Closed
Description
Name and Version
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 4 CUDA devices:
Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
Device 1: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
Device 2: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes
Device 3: NVIDIA RTX A6000, compute capability 8.6, VMM: yes
version: 5255 (d24d5928)
built with gcc-14 (GCC) 14.2.1 20250210 (Red Hat 14.2.1-8) for x86_64-redhat-linux
Operating systems
Linux
GGML backends
CUDA
Hardware
Ryzen 7 7800X3D, 192GB RAM, 5090+4090x2+A6000
Models
Deepseek V3 0324
Problem description & steps to reproduce
Hi there, many thanks for all the work.
I was trying to use Deepseek V3 0324 Q2_K_XL (https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF-UD/tree/main/UD-Q2_K_XL) on my mixed PC, using ~120GB RAM and the rest on RAM.
When using the -fa flag, I get MUL_MAT failed.
When not using -fa, it works fine.
Model was being loaded with
./llama-server -m '/run/media/pancho/DE1652041651DDD9/HuggingFaceModelDownloader/Storage/GGUFs/DeepSeek-V3-0324-UD-Q2_K_XL-merged.gguf' -c 16384 --no-mmap --no-warmup -fa -v -ngl 99 --override-tensor 'blk\.(2[5-9]|[3-6][0-9])\..*_exps\.=CPU' --override-tensor 'blk\.([1-6])\..*_exps\.=CUDA0' --override-tensor 'blk\.([7-9]|1[0])\..*_exps\.=CUDA1' --override-tensor 'blk\.(1[1-5])\..*_exps\.=CUDA2' --override-tensor 'blk\.(1[6-9]|2[0-4])\..*_exps\.=CUDA3'
First Bad Commit
N/A
Relevant log output
/llama-server -m '/run/media/pancho/DE1652041651DDD9/HuggingFaceModelDownloader/Storage/GGUFs/DeepSeek-V3-0324-UD-Q2_K_XL-merged.gguf' -c 16384 --no-mmap --no-warmup -fa -ngl 99 --override-tensor 'blk\.(2[5-9]|[3-6][0-9])\..*_exps\.=CPU' --override-tensor 'blk\.([1-6])\..*_exps\.=CUDA0' --override-tensor 'blk\.([7-9]|1[0])\..*_exps\.=CUDA1' --override-tensor 'blk\.(1[1-5])\..*_exps\.=CUDA2' --override-tensor 'blk\.(1[6-9]|2[0-4])\..*_exps\.=CUDA3'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 4 CUDA devices:
Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
Device 1: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
Device 2: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes
Device 3: NVIDIA RTX A6000, compute capability 8.6, VMM: yes
build: 5255 (d24d5928) with gcc-14 (GCC) 14.2.1 20250210 (Red Hat 14.2.1-8) for x86_64-redhat-linux
system info: n_threads = 8, n_threads_batch = 8, total_threads = 16
system_info: n_threads = 8 (n_threads_batch = 8) / 16 | CUDA : ARCHS = 860,890,1200 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | FA_ALL_QUANTS = 1 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
main: binding port with default address family
main: HTTP server is listening, hostname: 127.0.0.1, port: 8080, http threads: 15
main: loading model
srv load_model: loading model '/run/media/pancho/DE1652041651DDD9/HuggingFaceModelDownloader/Storage/GGUFs/DeepSeek-V3-0324-UD-Q2_K_XL-merged.gguf'
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 4090) - 23698 MiB free
llama_model_load_from_file_impl: using device CUDA1 (NVIDIA GeForce RTX 4090) - 23698 MiB free
llama_model_load_from_file_impl: using device CUDA2 (NVIDIA GeForce RTX 5090) - 29679 MiB free
llama_model_load_from_file_impl: using device CUDA3 (NVIDIA RTX A6000) - 48281 MiB free
llama_model_loader: loaded meta data with 64 key-value pairs and 1086 tensors from /run/media/pancho/DE1652041651DDD9/HuggingFaceModelDownloader/Storage/GGUFs/DeepSeek-V3-0324-UD-Q2_K_XL-merged.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 = deepseek2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Deepseek-V3-0324
llama_model_loader: - kv 3: general.version str = V3-0324
llama_model_loader: - kv 4: general.basename str = Deepseek-V3-0324
llama_model_loader: - kv 5: general.quantized_by str = Unsloth
llama_model_loader: - kv 6: general.size_label str = 256x20B
llama_model_loader: - kv 7: general.license str = mit
llama_model_loader: - kv 8: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 9: general.base_model.count u32 = 1
llama_model_loader: - kv 10: general.base_model.0.name str = DeepSeek V3 0324
llama_model_loader: - kv 11: general.base_model.0.version str = V3-0324
llama_model_loader: - kv 12: general.base_model.0.organization str = Deepseek Ai
llama_model_loader: - kv 13: general.base_model.0.repo_url str = https://huggingface.co/deepseek-ai/De...
