Open
Description
Name and Version
build: 5335 (d891942) with MSVC 19.43.34810.0 for x64
Operating systems
Windows
GGML backends
Vulkan
Hardware
AMD 8840u (780m)
Models
Qwen3-30B-A3B-Q4_K_M.gguf
https://huggingface.co/bartowski/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen_Qwen3-30B-A3B-Q4_K_M.gguf
Problem description & steps to reproduce
Here are the command lines I used:
".\llama.cpp_release\llama-server.exe" -m ".\_model\Qwen3-30B-A3B-Q4_K_M__bartowski.gguf" -a "Qwen3-30B-A3B-Q4_K_M__bartowski" --host 0.0.0.0 --port 8090 --slots --props --metrics -np 1 -c 20480 -ngl 999 -ctk f16 -ctv f16 --no-mmap --keep 0 --cache-reuse 256 --jinja --chat-template-file ".\_model\Qwen3-30B-A3B-Q4_K_M__bartowski.jinja"I'm not sure if this qualifies as a bug, but I noticed something unusual.
When using the Qwen3-30B-A3B-Q4_K_M.gguf model, if /no_think is enabled in the first query, the second response becomes significantly slower. llama-server web ui show that the second response's Prompt Tokens nearly equal the sum of the first response + second query.
First response:
Prompt
- Tokens: 1
- Time: 80.396 ms
- Speed: 12.4 t/s
Generation
- Tokens: 1116
- Time: 48067.824 ms
- Speed: 23.2 t/sSecond response:
Prompt
- Tokens: 1126
- Time: 37210.091 ms
- Speed: 30.3 t/s
Generation
- Tokens: 178
- Time: 8257.332 ms
- Speed: 21.6 t/sHowever, if /no_think is not used in the first query, the second response remains fast, with Prompt Tokens only reflecting the second query.
First response:
Prompt
- Tokens: 1
- Time: 43.725 ms
- Speed: 22.9 t/s
Generation
- Tokens: 1420
- Time: 62888.84 ms
- Speed: 22.6 t/sSecond response:
Prompt
- Tokens: 16
- Time: 713.299 ms
- Speed: 22.4 t/s
Generation
- Tokens: 378
- Time: 17734.479 ms
- Speed: 21.3 t/sAdditional notes:
- I used this template because Qwen3's default template throws errors.
- Without
--cache-reuse 256, all second responses show high Prompt Tokens and become slow, regardless of/no_think.
Could this be a caching-related issue?
First Bad Commit
No response
Relevant log output
".\llama.cpp_release\llama-server.exe" -m ".\_model\Qwen3-30B-A3B-Q4_K_M__bartowski.gguf" -a "Qwen3-30B-A3B-Q4_K_M__bartowski" --host 0.0.0.0 --port 8090 --slots --props --metrics -np 1 -c 20480 -ngl 999 -ctk f16 -ctv f16 --no-mmap --keep 0 --cache-reuse 256 --jinja --chat-template-file ".\_model\Qwen3-30B-A3B-Q4_K_M__bartowski.jinja"
load_backend: loaded RPC backend from ggml-rpc.dll
ggml_vulkan: Found 1 Vulkan devices:
ggml_vulkan: 0 = AMD Radeon 780M Graphics (AMD proprietary driver) | uma: 1 | fp16: 1 | warp size: 64 | shared memory: 32768 | int dot: 1 | matrix cores: KHR_coopmat
load_backend: loaded Vulkan backend from ggml-vulkan.dll
load_backend: loaded CPU backend from ggml-cpu-icelake.dll
build: 5335 (d8919424) with MSVC 19.43.34810.0 for x64
system info: n_threads = 8, n_threads_batch = 8, total_threads = 16
system_info: n_threads = 8 (n_threads_batch = 8) / 16 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
main: binding port with default address family
main: HTTP server is listening, hostname: 0.0.0.0, port: 8090, http threads: 15
main: loading model
srv load_model: loading model '.\_model\Qwen3-30B-A3B-Q4_K_M__bartowski.gguf'
llama_model_load_from_file_impl: using device Vulkan0 (AMD Radeon 780M Graphics) - 16128 MiB free
llama_model_loader: loaded meta data with 41 key-value pairs and 579 tensors from .\_model\Qwen3-30B-A3B-Q4_K_M__bartowski.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 = qwen3moe
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Qwen3 30B A3B
llama_model_loader: - kv 3: general.basename str = Qwen3
llama_model_loader: - kv 4: general.size_label str = 30B-A3B
llama_model_loader: - kv 5: general.license str = apache-2.0
llama_model_loader: - kv 6: general.license.link str = https://huggingface.co/Qwen/Qwen3-30B...
llama_model_loader: - kv 7: general.base_model.count u32 = 1
llama_model_loader: - kv 8: general.base_model.0.name str = Qwen3 30B A3B Base
llama_model_loader: - kv 9: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 10: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3-30B...
