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Description
Name and Version
$ ~/llama.cpp/build/bin/llama-cli --version
version: 5390 (aa48e373)
built with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for x86_64-linux-gnu
Operating systems
Linux
Which llama.cpp modules do you know to be affected?
llama-cli
Command line
numactl -i 0 -N 0 ~/llama.cpp/build/bin/llama-cli --numa numactl --no-mmap -fa -ctk q8_0 -ctv q8_0 -m unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF/Q4_K_M/Llama-4-Scout-17B-16E-Instruct-Q4_K_M-00001-of-00002.gguf -c 0 -sp -stProblem description & steps to reproduce
When using the Llama-4-Scout-17B-16E-Instruct model, setting --ctx-size to 10485760 or 5242880 cause assert failed and hang. 2621440 is ok.
llama_kv_cache_unified: kv_size = 10485760, type_k = 'q8_0', type_v = 'q8_0', n_layer = 48, can_shift = 1, padding = 256
llama_kv_cache_unified: CPU KV buffer size = 208896.00 MiB
llama_kv_cache_unified: KV self size = 208896.00 MiB, K (q8_0): 104448.00 MiB, V (q8_0): 104448.00 MiB
/home/user/llama.cpp/ggml/src/ggml.c:1554: GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src)) failed
llama_kv_cache_unified: kv_size = 5242880, type_k = 'q8_0', type_v = 'q8_0', n_layer = 48, can_shift = 1, padding = 256
llama_kv_cache_unified: CPU KV buffer size = 104448.00 MiB
llama_kv_cache_unified: KV self size = 104448.00 MiB, K (q8_0): 52224.00 MiB, V (q8_0): 52224.00 MiB
/home/user/llama.cpp/ggml/src/ggml.c:1554: GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src)) failed
llama_kv_cache_unified: kv_size = 2621440, type_k = 'q8_0', type_v = 'q8_0', n_layer = 48, can_shift = 1, padding = 256
llama_kv_cache_unified: CPU KV buffer size = 261120.00 MiB
llama_kv_cache_unified: KV self size = 261120.00 MiB, K (q8_0): 130560.00 MiB, V (q8_0): 130560.00 MiB
First Bad Commit
No response
Relevant log output
$ numactl -i 0 -N 0 ~/llama.cpp/build/bin/llama-cli --numa numactl --no-mmap -fa -ctk q8_0 -ctv q8_0 -m unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF/Q4_K_M/Llama-4-Scout-17B-16E-Instruct-Q4_K_M-00001-of-00002.gguf -c 0 -sp -st
build: 5390 (aa48e373) with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for x86_64-linux-gnu
main: llama backend init
/proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance
main: load the model and apply lora adapter, if any
llama_model_loader: additional 1 GGUFs metadata loaded.
llama_model_loader: loaded meta data with 51 key-value pairs and 628 tensors from unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF/Q4_K_M/Llama-4-Scout-17B-16E-Instruct-Q4_K_M-00001-of-00002.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 = llama4
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Llama-4-Scout-17B-16E-Instruct
llama_model_loader: - kv 3: general.finetune str = 16E-Instruct
llama_model_loader: - kv 4: general.basename str = Llama-4-Scout-17B-16E-Instruct
llama_model_loader: - kv 5: general.quantized_by str = Unsloth
llama_model_loader: - kv 6: general.size_label str = 17B
llama_model_loader: - kv 7: general.license str = other
llama_model_loader: - kv 8: general.license.name str = llama4
llama_model_loader: - kv 9: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 10: general.base_model.count u32 = 1
llama_model_loader: - kv 11: general.base_model.0.name str = Llama 4 Scout 17B 16E Instruct
llama_model_loader: - kv 12: general.base_model.0.organization str = Meta Llama
llama_model_loader: - kv 13: general.base_model.0.repo_url str = https://huggingface.co/meta-llama/Lla...
llama_model_loader: - kv 14: general.tags arr[str,6] = ["facebook", "unsloth", "meta", "pyto...
llama_model_loader: - kv 15: general.languages arr[str,12] = ["ar", "de", "en", "es", "fr", "hi", ...