llama_model_loader: - kv 14: general.tags arr[str,4] = ["deepseek_v3", "deepseek", "unsloth"...
llama_model_loader: - kv 15: general.languages arr[str,1] = ["en"]
llama_model_loader: - kv 16: deepseek2.block_count u32 = 61
llama_model_loader: - kv 17: deepseek2.context_length u32 = 163840
llama_model_loader: - kv 18: deepseek2.embedding_length u32 = 7168
llama_model_loader: - kv 19: deepseek2.feed_forward_length u32 = 18432
llama_model_loader: - kv 20: deepseek2.attention.head_count u32 = 128
llama_model_loader: - kv 21: deepseek2.attention.head_count_kv u32 = 1
llama_model_loader: - kv 22: deepseek2.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 23: deepseek2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 24: deepseek2.expert_used_count u32 = 8
llama_model_loader: - kv 25: deepseek2.leading_dense_block_count u32 = 3
llama_model_loader: - kv 26: deepseek2.vocab_size u32 = 129280
llama_model_loader: - kv 27: deepseek2.attention.q_lora_rank u32 = 1536
llama_model_loader: - kv 28: deepseek2.attention.kv_lora_rank u32 = 512
llama_model_loader: - kv 29: deepseek2.attention.key_length u32 = 576
llama_model_loader: - kv 30: deepseek2.attention.value_length u32 = 512
llama_model_loader: - kv 31: deepseek2.attention.key_length_mla u32 = 192
llama_model_loader: - kv 32: deepseek2.attention.value_length_mla u32 = 128
llama_model_loader: - kv 33: deepseek2.expert_feed_forward_length u32 = 2048
llama_model_loader: - kv 34: deepseek2.expert_count u32 = 256
llama_model_loader: - kv 35: deepseek2.expert_shared_count u32 = 1
llama_model_loader: - kv 36: deepseek2.expert_weights_scale f32 = 2.500000
llama_model_loader: - kv 37: deepseek2.expert_weights_norm bool = true
llama_model_loader: - kv 38: deepseek2.expert_gating_func u32 = 2
llama_model_loader: - kv 39: deepseek2.rope.dimension_count u32 = 64
llama_model_loader: - kv 40: deepseek2.rope.scaling.type str = yarn
llama_model_loader: - kv 41: deepseek2.rope.scaling.factor f32 = 40.000000
llama_model_loader: - kv 42: deepseek2.rope.scaling.original_context_length u32 = 4096
llama_model_loader: - kv 43: deepseek2.rope.scaling.yarn_log_multiplier f32 = 0.100000
llama_model_loader: - kv 44: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 45: tokenizer.ggml.pre str = deepseek-v3
llama_model_loader: - kv 46: tokenizer.ggml.tokens arr[str,129280] = ["<｜begin▁of▁sentence｜>", "<�...
llama_model_loader: - kv 47: tokenizer.ggml.token_type arr[i32,129280] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 48: tokenizer.ggml.merges arr[str,127741] = ["Ġ t", "Ġ a", "i n", "Ġ Ġ", "h e...
llama_model_loader: - kv 49: tokenizer.ggml.bos_token_id u32 = 0
llama_model_loader: - kv 50: tokenizer.ggml.eos_token_id u32 = 1
llama_model_loader: - kv 51: tokenizer.ggml.padding_token_id u32 = 2
llama_model_loader: - kv 52: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 53: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 54: tokenizer.chat_template str = {% if not add_generation_prompt is de...
llama_model_loader: - kv 55: general.quantization_version u32 = 2
llama_model_loader: - kv 56: general.file_type u32 = 10
llama_model_loader: - kv 57: quantize.imatrix.file str = DeepSeek-V3-0324-GGUF/imatrix_unsloth...