llama_model_loader: - kv 11: general.tags arr[str,1] = ["text-generation"]
llama_model_loader: - kv 12: qwen3moe.block_count u32 = 48
llama_model_loader: - kv 13: qwen3moe.context_length u32 = 32768
llama_model_loader: - kv 14: qwen3moe.embedding_length u32 = 2048
llama_model_loader: - kv 15: qwen3moe.feed_forward_length u32 = 6144
llama_model_loader: - kv 16: qwen3moe.attention.head_count u32 = 32
llama_model_loader: - kv 17: qwen3moe.attention.head_count_kv u32 = 4
llama_model_loader: - kv 18: qwen3moe.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 19: qwen3moe.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 20: qwen3moe.expert_used_count u32 = 8
llama_model_loader: - kv 21: qwen3moe.attention.key_length u32 = 128
llama_model_loader: - kv 22: qwen3moe.attention.value_length u32 = 128
llama_model_loader: - kv 23: qwen3moe.expert_count u32 = 128
llama_model_loader: - kv 24: qwen3moe.expert_feed_forward_length u32 = 768
llama_model_loader: - kv 25: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 26: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 27: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 28: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 29: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 30: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 31: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 32: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 33: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 34: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>...
llama_model_loader: - kv 35: general.quantization_version u32 = 2
llama_model_loader: - kv 36: general.file_type u32 = 15
llama_model_loader: - kv 37: quantize.imatrix.file str = /models_out/Qwen3-30B-A3B-GGUF/Qwen_Q...
llama_model_loader: - kv 38: quantize.imatrix.dataset str = /training_data/calibration_datav3.txt
llama_model_loader: - kv 39: quantize.imatrix.entries_count i32 = 384
llama_model_loader: - kv 40: quantize.imatrix.chunks_count i32 = 209
llama_model_loader: - type f32: 241 tensors
llama_model_loader: - type q8_0: 48 tensors
llama_model_loader: - type q4_K: 193 tensors
llama_model_loader: - type q5_K: 48 tensors
llama_model_loader: - type q6_K: 49 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 17.35 GiB (4.88 BPW)
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch = qwen3moe
print_info: vocab_only = 0
print_info: n_ctx_train = 32768
print_info: n_embd = 2048
print_info: n_layer = 48
print_info: n_head = 32
print_info: n_head_kv = 4
print_info: n_rot = 128
print_info: n_swa = 0
print_info: n_swa_pattern = 1
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 = 512
print_info: n_embd_v_gqa = 512
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 = 6144
print_info: n_expert = 128
print_info: n_expert_used = 8
print_info: causal attn = 1
print_info: pooling type = 0
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_finetuned = unknown
print_info: ssm_d_conv = 0
print_info: ssm_d_inner = 0
print_info: ssm_d_state = 0
print_info: ssm_dt_rank = 0
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 30B.A3B
print_info: model params = 30.53 B
print_info: general.name = Qwen3 30B A3B
print_info: n_ff_exp = 768
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 48 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 49/49 layers to GPU
load_tensors: Vulkan0 model buffer size = 17596.42 MiB
load_tensors: CPU model buffer size = 166.92 MiB
....................................................................................................
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 20480
llama_context: n_ctx_per_seq = 20480
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = 0
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_per_seq (20480) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
llama_context: Vulkan_Host output buffer size = 0.58 MiB
llama_kv_cache_unified: kv_size = 20480, type_k = 'f16', type_v = 'f16', n_layer = 48, can_shift = 1, padding = 32
llama_kv_cache_unified: Vulkan0 KV buffer size = 1920.00 MiB
llama_kv_cache_unified: KV self size = 1920.00 MiB, K (f16): 960.00 MiB, V (f16): 960.00 MiB
llama_context: Vulkan0 compute buffer size = 1344.00 MiB
llama_context: Vulkan_Host compute buffer size = 44.01 MiB
llama_context: graph nodes = 3126
llama_context: graph splits = 2
common_init_from_params: setting dry_penalty_last_n to ctx_size = 20480
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 = 20480
main: model loaded
main: chat template, chat_template: {%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\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' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=0) %}
{%- for message in messages %}
{%- if ns.multi_step_tool %}
{%- if message.role == "user" %}
{%- if not (message.content[0:14] == '<tool_response>' and message.content[-15:] == '</tool_response>') %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = loop.index0 %}
{%- endif %}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.role == "user" or (message.role == "system" and loop.index0 != 0) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }}
{%- elif message.role == "assistant" %}
{%- set content = message.content %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is defined and message.reasoning_content %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '<think>' in message.content and '</think>' in message.content %}
{%- set think_start = message.content.find('<think>') + 7 %}
{%- set think_end = message.content.find('</think>') %}
{%- set reasoning_content = message.content[think_start:think_end] %}
{%- set content = message.content[think_end + 8:] %}
{%- 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 + '\n</think>\n\n' + content }}
{%- 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 %}
{{- '\n' }}
{%- 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" %}
{%- set prev_role = (loop.index0 > 0) and (messages[loop.index0 - 1].role) or '' %}
{%- if loop.index0 == 0 or prev_role != "tool" %}
{{- '<|im_start|>user\n' }}
{%- endif %}
{{- '<tool_response>\n' + message.content + '\n</tool_response>' }}
{%- set next_role = (loop.index0 + 1 < messages|length) and (messages[loop.index0 + 1].role) or '' %}
{%- if loop.index0 == messages|length - 1 or next_role != "tool" %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking == false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- 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
'
main: server is listening on http://0.0.0.0:8090 - starting the main loop
srv update_slots: all slots are idle