q8_0): 587.56 MiB
llama_context: CUDA0 compute buffer size = 298.75 MiB
llama_context: CUDA_Host compute buffer size = 43.51 MiB
llama_context: graph nodes = 987
llama_context: graph splits = 275 (with bs=512), 2 (with bs=1)
slot load_model: id 0 | task -1 | new slot, n_ctx = 20224
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 20224
llama_context: n_ctx_seq = 20224
llama_context: n_batch = 20224
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = true
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (20224) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CPU KV buffer size = 881.34 MiB
llama_kv_cache: CUDA0 KV buffer size = 293.78 MiB
llama_kv_cache: size = 1175.12 MiB ( 20224 cells, 28 layers, 1/1 seqs), K (q8_0): 587.56 MiB, V (q8_0): 587.56 MiB
llama_context: CUDA0 compute buffer size = 298.75 MiB
llama_context: CUDA_Host compute buffer size = 43.51 MiB
llama_context: graph nodes = 987
llama_context: graph splits = 275 (with bs=512), 2 (with bs=1)
slot load_model: id 1 | task -1 | new slot, n_ctx = 20224
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 20224
llama_context: n_ctx_seq = 20224
llama_context: n_batch = 20224
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = true
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (20224) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CPU KV buffer size = 881.34 MiB
llama_kv_cache: CUDA0 KV buffer size = 293.78 MiB
llama_kv_cache: size = 1175.12 MiB ( 20224 cells, 28 layers, 1/1 seqs), K (q8_0): 587.56 MiB, V (q8_0): 587.56 MiB
llama_context: CUDA0 compute buffer size = 298.75 MiB
llama_context: CUDA_Host compute buffer size = 43.51 MiB
llama_context: graph nodes = 987
llama_context: graph splits = 275 (with bs=512), 2 (with bs=1)
slot load_model: id 2 | task -1 | new slot, n_ctx = 20224
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 20224
llama_context: n_ctx_seq = 20224
llama_context: n_batch = 20224
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = true
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (20224) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CPU KV buffer size = 881.34 MiB
llama_kv_cache: CUDA0 KV buffer size = 293.78 MiB
llama_kv_cache: size = 1175.12 MiB ( 20224 cells, 28 layers, 1/1 seqs), K (q8_0): 587.56 MiB, V (q8_0): 587.56 MiB
ggml_backend_cuda_buffer_type_alloc_buffer: allocating 298.75 MiB on device 0: cudaMalloc failed: out of memory
ggml_gallocr_reserve_n_impl: failed to allocate CUDA0 buffer of size 313262080
graph_reserve: failed to allocate compute buffers
llama_init_from_model: failed to initialize the context: failed to allocate compute pp buffers
srv load_model: failed to create draft context
srv operator(): operator(): cleaning up before exit...
main: exiting due to model loading errorlog for Qwen3-VL-32B-Thinking.Q4_K_M with draft that able to load on b7406:
Working Dir: D:/llamacpp/llama-b7406-bin-win-cuda-12.4-x64
Executing Command: llama-server.exe -m D:/lm_studio/mradermacher/Qwen3-VL-32B-Thinking-GGUF/Qwen3-VL-32B-Thinking.Q4_K_M.gguf --jinja -c 20000 -ngl 999 -fa on --temp 1.0 --top-k 40 --top-p 1.0 --min-p 0.05 --repeat-penalty 1.0 --no-mmap --cache-type-k q8_0 --cache-type-v q8_0 --no-warmup --main-gpu 0 -md D:/lm_studio/lmstudio-community/Qwen3-0.6B-GGUF/Qwen3-0.6B-Q8_0.gguf --cache-type-k-draft q8_0 --cache_type-v-draft q8_0 --device-draft CUDA0 --split-mode none
================================================================================
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 2 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
Device 1: NVIDIA GeForce RTX 2070 SUPER, compute capability 7.5, VMM: yes
load_backend: loaded CUDA backend from D:\llamacpp\llama-b7406-bin-win-cuda-12.4-x64\ggml-cuda.dll
load_backend: loaded RPC backend from D:\llamacpp\llama-b7406-bin-win-cuda-12.4-x64\ggml-rpc.dll
load_backend: loaded CPU backend from D:\llamacpp\llama-b7406-bin-win-cuda-12.4-x64\ggml-cpu-haswell.dll
build: 7406 (4aced7a63) with Clang 19.1.5 for Windows x86_64
system info: n_threads = 8, n_threads_batch = 8, total_threads = 16
system_info: n_threads = 8 (n_threads_batch = 8) / 16 | CUDA : ARCHS = 500,610,700,750,800,860,890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
init: using 15 threads for HTTP server