llama_model_loader: - kv 58: quantize.imatrix.dataset str = unsloth_calibration_DeepSeek-V3-0324.txt
llama_model_loader: - kv 59: quantize.imatrix.entries_count i32 = 720
llama_model_loader: - kv 60: quantize.imatrix.chunks_count i32 = 60
llama_model_loader: - kv 61: split.no u16 = 0
llama_model_loader: - kv 62: split.tensors.count i32 = 1086
llama_model_loader: - kv 63: split.count u16 = 0
llama_model_loader: - type f32: 361 tensors
llama_model_loader: - type q8_0: 122 tensors
llama_model_loader: - type q2_K: 122 tensors
llama_model_loader: - type q3_K: 54 tensors
llama_model_loader: - type q4_K: 389 tensors
llama_model_loader: - type q5_K: 23 tensors
llama_model_loader: - type q6_K: 15 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q2_K - Medium
print_info: file size = 233.18 GiB (2.98 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 818
load: token to piece cache size = 0.8223 MB
print_info: arch = deepseek2
print_info: vocab_only = 0
print_info: n_ctx_train = 163840
print_info: n_embd = 7168
print_info: n_layer = 61
print_info: n_head = 128
print_info: n_head_kv = 1
print_info: n_rot = 64
print_info: n_swa = 0
print_info: n_swa_pattern = 1
print_info: n_embd_head_k = 576
print_info: n_embd_head_v = 512
print_info: n_gqa = 128
print_info: n_embd_k_gqa = 576
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 = 18432
print_info: n_expert = 256
print_info: n_expert_used = 8
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 0
print_info: rope scaling = yarn
print_info: freq_base_train = 10000.0
print_info: freq_scale_train = 0.025
print_info: n_ctx_orig_yarn = 4096
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 = 671B
print_info: model params = 671.03 B
print_info: general.name = Deepseek-V3-0324
print_info: n_layer_dense_lead = 3
print_info: n_lora_q = 1536
print_info: n_lora_kv = 512
print_info: n_embd_head_k_mla = 192
print_info: n_embd_head_v_mla = 128
print_info: n_ff_exp = 2048
print_info: n_expert_shared = 1
print_info: expert_weights_scale = 2.5
print_info: expert_weights_norm = 1
print_info: expert_gating_func = sigmoid
print_info: rope_yarn_log_mul = 0.1000
print_info: vocab type = BPE
print_info: n_vocab = 129280
print_info: n_merges = 127741
print_info: BOS token = 0 '<｜begin▁of▁sentence｜>'
print_info: EOS token = 1 '<｜end▁of▁sentence｜>'
print_info: EOT token = 1 '<｜end▁of▁sentence｜>'
print_info: PAD token = 2 '<｜▁pad▁｜>'
print_info: LF token = 201 'Ċ'
print_info: FIM PRE token = 128801 '<｜fim▁begin｜>'
print_info: FIM SUF token = 128800 '<｜fim▁hole｜>'
print_info: FIM MID token = 128802 '<｜fim▁end｜>'
print_info: EOG token = 1 '<｜end▁of▁sentence｜>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false)
load_tensors: offloading 61 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 62/62 layers to GPU
load_tensors: CUDA0 model buffer size = 18097.53 MiB
load_tensors: CUDA1 model buffer size = 17719.83 MiB
load_tensors: CUDA2 model buffer size = 22027.26 MiB
load_tensors: CUDA3 model buffer size = 38894.36 MiB
load_tensors: CPU model buffer size = 142037.11 MiB
load_all_data: using async uploads for device CUDA0, buffer type CUDA0, backend CUDA0
.......load_all_data: using async uploads for device CUDA1, buffer type CUDA1, backend CUDA1
.......load_all_data: using async uploads for device CUDA2, buffer type CUDA2, backend CUDA2
..........load_all_data: using async uploads for device CUDA3, buffer type CUDA3, backend CUDA3
................load_all_data: no device found for buffer type CPU for async uploads
............................................................
