Name and Version
./llama-cli -v
ggml_cuda_init: found 1 CUDA devices (Total VRAM: 124610 MiB):
Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes, VRAM: 124610 MiB
Operating systems
Linux
GGML backends
CUDA
Hardware
Dgx Spark
Models
Qwen 3.5 35B A3b Q4
Problem description & steps to reproduce
I am trying to use llama-finetune to fine tune the Qwen3.6 model, and got this error:
user@spark-1a51:~/llama.cpp/build/bin$ ./llama-finetune --file /home/user/training_data/content.txt -ngl 999 --model /home/user/models/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf -c 18000 -b 512 -ub 512
ggml_cuda_init: found 1 CUDA devices (Total VRAM: 124610 MiB):
Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes, VRAM: 124610 MiB
main: force disabling memory mapping because it would result in-read-only pointers to the weights
main: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD
main: force changing v cache type to f32 due to a lack of f16 support for OUT_PROD
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 21853 MiB of device memory vs. 62807 MiB of free device memory
llama_params_fit_impl: will leave 40954 >= 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 0.34 seconds
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GB10) (000f:01:00.0) - 62870 MiB free
llama_model_loader: loaded meta data with 54 key-value pairs and 733 tensors from /home/user/models/Qwen3.6-35B-A3B-UD-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 = qwen35moe
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Qwen3.6-35B-A3B
llama_model_loader: - kv 6: general.basename str = Qwen3.6-35B-A3B
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 35B-A3B
llama_model_loader: - kv 9: general.license str = apache-2.0
llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.6-3...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.base_model.count u32 = 1
llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.6 35B A3B
llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.6-3...
llama_model_loader: - kv 16: general.tags arr[str,3] = ["qwen3_5_moe", "qwen", "image-text-t...
llama_model_loader: - kv 17: qwen35moe.block_count u32 = 40
llama_model_loader: - kv 18: qwen35moe.context_length u32 = 262144
llama_model_loader: - kv 19: qwen35moe.embedding_length u32 = 2048
llama_model_loader: - kv 20: qwen35moe.attention.head_count u32 = 16
llama_model_loader: - kv 21: qwen35moe.attention.head_count_kv u32 = 2
llama_model_loader: - kv 22: qwen35moe.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
llama_model_loader: - kv 23: qwen35moe.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 24: qwen35moe.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 25: qwen35moe.expert_count u32 = 256
llama_model_loader: - kv 26: qwen35moe.expert_used_count u32 = 8
llama_model_loader: - kv 27: qwen35moe.attention.key_length u32 = 256
llama_model_loader: - kv 28: qwen35moe.attention.value_length u32 = 256
llama_model_loader: - kv 29: qwen35moe.expert_feed_forward_length u32 = 512
llama_model_loader: - kv 30: qwen35moe.expert_shared_feed_forward_length u32 = 512
llama_model_loader: - kv 31: qwen35moe.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 32: qwen35moe.ssm.state_size u32 = 128
llama_model_loader: - kv 33: qwen35moe.ssm.group_count u32 = 16
llama_model_loader: - kv 34: qwen35moe.ssm.time_step_rank u32 = 32
llama_model_loader: - kv 35: qwen35moe.ssm.inner_size u32 = 4096
llama_model_loader: - kv 36: qwen35moe.full_attention_interval u32 = 4
llama_model_loader: - kv 37: qwen35moe.rope.dimension_count u32 = 64
llama_model_loader: - kv 38: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 39: tokenizer.ggml.pre str = qwen35
llama_model_loader: - kv 40: tokenizer.ggml.tokens arr[str,248320] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 41: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 42: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 43: tokenizer.ggml.eos_token_id u32 = 248046
llama_model_loader: - kv 44: tokenizer.ggml.padding_token_id u32 = 248055
llama_model_loader: - kv 45: tokenizer.ggml.bos_token_id u32 = 248044
llama_model_loader: - kv 46: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 47: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 48: general.quantization_version u32 = 2
llama_model_loader: - kv 49: general.file_type u32 = 15
llama_model_loader: - kv 50: quantize.imatrix.file str = Qwen3.6-35B-A3B-GGUF/imatrix_unsloth....