start: binding port with default address family
main: loading model
srv load_model: loading model 'D:/lm_studio/mradermacher/Qwen3-VL-32B-Thinking-GGUF/Qwen3-VL-32B-Thinking.Q4_K_M.gguf'
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3090) (0000:0b:00.0) - 23304 MiB free
llama_model_loader: loaded meta data with 39 key-value pairs and 707 tensors from D:/lm_studio/mradermacher/Qwen3-VL-32B-Thinking-GGUF/Qwen3-VL-32B-Thinking.Q4_K_M.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 = qwen3vl
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Qwen3 VL 32B Thinking
llama_model_loader: - kv 3: general.finetune str = Thinking
llama_model_loader: - kv 4: general.basename str = Qwen3-VL
llama_model_loader: - kv 5: general.size_label str = 32B
llama_model_loader: - kv 6: general.license str = apache-2.0
llama_model_loader: - kv 7: general.tags arr[str,1] = ["image-text-to-text"]
llama_model_loader: - kv 8: qwen3vl.block_count u32 = 64
llama_model_loader: - kv 9: qwen3vl.context_length u32 = 262144
llama_model_loader: - kv 10: qwen3vl.embedding_length u32 = 5120
llama_model_loader: - kv 11: qwen3vl.feed_forward_length u32 = 25600
llama_model_loader: - kv 12: qwen3vl.attention.head_count u32 = 64
llama_model_loader: - kv 13: qwen3vl.attention.head_count_kv u32 = 8
llama_model_loader: - kv 14: qwen3vl.rope.freq_base f32 = 5000000.000000
llama_model_loader: - kv 15: qwen3vl.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 16: qwen3vl.attention.key_length u32 = 128
llama_model_loader: - kv 17: qwen3vl.attention.value_length u32 = 128
llama_model_loader: - kv 18: qwen3vl.rope.dimension_sections arr[i32,4] = [24, 20, 20, 0]
llama_model_loader: - kv 19: qwen3vl.n_deepstack_layers u32 = 3
llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 21: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 25: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 26: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 27: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 28: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 29: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 30: general.quantization_version u32 = 2
llama_model_loader: - kv 31: general.file_type u32 = 15
llama_model_loader: - kv 32: general.url str = https://huggingface.co/mradermacher/Q...
llama_model_loader: - kv 33: mradermacher.quantize_version str = 2
llama_model_loader: - kv 34: mradermacher.quantized_by str = mradermacher
llama_model_loader: - kv 35: mradermacher.quantized_at str = 2025-10-30T18:34:42+01:00
llama_model_loader: - kv 36: mradermacher.quantized_on str = nico1
llama_model_loader: - kv 37: general.source.url str = https://huggingface.co/Qwen/Qwen3-VL-...
llama_model_loader: - kv 38: mradermacher.convert_type str = hf
llama_model_loader: - type f32: 257 tensors
llama_model_loader: - type q4_K: 385 tensors
llama_model_loader: - type q6_K: 65 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 18.40 GiB (4.82 BPW)
load: printing all EOG tokens:
load: - 151643 ('<|endoftext|>')
load: - 151645 ('<|im_end|>')
load: - 151662 ('<|fim_pad|>')
load: - 151663 ('<|repo_name|>')
load: - 151664 ('<|file_sep|>')
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch = qwen3vl
print_info: vocab_only = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 5120
print_info: n_embd_inp = 20480
print_info: n_layer = 64
print_info: n_head = 64
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 8
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 25600
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 40
print_info: rope scaling = linear
print_info: freq_base_train = 5000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 262144
print_info: rope_yarn_log_mul= 0.0000
print_info: rope_finetuned = unknown
print_info: mrope sections = [24, 20, 20, 0]
print_info: model type = 32B
print_info: model params = 32.76 B
print_info: general.name = Qwen3 VL 32B Thinking
print_info: vocab type = BPE
print_info: n_vocab = 151936
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false)
load_tensors: offloading 64 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 65/65 layers to GPU
load_tensors: CPU model buffer size = 417.30 MiB
load_tensors: CUDA0 model buffer size = 18423.65 MiB
.