llama_model_loader: - kv 16: llama4.block_count u32 = 48
llama_model_loader: - kv 17: llama4.context_length u32 = 10485760
llama_model_loader: - kv 18: llama4.embedding_length u32 = 5120
llama_model_loader: - kv 19: llama4.feed_forward_length u32 = 16384
llama_model_loader: - kv 20: llama4.attention.head_count u32 = 40
llama_model_loader: - kv 21: llama4.attention.head_count_kv u32 = 8
llama_model_loader: - kv 22: llama4.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 23: llama4.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 24: llama4.expert_count u32 = 16
llama_model_loader: - kv 25: llama4.expert_used_count u32 = 1
llama_model_loader: - kv 26: llama4.attention.key_length u32 = 128
llama_model_loader: - kv 27: llama4.attention.value_length u32 = 128
llama_model_loader: - kv 28: llama4.vocab_size u32 = 202048
llama_model_loader: - kv 29: llama4.rope.dimension_count u32 = 128
llama_model_loader: - kv 30: llama4.interleave_moe_layer_step u32 = 1
llama_model_loader: - kv 31: llama4.expert_feed_forward_length u32 = 8192
llama_model_loader: - kv 32: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 33: tokenizer.ggml.pre str = llama4
llama_model_loader: - kv 34: tokenizer.ggml.tokens arr[str,202048] = ["À", "Á", "õ", "ö", "÷", "ø", ...
llama_model_loader: - kv 35: tokenizer.ggml.token_type arr[i32,202048] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 36: tokenizer.ggml.merges arr[str,439802] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 37: tokenizer.ggml.bos_token_id u32 = 200000
llama_model_loader: - kv 38: tokenizer.ggml.eos_token_id u32 = 200008
llama_model_loader: - kv 39: tokenizer.ggml.padding_token_id u32 = 200018
llama_model_loader: - kv 40: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv 41: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 42: general.quantization_version u32 = 2
llama_model_loader: - kv 43: general.file_type u32 = 15
llama_model_loader: - kv 44: quantize.imatrix.file str = Llama-4-Scout-17B-16E-Instruct-GGUF/i...
llama_model_loader: - kv 45: quantize.imatrix.dataset str = unsloth_calibration_Llama-4-Scout-17B...
llama_model_loader: - kv 46: quantize.imatrix.entries_count i32 = 528
llama_model_loader: - kv 47: quantize.imatrix.chunks_count i32 = 59
llama_model_loader: - kv 48: split.no u16 = 0
llama_model_loader: - kv 49: split.tensors.count i32 = 628
llama_model_loader: - kv 50: split.count u16 = 2
llama_model_loader: - type f32: 146 tensors
llama_model_loader: - type q4_K: 409 tensors
llama_model_loader: - type q6_K: 73 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 60.86 GiB (4.85 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 1135
load: token to piece cache size = 1.3873 MB
print_info: arch = llama4
print_info: vocab_only = 0
print_info: n_ctx_train = 10485760
print_info: n_embd = 5120
print_info: n_layer = 48
print_info: n_head = 40
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 1
print_info: n_swa_pattern = 4
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 5
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-05
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 = 16384
print_info: n_expert = 16
print_info: n_expert_used = 1
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 0
print_info: rope scaling = linear
print_info: freq_base_train = 500000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 10485760
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 = 17Bx16E (Scout)
print_info: model params = 107.77 B
print_info: general.name = Llama-4-Scout-17B-16E-Instruct
print_info: vocab type = BPE
print_info: n_vocab = 202048
print_info: n_merges = 439802
print_info: BOS token = 200000 '<|begin_of_text|>'
print_info: EOS token = 200008 '<|eot|>'
print_info: PAD token = 200018 '<|finetune_right_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 200002 '<|fim_prefix|>'
print_info: FIM SUF token = 200004 '<|fim_suffix|>'
print_info: FIM MID token = 200003 '<|fim_middle|>'
print_info: EOG token = 200008 '<|eot|>'
print_info: max token length = 192
load_tensors: loading model tensors, this can take a while... (mmap = false)
load_tensors: CPU model buffer size = 14867.06 MiB
load_tensors: CPU_AARCH64 model buffer size = 47452.50 MiB
...................................................................................................
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 10485760
llama_context: n_ctx_per_seq = 10485760
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = 1
llama_context: freq_base = 500000.0
llama_context: freq_scale = 1
llama_context: CPU output buffer size = 0.77 MiB
llama_kv_cache_unified: kv_size = 10485760, type_k = 'q8_0', type_v = 'q8_0', n_layer = 48, can_shift = 1, padding = 256
llama_kv_cache_unified: CPU KV buffer size = 208896.00 MiB
llama_kv_cache_unified: KV self size = 208896.00 MiB, K (q8_0): 104448.00 MiB, V (q8_0): 104448.00 MiB
/home/user/llama.cpp/ggml/src/ggml.c:1554: GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src)) failed