llama_model_loader: - kv 51: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.6-35B-A3B.txt
llama_model_loader: - kv 52: quantize.imatrix.entries_count u32 = 510
llama_model_loader: - kv 53: quantize.imatrix.chunks_count u32 = 76
llama_model_loader: - type f32: 361 tensors
llama_model_loader: - type q8_0: 251 tensors
llama_model_loader: - type q4_K: 80 tensors
llama_model_loader: - type q5_K: 37 tensors
llama_model_loader: - type q6_K: 4 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 20.60 GiB (5.11 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 248044 ('<|endoftext|>')
load: - 248046 ('<|im_end|>')
load: - 248063 ('<|fim_pad|>')
load: - 248064 ('<|repo_name|>')
load: - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch = qwen35moe
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 2048
print_info: n_embd_inp = 2048
print_info: n_layer = 40
print_info: n_head = 16
print_info: n_head_kv = 2
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
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 = 0
print_info: n_expert = 256
print_info: n_expert_used = 8
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 = 40
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
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 = [11, 11, 10, 0]
print_info: ssm_d_conv = 4
print_info: ssm_d_inner = 4096
print_info: ssm_d_state = 128
print_info: ssm_dt_rank = 32
print_info: ssm_n_group = 16
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 35B.A3B
print_info: model params = 34.66 B
print_info: general.name = Qwen3.6-35B-A3B
print_info: vocab type = BPE
print_info: n_vocab = 248320
print_info: n_merges = 247587
print_info: BOS token = 248044 '<|endoftext|>'
print_info: EOS token = 248046 '<|im_end|>'
print_info: EOT token = 248046 '<|im_end|>'
print_info: PAD token = 248055 '<|vision_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 248060 '<|fim_prefix|>'
print_info: FIM SUF token = 248062 '<|fim_suffix|>'
print_info: FIM MID token = 248061 '<|fim_middle|>'
print_info: FIM PAD token = 248063 '<|fim_pad|>'
print_info: FIM REP token = 248064 '<|repo_name|>'
print_info: FIM SEP token = 248065 '<|file_sep|>'
print_info: EOG token = 248044 '<|endoftext|>'
print_info: EOG token = 248046 '<|im_end|>'
print_info: EOG token = 248063 '<|fim_pad|>'
print_info: EOG token = 248064 '<|repo_name|>'
print_info: EOG token = 248065 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 39 repeating layers to GPU
load_tensors: offloaded 41/41 layers to GPU
load_tensors: CUDA0 model buffer size = 20583.34 MiB
load_tensors: CUDA_Host model buffer size = 515.31 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 = 18176
llama_context: n_ctx_seq = 18176
llama_context: n_batch = 512
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (18176) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.95 MiB
llama_kv_cache: CUDA0 KV buffer size = 710.00 MiB
llama_kv_cache: size = 710.00 MiB ( 18176 cells, 10 layers, 1/1 seqs), K (f32): 355.00 MiB, V (f32): 355.00 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_memory_recurrent: CUDA0 RS buffer size = 62.81 MiB
llama_memory_recurrent: size = 62.81 MiB ( 1 cells, 40 layers, 1 seqs), R (f32): 2.81 MiB, S (f32): 60.00 MiB
sched_reserve: reserving ...
sched_reserve: Flash Attention was auto, set to enabled
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: CUDA0 compute buffer size = 497.00 MiB
sched_reserve: CUDA_Host compute buffer size = 43.52 MiB
sched_reserve: graph nodes = 3749
sched_reserve: graph splits = 2
sched_reserve: reserve took 42.06 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 20 (n_threads_batch = 20) / 20 | CUDA : ARCHS = 1210 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | BLACKWELL_NATIVE_FP4 = 1 | CPU : NEON = 1 | ARM_FMA = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
ggml_aligned_malloc: insufficient memory (attempted to allocate 17592186044415.76 MB)
ggml_backend_cpu_buffer_type_alloc_buffer: failed to allocate buffer of size 18446744073709303808
alloc_tensor_range: failed to allocate CPU buffer of size 18446744073709303808
[New LWP 328830]
[New LWP 328829]
[New LWP 328827]
This GDB supports auto-downloading debuginfo from the following URLs:
https://debuginfod.ubuntu.com
Enable debuginfod for this session? (y or [n]) [answered N; input not from terminal]
Debuginfod has been disabled.