...
.
..
.
..
..
.
..
..
.
..
..
..
..
.
..
..
.
..
..
..
..
.
..
..
.
..
.
..
..
..
.
..
.
..
..
.
..
..
..
..
.
..
..
..
.
..
..
..
.
..
..
.
..
..
.
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 20224
llama_context: n_ctx_seq = 20224
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = false
llama_context: freq_base = 5000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (20224) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CUDA0 KV buffer size = 2686.00 MiB
llama_kv_cache: size = 2686.00 MiB ( 20224 cells, 64 layers, 1/1 seqs), K (q8_0): 1343.00 MiB, V (q8_0): 1343.00 MiB
llama_context: CUDA0 compute buffer size = 306.75 MiB
llama_context: CUDA_Host compute buffer size = 49.52 MiB
llama_context: graph nodes = 2247
llama_context: graph splits = 2
srv load_model: loading draft model 'D:/lm_studio/lmstudio-community/Qwen3-0.6B-GGUF/Qwen3-0.6B-Q8_0.gguf'
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3090) (0000:0b:00.0) - 1886 MiB free
llama_model_loader: loaded meta data with 27 key-value pairs and 311 tensors from D:/lm_studio/lmstudio-community/Qwen3-0.6B-GGUF/Qwen3-0.6B-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 = qwen3
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Qwen3 0.6B
llama_model_loader: - kv 3: general.basename str = Qwen3
llama_model_loader: - kv 4: general.size_label str = 0.6B
llama_model_loader: - kv 5: qwen3.block_count u32 = 28
llama_model_loader: - kv 6: qwen3.context_length u32 = 32768
llama_model_loader: - kv 7: qwen3.embedding_length u32 = 1024
llama_model_loader: - kv 8: qwen3.feed_forward_length u32 = 3072
llama_model_loader: - kv 9: qwen3.attention.head_count u32 = 16
llama_model_loader: - kv 10: qwen3.attention.head_count_kv u32 = 8
llama_model_loader: - kv 11: qwen3.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 12: qwen3.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 13: qwen3.attention.key_length u32 = 128
llama_model_loader: - kv 14: qwen3.attention.value_length u32 = 128
llama_model_loader: - kv 15: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 16: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 17: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 19: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 22: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 23: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 24: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>...
llama_model_loader: - kv 25: general.quantization_version u32 = 2
llama_model_loader: - kv 26: general.file_type u32 = 7
llama_model_loader: - type f32: 113 tensors
llama_model_loader: - type q8_0: 198 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 761.80 MiB (8.50 BPW)
load: printing all EOG tokens:
load: - 151643 ('<|endoftext|>')
load: - 151645 ('<|im_end|>')
load: - 151662 ('<|fim_pad|>')
load: - 151663 ('<|repo_name|>')
load: - 151664 ('<|file_sep|>')
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch = qwen3
print_info: vocab_only = 0
print_info: n_ctx_train = 32768
print_info: n_embd = 1024
print_info: n_embd_inp = 1024
print_info: n_layer = 28
print_info: n_head = 16
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 2
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 3072
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 32768
print_info: rope_yarn_log_mul= 0.0000
print_info: rope_finetuned = unknown
print_info: model type = 0.6B
print_info: model params = 751.63 M
print_info: general.name = Qwen3 0.6B
print_info: vocab type = BPE
print_info: n_vocab = 151936
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false)
load_tensors: offloading 28 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 29/29 layers to GPU
load_tensors: CUDA0 model buffer size = 604.15 MiB
load_tensors: CUDA_Host model buffer size = 157.65 MiB
.............
............................
...................
.
