Description
Name and Version
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3080 Laptop GPU, compute capability 8.6, VMM: yes
load_backend: loaded CUDA backend from c:\D_Drive\Copies\ML_MODELS\llama-b5293-bin-win-cuda-cu12.4-x64\ggml-cuda.dll
load_backend: loaded RPC backend from c:\D_Drive\Copies\ML_MODELS\llama-b5293-bin-win-cuda-cu12.4-x64\ggml-rpc.dll
load_backend: loaded CPU backend from c:\D_Drive\Copies\ML_MODELS\llama-b5293-bin-win-cuda-cu12.4-x64\ggml-cpu-haswell.dll
version: 5293 (1e333d5)
built with MSVC 19.29.30159.0 for Windows AMD64
Operating systems
Windows
Which llama.cpp modules do you know to be affected?
llama-server
Command line
llama-server.exe --jinja -fa -m .\LLM\Qwen3-1.7B-UD-Q8_K_XL.gguf -c 4096 -ngl 100 -t 8 --host 192.168.1.64 --port 5000 --log-timestamps -b 2048 -ub 2048 --temp 0.0Problem description & steps to reproduce
It seems that when the total number of tokens (parsing + generation) exceeds or approaches the limit n_ctx_slot - llama-server instead of interrupting the response, it crashes with the following error:
ggml\src\ggml.c:1554: GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src)) failed
First Bad Commit
No response
Relevant log output
.\llama-b5293-bin-win-cuda-cu12.4-x64\llama-server.exe --jinja -fa -m .\LLM\Qwen3-1.7B-UD-Q8_K_XL.gguf -c 4096 -ngl 100 -t 8 --host 192.168.1.64 --port 5000 --log-timestamps -b 2048 -ub 2048 --temp 0.0
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3080 Laptop GPU, compute capability 8.6, VMM: yes
load_backend: loaded CUDA backend from c:\D_Drive\Copies\ML_MODELS\llama-b5293-bin-win-cuda-cu12.4-x64\ggml-cuda.dll
load_backend: loaded RPC backend from c:\D_Drive\Copies\ML_MODELS\llama-b5293-bin-win-cuda-cu12.4-x64\ggml-rpc.dll
load_backend: loaded CPU backend from c:\D_Drive\Copies\ML_MODELS\llama-b5293-bin-win-cuda-cu12.4-x64\ggml-cpu-haswell.dll
build: 5293 (1e333d5b) with MSVC 19.29.30159.0 for Windows AMD64
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 | 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 | AARCH64_REPACK = 1 |
main: binding port with default address family
main: HTTP server is listening, hostname: 192.168.1.64, port: 5000, http threads: 15
main: loading model
srv load_model: loading model '.\LLM\Qwen3-1.7B-UD-Q8_K_XL.gguf'
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3080 Laptop GPU) - 15253 MiB free
llama_model_loader: loaded meta data with 32 key-value pairs and 310 tensors from .\LLM\Qwen3-1.7B-UD-Q8_K_XL.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 = Qwen3-1.7B
llama_model_loader: - kv 3: general.basename str = Qwen3-1.7B
llama_model_loader: - kv 4: general.quantized_by str = Unsloth
llama_model_loader: - kv 5: general.size_label str = 1.7B
llama_model_loader: - kv 6: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 7: qwen3.block_count u32 = 28
llama_model_loader: - kv 8: qwen3.context_length u32 = 40960
llama_model_loader: - kv 9: qwen3.embedding_length u32 = 2048
llama_model_loader: - kv 10: qwen3.feed_forward_length u32 = 6144
llama_model_loader: - kv 11: qwen3.attention.head_count u32 = 16
llama_model_loader: - kv 12: qwen3.attention.head_count_kv u32 = 8
llama_model_loader: - kv 13: qwen3.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 14: qwen3.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 15: qwen3.attention.key_length u32 = 128
llama_model_loader: - kv 16: qwen3.attention.value_length u32 = 128
llama_model_loader: - kv 17: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 18: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 19: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 20: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 21: tokenizer.ggml.merges arr[str,151387] = ["─а ─а", "─а─а ─а─а", "i n", "─а t",...
llama_model_loader: - kv 22: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 23: tokenizer.ggml.padding_token_id u32 = 151654
llama_model_loader: - kv 24: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 25: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>...
llama_model_loader: - kv 26: general.quantization_version u32 = 2
llama_model_loader: - kv 27: general.file_type u32 = 7
llama_model_loader: - kv 28: quantize.imatrix.file str = Qwen3-1.7B-GGUF/imatrix_unsloth.dat
llama_model_loader: - kv 29: quantize.imatrix.dataset str = unsloth_calibration_Qwen3-1.7B.txt
llama_model_loader: - kv 30: quantize.imatrix.entries_count i32 = 196
llama_model_loader: - kv 31: quantize.imatrix.chunks_count i32 = 32
llama_model_loader: - type f32: 113 tensors
llama_model_loader: - type q8_0: 171 tensors
llama_model_loader: - type bf16: 26 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 2.17 GiB (10.82 BPW)
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 = 40960
print_info: n_embd = 2048
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: n_swa_pattern = 1
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 = 6144
print_info: n_expert = 0
print_info: n_expert_used = 0
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 = 40960
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 = 1.7B
print_info: model params = 1.72 B
print_info: general.name = Qwen3-1.7B
print_info: vocab type = BPE
print_info: n_vocab = 151936
print_info: n_merges = 151387
print_info: BOS token = 11 ','
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151654 '<|vision_pad|>'
print_info: LF token = 198 '─К'
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 = true)
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 = 2218.85 MiB
load_tensors: CPU_Mapped model buffer size = 593.50 MiB
...........................................................