To make this setting permanent, add 'set debuginfod enabled off' to .gdbinit.
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/aarch64-linux-gnu/libthread_db.so.1".
0x0000f825a63e7b74 in __GI___wait4 (pid=328856, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
warning: 30 ../sysdeps/unix/sysv/linux/wait4.c: No such file or directory
#0 0x0000f825a63e7b74 in __GI___wait4 (pid=328856, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
30 in ../sysdeps/unix/sysv/linux/wait4.c
#1 0x0000f825a6875bac in ggml_print_backtrace () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#2 0x0000f825a688816c in ggml_uncaught_exception() () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#3 0x0000f825a666a570 in ?? () from /lib/aarch64-linux-gnu/libstdc++.so.6
#4 0x0000f825a6660e04 [PAC] in std::terminate() () from /lib/aarch64-linux-gnu/libstdc++.so.6
#5 0x0000f825a666a908 [PAC] in __cxa_throw () from /lib/aarch64-linux-gnu/libstdc++.so.6
#6 0x0000f825a666b030 [PAC] in operator new(unsigned long) () from /lib/aarch64-linux-gnu/libstdc++.so.6
#7 0x0000f825a6895958 [PAC] in std::vector<long, std::allocator >::_M_default_append(unsigned long) () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#8 0x0000f825a6893050 in ggml_opt_dataset_init () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#9 0x0000afe927bc81a0 in common_opt_dataset_init(llama_context*, std::vector<int, std::allocator > const&, long) ()
#10 0x0000afe927b2e16c in main ()
[Inferior 1 (process 328826) detached]
terminate called after throwing an instance of 'std::bad_alloc'
what(): std::bad_alloc
Aborted (core dumped)
**ggml_aligned_malloc: insufficient memory (attempted to allocate 17592186044415.76 MB)
I think "attempted to allocate 17592186044415.76 MB", this part is not make sense to me, as I am using Qwen3.6 35B A3b with 4 bit Quantization. I am not sure if anyone else having this error?**
First Bad Commit
No response
Relevant log output
user@spark-1a51:~/llama.cpp/build/bin$ ./llama-finetune --file /home/user/training_data/content.txt -ngl 999 --model /home/user/models/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf -c 18000 -b 512 -ub 512
ggml_cuda_init: found 1 CUDA devices (Total VRAM: 124610 MiB):
Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes, VRAM: 124610 MiB
main: force disabling memory mapping because it would result in-read-only pointers to the weights
main: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD
main: force changing v cache type to f32 due to a lack of f16 support for OUT_PROD
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 21853 MiB of device memory vs. 62807 MiB of free device memory
llama_params_fit_impl: will leave 40954 >= 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 0.34 seconds
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GB10) (000f:01:00.0) - 62870 MiB free
llama_model_loader: loaded meta data with 54 key-value pairs and 733 tensors from /home/user/models/Qwen3.6-35B-A3B-UD-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 = qwen35moe
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Qwen3.6-35B-A3B
llama_model_loader: - kv 6: general.basename str = Qwen3.6-35B-A3B
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 35B-A3B
llama_model_loader: - kv 9: general.license str = apache-2.0
llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.6-3...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.base_model.count u32 = 1
llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.6 35B A3B
llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.6-3...
llama_model_loader: - kv 16: general.tags arr[str,3] = ["qwen3_5_moe", "qwen", "image-text-t...