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 20224
llama_context: n_ctx_seq = 20224
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = false
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (20224) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CUDA0 KV buffer size = 1175.12 MiB
llama_kv_cache: size = 1175.12 MiB ( 20224 cells, 28 layers, 1/1 seqs), K (q8_0): 587.56 MiB, V (q8_0): 587.56 MiB
llama_context: CUDA0 compute buffer size = 298.75 MiB
llama_context: CUDA_Host compute buffer size = 41.51 MiB
llama_context: graph nodes = 987
llama_context: graph splits = 2
srv init: initializing slots, n_slots = 1
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 20224
llama_context: n_ctx_seq = 20224
llama_context: n_batch = 20224
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = false
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (20224) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CUDA0 KV buffer size = 1175.12 MiB
llama_kv_cache: size = 1175.12 MiB ( 20224 cells, 28 layers, 1/1 seqs), K (q8_0): 587.56 MiB, V (q8_0): 587.56 MiB
llama_context: CUDA0 compute buffer size = 298.75 MiB
llama_context: CUDA_Host compute buffer size = 41.51 MiB
llama_context: graph nodes = 987
llama_context: graph splits = 2
slot init: id 0 | task -1 | new slot, n_ctx = 20224
srv init: prompt cache is enabled, size limit: 8192 MiB
srv init: use `--cache-ram 0` to disable the prompt cache
srv init: for more info see https://github.com/ggml-org/llama.cpp/pull/16391
srv init: thinking = 1
init: chat template, chat_template: {%- set image_count = namespace(value=0) %}
{%- set video_count = namespace(value=0) %}
{%- macro render_content(content, do_vision_count) %}
{%- if content is string %}
{{- content }}
{%- else %}
{%- for item in content %}
{%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
{%- if do_vision_count %}
{%- set image_count.value = image_count.value + 1 %}
{%- endif %}
{%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
<|vision_start|><|image_pad|><|vision_end|>
{%- elif 'video' in item or item.type == 'video' %}
{%- if do_vision_count %}
{%- set video_count.value = video_count.value + 1 %}
{%- endif %}
{%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
<|vision_start|><|video_pad|><|vision_end|>
{%- elif 'text' in item %}
{{- item.text }}
{%- endif %}
{%- endfor %}
{%- endif %}
{%- endmacro %}
{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- render_content(messages[0].content, false) + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + render_content(messages[0].content, false) + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" %}
{%- set content = render_content(message.content, false) %}
{%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- set content = render_content(message.content, True) %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n<think>\n' }}
{%- endif %}
, example_format: '<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
<think>
'
main: model loaded
main: server is listening on http://127.0.0.1:8080
main: starting the main loop...
srv update_slots: all slots are idle
[INFO] Model is fully loaded.
[DIAGNOSTICS] Detected 'model loaded' string.
srv params_from_: Chat format: Hermes 2 Pro
slot get_availabl: id 0 | task -1 | selected slot by LRU, t_last = -1
slot launch_slot_: id 0 | task -1 | sampler chain: logits -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
slot launch_slot_: id 0 | task 0 | processing task
slot update_slots: id 0 | task 0 | new prompt, n_ctx_slot = 20224, n_keep = 0, task.n_tokens = 3692
slot update_slots: id 0 | task 0 | n_tokens = 0, memory_seq_rm [0, end)
slot update_slots: id 0 | task 0 | prompt processing progress, n_tokens = 2048, batch.n_tokens = 2048, progress = 0.554713
slot update_slots: id 0 | task 0 | n_tokens = 2048, memory_seq_rm [2048, end)
slot update_slots: id 0 | task 0 | prompt processing progress, n_tokens = 3692, batch.n_tokens = 1644, progress = 1.000000
slot update_slots: id 0 | task 0 | prompt done, n_tokens = 3692, batch.n_tokens = 1644
slot print_timing: id 0 | task 0 |
prompt eval time = 3342.05 ms / 3692 tokens ( 0.91 ms per token, 1104.71 tokens per second)
eval time = 91573.07 ms / 4522 tokens ( 20.25 ms per token, 49.38 tokens per second)
total time = 94915.12 ms / 8214 tokens
draft acceptance rate = 0.71409 ( 3574 accepted / 5005 generated)
slot release: id 0 | task 0 | stop processing: n_tokens = 8213, truncated = 0
srv update_slots: all slots are idle
srv log_server_r: request: POST /v1/chat/completions 127.0.0.1 200
'''