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 4096
llama_context: n_ctx_per_seq = 4096
llama_context: n_batch = 2048
llama_context: n_ubatch = 2048
llama_context: causal_attn = 1
llama_context: flash_attn = 1
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_per_seq (4096) < n_ctx_train (40960) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache_unified: kv_size = 4096, type_k = 'f16', type_v = 'f16', n_layer = 28, can_shift = 1, padding = 256
llama_kv_cache_unified: CUDA0 KV buffer size = 448.00 MiB
llama_kv_cache_unified: KV self size = 448.00 MiB, K (f16): 224.00 MiB, V (f16): 224.00 MiB
llama_context: CUDA0 compute buffer size = 1203.00 MiB
llama_context: CUDA_Host compute buffer size = 48.02 MiB
llama_context: graph nodes = 959
llama_context: graph splits = 2
common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096
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
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=messages|length - 1) %}
{%- for forward_message in messages %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- set message = messages[index] %}
{%- set tool_start = '<tool_response>' %}
{%- set tool_start_length = tool_start|length %}
{%- set start_of_message = message.content[:tool_start_length] %}
{%- set tool_end = '</tool_response>' %}
{%- set tool_end_length = tool_end|length %}
{%- set start_pos = (message.content|length) - tool_end_length %}
{%- if start_pos < 0 %}
{%- set start_pos = 0 %}
{%- endif %}
{%- set end_of_message = message.content[start_pos:] %}
{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|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 is not none %}
947D
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in message.content %}
{%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
{%- set reasoning_content = (reasoning_content.split('<think>')|last).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' }}
{{- message.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' }}
{%- if enable_thinking is defined and enable_thinking is 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://192.168.1.64:5000 - starting the main loop
srv update_slots: all slots are idle
srv params_from_: Chat format: Content-only
slot launch_slot_: id 0 | task 0 | processing task
slot update_slots: id 0 | task 0 | new prompt, n_ctx_slot = 4096, n_keep = 0, n_prompt_tokens = 543
slot update_slots: id 0 | task 0 | kv cache rm [0, end)
slot update_slots: id 0 | task 0 | prompt processing progress, n_past = 543, n_tokens = 543, progress = 1.000000
slot update_slots: id 0 | task 0 | prompt done, n_past = 543, n_tokens = 543
slot release: id 0 | task 0 | stop processing: n_past = 2660, truncated = 0
slot print_timing: id 0 | task 0 |
prompt eval time = 96.55 ms / 543 tokens ( 0.18 ms per token, 5624.15 tokens per second)
eval time = 19154.37 ms / 2118 tokens ( 9.04 ms per token, 110.58 tokens per second)
total time = 19250.91 ms / 2661 tokens
srv update_slots: all slots are idle
srv log_server_r: request: POST /v1/chat/completions 192.168.1.64 200
srv params_from_: Chat format: Content-only
slot launch_slot_: id 0 | task 2119 | processing task
slot update_slots: id 0 | task 2119 | new prompt, n_ctx_slot = 4096, n_keep = 0, n_prompt_tokens = 547
slot update_slots: id 0 | task 2119 | kv cache rm [538, end)
slot update_slots: id 0 | task 2119 | prompt processing progress, n_past = 547, n_tokens = 9, progress = 0.016453
slot update_slots: id 0 | task 2119 | prompt done, n_past = 547, n_tokens = 9
slot release: id 0 | task 2119 | stop processing: n_past = 1161, truncated = 0
slot print_timing: id 0 | task 2119 |
prompt eval time = 188.43 ms / 9 tokens ( 20.94 ms per token, 47.76 tokens per second)
eval time = 5479.40 ms / 615 tokens ( 8.91 ms per token, 112.24 tokens per second)
total time = 5667.83 ms / 624 tokens
srv update_slots: all slots are idle
srv log_server_r: request: POST /v1/chat/completions 192.168.1.64 200
srv params_from_: Chat format: Content-only
slot launch_slot_: id 0 | task 2735 | processing task
slot update_slots: id 0 | task 2735 | new prompt, n_ctx_slot = 4096, n_keep = 0, n_prompt_tokens = 547
slot update_slots: id 0 | task 2735 | need to evaluate at least 1 token to generate logits, n_past = 547, n_prompt_tokens = 547
slot update_slots: id 0 | task 2735 | kv cache rm [546, end)
slot update_slots: id 0 | task 2735 | prompt processing progress, n_past = 547, n_tokens = 1, progress = 0.001828
slot update_slots: id 0 | task 2735 | prompt done, n_past = 547, n_tokens = 1
slot release: id 0 | task 2735 | stop processing: n_past = 2656, truncated = 0
slot print_timing: id 0 | task 2735 |
prompt eval time = 152.14 ms / 1 tokens ( 152.14 ms per token, 6.57 tokens per second)
eval time = 19106.85 ms / 2110 tokens ( 9.06 ms per token, 110.43 tokens per second)
total time = 19258.99 ms / 2111 tokens
srv update_slots: all slots are idle
srv log_server_r: request: POST /v1/chat/completions 192.168.1.64 200
srv params_from_: Chat format: Content-only
slot launch_slot_: id 0 | task 4846 | processing task
slot update_slots: id 0 | task 4846 | new prompt, n_ctx_slot = 4096, n_keep = 0, n_prompt_tokens = 87
slot update_slots: id 0 | task 4846 | kv cache rm [1, end)
slot update_slots: id 0 | task 4846 | prompt processing progress, n_past = 87, n_tokens = 86, progress = 0.988506
slot update_slots: id 0 | task 4846 | prompt done, n_past = 87, n_tokens = 86
slot update_slots: id 0 | task 4846 | slot context shift, n_keep = 0, n_left = 4095, n_discard = 2047
D:\a\llama.cpp\llama.cpp\ggml\src\ggml.c:1554: GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src)) failed