llama_model_loader: - kv 17: qwen35moe.block_count u32 = 40
llama_model_loader: - kv 18: qwen35moe.context_length u32 = 262144
llama_model_loader: - kv 19: qwen35moe.embedding_length u32 = 2048
llama_model_loader: - kv 20: qwen35moe.attention.head_count u32 = 16
llama_model_loader: - kv 21: qwen35moe.attention.head_count_kv u32 = 2
llama_model_loader: - kv 22: qwen35moe.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
llama_model_loader: - kv 23: qwen35moe.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 24: qwen35moe.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 25: qwen35moe.expert_count u32 = 256
llama_model_loader: - kv 26: qwen35moe.expert_used_count u32 = 8
llama_model_loader: - kv 27: qwen35moe.attention.key_length u32 = 256
llama_model_loader: - kv 28: qwen35moe.attention.value_length u32 = 256
llama_model_loader: - kv 29: qwen35moe.expert_feed_forward_length u32 = 512
llama_model_loader: - kv 30: qwen35moe.expert_shared_feed_forward_length u32 = 512
llama_model_loader: - kv 31: qwen35moe.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 32: qwen35moe.ssm.state_size u32 = 128
llama_model_loader: - kv 33: qwen35moe.ssm.group_count u32 = 16
llama_model_loader: - kv 34: qwen35moe.ssm.time_step_rank u32 = 32
llama_model_loader: - kv 35: qwen35moe.ssm.inner_size u32 = 4096
llama_model_loader: - kv 36: qwen35moe.full_attention_interval u32 = 4
llama_model_loader: - kv 37: qwen35moe.rope.dimension_count u32 = 64
llama_model_loader: - kv 38: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 39: tokenizer.ggml.pre str = qwen35
llama_model_loader: - kv 40: tokenizer.ggml.tokens arr[str,248320] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 41: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 42: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 43: tokenizer.ggml.eos_token_id u32 = 248046
llama_model_loader: - kv 44: tokenizer.ggml.padding_token_id u32 = 248055
llama_model_loader: - kv 45: tokenizer.ggml.bos_token_id u32 = 248044
llama_model_loader: - kv 46: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 47: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 48: general.quantization_version u32 = 2
llama_model_loader: - kv 49: general.file_type u32 = 15
llama_model_loader: - kv 50: quantize.imatrix.file str = Qwen3.6-35B-A3B-GGUF/imatrix_unsloth....
llama_model_loader: - kv 51: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.6-35B-A3B.txt
llama_model_loader: - kv 52: quantize.imatrix.entries_count u32 = 510
llama_model_loader: - kv 53: quantize.imatrix.chunks_count u32 = 76
llama_model_loader: - type f32: 361 tensors
llama_model_loader: - type q8_0: 251 tensors
llama_model_loader: - type q4_K: 80 tensors
llama_model_loader: - type q5_K: 37 tensors
llama_model_loader: - type q6_K: 4 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 20.60 GiB (5.11 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 248044 ('<|endoftext|>')
load: - 248046 ('<|im_end|>')
load: - 248063 ('<|fim_pad|>')
load: - 248064 ('<|repo_name|>')
load: - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch = qwen35moe
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 2048
print_info: n_embd_inp = 2048
print_info: n_layer = 40
print_info: n_head = 16
print_info: n_head_kv = 2
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
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 = 0
print_info: n_expert = 256
print_info: n_expert_used = 8
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 = 40
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
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 = [11, 11, 10, 0]
print_info: ssm_d_conv = 4
print_info: ssm_d_inner = 4096
print_info: ssm_d_state = 128
print_info: ssm_dt_rank = 32
print_info: ssm_n_group = 16
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 35B.A3B
print_info: model params = 34.66 B
print_info: general.name = Qwen3.6-35B-A3B
print_info: vocab type = BPE
print_info: n_vocab = 248320
print_info: n_merges = 247587
print_info: BOS token = 248044 '<|endoftext|>'
print_info: EOS token = 248046 '<|im_end|>'
print_info: EOT token = 248046 '<|im_end|>'
print_info: PAD token = 248055 '<|vision_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 248060 '<|fim_prefix|>'