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 16384
llama_context: n_ctx_per_seq = 16384
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = 1
llama_context: freq_base = 10000.0
llama_context: freq_scale = 0.025
llama_context: n_ctx_per_seq (16384) < n_ctx_train (163840) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context: CUDA_Host output buffer size = 0.49 MiB
llama_context: n_ctx = 16384
llama_context: n_ctx = 16384 (padded)
init: kv_size = 16384, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 61, can_shift = 1
init: layer 0: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA0
init: layer 1: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA0
init: layer 2: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA0
init: layer 3: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA0
init: layer 4: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA0
init: layer 5: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA0
init: layer 6: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA0
init: layer 7: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA0
init: layer 8: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA0
init: layer 9: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA0
init: layer 10: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA0
init: layer 11: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA0
init: layer 12: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA1
init: layer 13: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA1
init: layer 14: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA1
init: layer 15: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA1
init: layer 16: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA1
init: layer 17: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA1
init: layer 18: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA1
init: layer 19: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA1
init: layer 20: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA1
init: layer 21: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA1
init: layer 22: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA1
init: layer 23: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA1
init: layer 24: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 25: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 26: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 27: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 28: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 29: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 30: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 31: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 32: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 33: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 34: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 35: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 36: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 37: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 38: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA2
init: layer 39: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 40: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 41: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 42: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 43: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 44: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 45: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 46: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 47: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 48: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 49: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 50: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 51: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 52: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 53: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 54: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 55: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 56: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 57: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 58: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 59: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: layer 60: n_embd_k_gqa = 576, n_embd_v_gqa = 512, dev = CUDA3
init: CUDA0 KV buffer size = 408.00 MiB
init: CUDA1 KV buffer size = 408.00 MiB
init: CUDA2 KV buffer size = 510.00 MiB
init: CUDA3 KV buffer size = 748.00 MiB
llama_context: KV self size = 2074.00 MiB, K (f16): 1098.00 MiB, V (f16): 976.00 MiB
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 5
llama_context: max_nodes = 65536
llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
llama_context: reserving graph for n_tokens = 512, n_seqs = 1
llama_context: reserving graph for n_tokens = 1, n_seqs = 1
llama_context: reserving graph for n_tokens = 512, n_seqs = 1
llama_context: CUDA0 compute buffer size = 3238.50 MiB
llama_context: CUDA1 compute buffer size = 378.00 MiB
llama_context: CUDA2 compute buffer size = 378.00 MiB
llama_context: CUDA3 compute buffer size = 378.00 MiB
llama_context: CUDA_Host compute buffer size = 336.01 MiB
llama_context: graph nodes = 4660
llama_context: graph splits = 307 (with bs=512), 235 (with bs=1)
clear_adapter_lora: call
common_init_from_params: setting dry_penalty_last_n to ctx_size = 16384
srv init: initializing slots, n_slots = 1
slot init: id 0 | task -1 | new slot n_ctx_slot = 16384
slot reset: id 0 | task -1 |
...
slot update_slots: id 0 | task 0 | prompt processing progress, n_past = 2048, n_tokens = 2048, progress = 0.632294
srv update_slots: decoding batch, n_tokens = 2048
set_embeddings: value = 0
clear_adapter_lora: call
/run/media/pancho/6AE20D1AE20CEBDF/ChatIAs/llama.cpp/ggml/src/ggml-cuda/ggml-cuda.cu:75: ggml_cuda_compute_forward: MUL_MAT failed
CUDA error: invalid configuration argument
current device: 0, in function ggml_cuda_compute_forward at /run/media/pancho/6AE20D1AE20CEBDF/ChatIAs/llama.cpp/ggml/src/ggml-cuda/ggml-cuda.cu:2344
err
CUDA error
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[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib64/libthread_db.so.1".
0x00007f47c40876c2 in __syscall_cancel_arch () from /lib64/libc.so.6
#0 0x00007f47c40876c2 in __syscall_cancel_arch () from /lib64/libc.so.6
#1 0x00007f47c407b9da in __internal_syscall_cancel () from /lib64/libc.so.6
#2 0x00007f47c407ba24 in __syscall_cancel () from /lib64/libc.so.6
#3 0x00007f47c40eb5af in wait4 () from /lib64/libc.so.6
#4 0x00007f47c8b35fb6 in ggml_abort () from libggml-base.so
#5 0x00007f47c8c93963 in ggml_cuda_error(char const*, char const*, char const*, int, char const*) () from libggml-cuda.so
#6 0x00007f47c8c9edbe in ggml_backend_cuda_graph_compute(ggml_backend*, ggml_cgraph*) () from libggml-cuda.so
#7 0x00007f47c8b4b344 in ggml_backend_sched_graph_compute_async () from libggml-base.so
#8 0x00007f47d5b9d371 in llama_context::graph_compute(ggml_cgraph*, bool) () from libllama.so
#9 0x00007f47d5ba0ef8 in llama_context::decode(llama_batch&) () from libllama.so
#10 0x00007f47d5ba219b in llama_decode () from libllama.so
#11 0x000000000048b040 in server_context::update_slots() ()
#12 0x000000000045b25c in server_queue::start_loop() ()
#13 0x0000000000426020 in main ()
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