print_info: FIM SUF token = 248062 '<|fim_suffix|>'
print_info: FIM MID token = 248061 '<|fim_middle|>'
print_info: FIM PAD token = 248063 '<|fim_pad|>'
print_info: FIM REP token = 248064 '<|repo_name|>'
print_info: FIM SEP token = 248065 '<|file_sep|>'
print_info: EOG token = 248044 '<|endoftext|>'
print_info: EOG token = 248046 '<|im_end|>'
print_info: EOG token = 248063 '<|fim_pad|>'
print_info: EOG token = 248064 '<|repo_name|>'
print_info: EOG token = 248065 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 39 repeating layers to GPU
load_tensors: offloaded 41/41 layers to GPU
load_tensors: CUDA0 model buffer size = 20583.34 MiB
load_tensors: CUDA_Host model buffer size = 515.31 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 = 18176
llama_context: n_ctx_seq = 18176
llama_context: n_batch = 512
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (18176) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.95 MiB
llama_kv_cache: CUDA0 KV buffer size = 710.00 MiB
llama_kv_cache: size = 710.00 MiB ( 18176 cells, 10 layers, 1/1 seqs), K (f32): 355.00 MiB, V (f32): 355.00 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_memory_recurrent: CUDA0 RS buffer size = 62.81 MiB
llama_memory_recurrent: size = 62.81 MiB ( 1 cells, 40 layers, 1 seqs), R (f32): 2.81 MiB, S (f32): 60.00 MiB
sched_reserve: reserving ...
sched_reserve: Flash Attention was auto, set to enabled
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: CUDA0 compute buffer size = 497.00 MiB
sched_reserve: CUDA_Host compute buffer size = 43.52 MiB
sched_reserve: graph nodes = 3749
sched_reserve: graph splits = 2
sched_reserve: reserve took 42.06 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 20 (n_threads_batch = 20) / 20 | CUDA : ARCHS = 1210 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | BLACKWELL_NATIVE_FP4 = 1 | CPU : NEON = 1 | ARM_FMA = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
ggml_aligned_malloc: insufficient memory (attempted to allocate 17592186044415.76 MB)
ggml_backend_cpu_buffer_type_alloc_buffer: failed to allocate buffer of size 18446744073709303808
alloc_tensor_range: failed to allocate CPU buffer of size 18446744073709303808
[New LWP 328830]
[New LWP 328829]
[New LWP 328827]
This GDB supports auto-downloading debuginfo from the following URLs:
https://debuginfod.ubuntu.com
Enable debuginfod for this session? (y or [n]) [answered N; input not from terminal]
Debuginfod has been disabled.
To make this setting permanent, add 'set debuginfod enabled off' to .gdbinit.
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/aarch64-linux-gnu/libthread_db.so.1".
0x0000f825a63e7b74 in __GI___wait4 (pid=328856, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
warning: 30 ../sysdeps/unix/sysv/linux/wait4.c: No such file or directory
#0 0x0000f825a63e7b74 in __GI___wait4 (pid=328856, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
30 in ../sysdeps/unix/sysv/linux/wait4.c
#1 0x0000f825a6875bac in ggml_print_backtrace () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#2 0x0000f825a688816c in ggml_uncaught_exception() () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#3 0x0000f825a666a570 in ?? () from /lib/aarch64-linux-gnu/libstdc++.so.6
#4 0x0000f825a6660e04 [PAC] in std::terminate() () from /lib/aarch64-linux-gnu/libstdc++.so.6
#5 0x0000f825a666a908 [PAC] in __cxa_throw () from /lib/aarch64-linux-gnu/libstdc++.so.6
#6 0x0000f825a666b030 [PAC] in operator new(unsigned long) () from /lib/aarch64-linux-gnu/libstdc++.so.6
#7 0x0000f825a6895958 [PAC] in std::vector<long, std::allocator >::_M_default_append(unsigned long) () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#8 0x0000f825a6893050 in ggml_opt_dataset_init () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#9 0x0000afe927bc81a0 in common_opt_dataset_init(llama_context*, std::vector<int, std::allocator > const&, long) ()
#10 0x0000afe927b2e16c in main ()
[Inferior 1 (process 328826) detached]
terminate called after throwing an instance of 'std::bad_alloc'
what(): std::bad_alloc
Aborted (core dumped)
Name and Version
./llama-cli -v
ggml_cuda_init: found 1 CUDA devices (Total VRAM: 124610 MiB):
Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes, VRAM: 124610 MiB
Operating systems
Linux
GGML backends
CUDA
Hardware
Dgx Spark
Models
Qwen 3.5 35B A3b Q4
Problem description & steps to reproduce
I am trying to use llama-finetune to fine tune the Qwen3.6 model, and got this error:
user@spark-1a51:~/llama.cpp/build/bin$ ./llama-finetune --file /home/user/training_data/content.txt -ngl 999 --model /home/user/models/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf -c 18000 -b 512 -ub 512
ggml_cuda_init: found 1 CUDA devices (Total VRAM: 124610 MiB):
Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes, VRAM: 124610 MiB
main: force disabling memory mapping because it would result in-read-only pointers to the weights
main: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD
main: force changing v cache type to f32 due to a lack of f16 support for OUT_PROD
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 21853 MiB of device memory vs. 62807 MiB of free device memory
llama_params_fit_impl: will leave 40954 >= 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 0.34 seconds
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GB10) (000f:01:00.0) - 62870 MiB free
llama_model_loader: loaded meta data with 54 key-value pairs and 733 tensors from /home/user/models/Qwen3.6-35B-A3B-UD-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 = qwen35moe
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Qwen3.6-35B-A3B
llama_model_loader: - kv 6: general.basename str = Qwen3.6-35B-A3B
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 35B-A3B
llama_model_loader: - kv 9: general.license str = apache-2.0
llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.6-3...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.base_model.count u32 = 1
llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.6 35B A3B
llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.6-3...
llama_model_loader: - kv 16: general.tags arr[str,3] = ["qwen3_5_moe", "qwen", "image-text-t...
llama_model_loader: - kv 17: qwen35moe.block_count u32 = 40
llama_model_loader: - kv 18: qwen35moe.context_length u32 = 262144
llama_model_loader: - kv 19: qwen35moe.embedding_length u32 = 2048
llama_model_loader: - kv 20: qwen35moe.attention.head_count u32 = 16
llama_model_loader: - kv 21: qwen35moe.attention.head_count_kv u32 = 2
llama_model_loader: - kv 22: qwen35moe.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
llama_model_loader: - kv 23: qwen35moe.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 24: qwen35moe.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 25: qwen35moe.expert_count u32 = 256
llama_model_loader: - kv 26: qwen35moe.expert_used_count u32 = 8
llama_model_loader: - kv 27: qwen35moe.attention.key_length u32 = 256
llama_model_loader: - kv 28: qwen35moe.attention.value_length u32 = 256
llama_model_loader: - kv 29: qwen35moe.expert_feed_forward_length u32 = 512
llama_model_loader: - kv 30: qwen35moe.expert_shared_feed_forward_length u32 = 512
llama_model_loader: - kv 31: qwen35moe.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 32: qwen35moe.ssm.state_size u32 = 128
llama_model_loader: - kv 33: qwen35moe.ssm.group_count u32 = 16
llama_model_loader: - kv 34: qwen35moe.ssm.time_step_rank u32 = 32
llama_model_loader: - kv 35: qwen35moe.ssm.inner_size u32 = 4096
llama_model_loader: - kv 36: qwen35moe.full_attention_interval u32 = 4
llama_model_loader: - kv 37: qwen35moe.rope.dimension_count u32 = 64
llama_model_loader: - kv 38: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 39: tokenizer.ggml.pre str = qwen35
llama_model_loader: - kv 40: tokenizer.ggml.tokens arr[str,248320] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 41: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 42: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 43: tokenizer.ggml.eos_token_id u32 = 248046
llama_model_loader: - kv 44: tokenizer.ggml.padding_token_id u32 = 248055
llama_model_loader: - kv 45: tokenizer.ggml.bos_token_id u32 = 248044
llama_model_loader: - kv 46: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 47: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 48: general.quantization_version u32 = 2
llama_model_loader: - kv 49: general.file_type u32 = 15
llama_model_loader: - kv 50: quantize.imatrix.file str = Qwen3.6-35B-A3B-GGUF/imatrix_unsloth....
llama_model_loader: - kv 51: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.6-35B-A3B.txt
llama_model_loader: - kv 52: quantize.imatrix.entries_count u32 = 510
llama_model_loader: - kv 53: quantize.imatrix.chunks_count u32 = 76
llama_model_loader: - type f32: 361 tensors
llama_model_loader: - type q8_0: 251 tensors
llama_model_loader: - type q4_K: 80 tensors
llama_model_loader: - type q5_K: 37 tensors
llama_model_loader: - type q6_K: 4 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 20.60 GiB (5.11 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 248044 ('<|endoftext|>')
load: - 248046 ('<|im_end|>')
load: - 248063 ('<|fim_pad|>')
load: - 248064 ('<|repo_name|>')
load: - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch = qwen35moe
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 2048
print_info: n_embd_inp = 2048
print_info: n_layer = 40
print_info: n_head = 16
print_info: n_head_kv = 2
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
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 = 0
print_info: n_expert = 256
print_info: n_expert_used = 8
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 = 40
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
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 = [11, 11, 10, 0]
print_info: ssm_d_conv = 4
print_info: ssm_d_inner = 4096
print_info: ssm_d_state = 128
print_info: ssm_dt_rank = 32
print_info: ssm_n_group = 16
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 35B.A3B
print_info: model params = 34.66 B
print_info: general.name = Qwen3.6-35B-A3B
print_info: vocab type = BPE
print_info: n_vocab = 248320
print_info: n_merges = 247587
print_info: BOS token = 248044 '<|endoftext|>'
print_info: EOS token = 248046 '<|im_end|>'
print_info: EOT token = 248046 '<|im_end|>'
print_info: PAD token = 248055 '<|vision_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 248060 '<|fim_prefix|>'
print_info: FIM SUF token = 248062 '<|fim_suffix|>'
print_info: FIM MID token = 248061 '<|fim_middle|>'
print_info: FIM PAD token = 248063 '<|fim_pad|>'
print_info: FIM REP token = 248064 '<|repo_name|>'
print_info: FIM SEP token = 248065 '<|file_sep|>'
print_info: EOG token = 248044 '<|endoftext|>'
print_info: EOG token = 248046 '<|im_end|>'
print_info: EOG token = 248063 '<|fim_pad|>'
print_info: EOG token = 248064 '<|repo_name|>'
print_info: EOG token = 248065 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 39 repeating layers to GPU
load_tensors: offloaded 41/41 layers to GPU
load_tensors: CUDA0 model buffer size = 20583.34 MiB
load_tensors: CUDA_Host model buffer size = 515.31 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 = 18176
llama_context: n_ctx_seq = 18176
llama_context: n_batch = 512
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (18176) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.95 MiB
llama_kv_cache: CUDA0 KV buffer size = 710.00 MiB
llama_kv_cache: size = 710.00 MiB ( 18176 cells, 10 layers, 1/1 seqs), K (f32): 355.00 MiB, V (f32): 355.00 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_memory_recurrent: CUDA0 RS buffer size = 62.81 MiB
llama_memory_recurrent: size = 62.81 MiB ( 1 cells, 40 layers, 1 seqs), R (f32): 2.81 MiB, S (f32): 60.00 MiB
sched_reserve: reserving ...
sched_reserve: Flash Attention was auto, set to enabled
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: CUDA0 compute buffer size = 497.00 MiB
sched_reserve: CUDA_Host compute buffer size = 43.52 MiB
sched_reserve: graph nodes = 3749
sched_reserve: graph splits = 2
sched_reserve: reserve took 42.06 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 20 (n_threads_batch = 20) / 20 | CUDA : ARCHS = 1210 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | BLACKWELL_NATIVE_FP4 = 1 | CPU : NEON = 1 | ARM_FMA = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
ggml_aligned_malloc: insufficient memory (attempted to allocate 17592186044415.76 MB)
ggml_backend_cpu_buffer_type_alloc_buffer: failed to allocate buffer of size 18446744073709303808
alloc_tensor_range: failed to allocate CPU buffer of size 18446744073709303808
[New LWP 328830]
[New LWP 328829]
[New LWP 328827]
This GDB supports auto-downloading debuginfo from the following URLs:
https://debuginfod.ubuntu.com
Enable debuginfod for this session? (y or [n]) [answered N; input not from terminal]
Debuginfod has been disabled.
To make this setting permanent, add 'set debuginfod enabled off' to .gdbinit.
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/aarch64-linux-gnu/libthread_db.so.1".
0x0000f825a63e7b74 in __GI___wait4 (pid=328856, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
warning: 30 ../sysdeps/unix/sysv/linux/wait4.c: No such file or directory
#0 0x0000f825a63e7b74 in __GI___wait4 (pid=328856, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
30 in ../sysdeps/unix/sysv/linux/wait4.c
#1 0x0000f825a6875bac in ggml_print_backtrace () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#2 0x0000f825a688816c in ggml_uncaught_exception() () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#3 0x0000f825a666a570 in ?? () from /lib/aarch64-linux-gnu/libstdc++.so.6
#4 0x0000f825a6660e04 [PAC] in std::terminate() () from /lib/aarch64-linux-gnu/libstdc++.so.6
#5 0x0000f825a666a908 [PAC] in __cxa_throw () from /lib/aarch64-linux-gnu/libstdc++.so.6
#6 0x0000f825a666b030 [PAC] in operator new(unsigned long) () from /lib/aarch64-linux-gnu/libstdc++.so.6
#7 0x0000f825a6895958 [PAC] in std::vector<long, std::allocator >::_M_default_append(unsigned long) () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#8 0x0000f825a6893050 in ggml_opt_dataset_init () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#9 0x0000afe927bc81a0 in common_opt_dataset_init(llama_context*, std::vector<int, std::allocator > const&, long) ()
#10 0x0000afe927b2e16c in main ()
[Inferior 1 (process 328826) detached]
terminate called after throwing an instance of 'std::bad_alloc'
what(): std::bad_alloc
Aborted (core dumped)
**ggml_aligned_malloc: insufficient memory (attempted to allocate 17592186044415.76 MB)
I think "attempted to allocate 17592186044415.76 MB", this part is not make sense to me, as I am using Qwen3.6 35B A3b with 4 bit Quantization. I am not sure if anyone else having this error?**
First Bad Commit
No response
Relevant log output
user@spark-1a51:~/llama.cpp/build/bin$ ./llama-finetune --file /home/user/training_data/content.txt -ngl 999 --model /home/user/models/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf -c 18000 -b 512 -ub 512
ggml_cuda_init: found 1 CUDA devices (Total VRAM: 124610 MiB):
Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes, VRAM: 124610 MiB
main: force disabling memory mapping because it would result in-read-only pointers to the weights
main: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD
main: force changing v cache type to f32 due to a lack of f16 support for OUT_PROD
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 21853 MiB of device memory vs. 62807 MiB of free device memory
llama_params_fit_impl: will leave 40954 >= 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 0.34 seconds
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GB10) (000f:01:00.0) - 62870 MiB free
llama_model_loader: loaded meta data with 54 key-value pairs and 733 tensors from /home/user/models/Qwen3.6-35B-A3B-UD-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 = qwen35moe
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Qwen3.6-35B-A3B
llama_model_loader: - kv 6: general.basename str = Qwen3.6-35B-A3B
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 35B-A3B
llama_model_loader: - kv 9: general.license str = apache-2.0
llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.6-3...
llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 12: general.base_model.count u32 = 1
llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.6 35B A3B
llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.6-3...
llama_model_loader: - kv 16: general.tags arr[str,3] = ["qwen3_5_moe", "qwen", "image-text-t...
llama_model_loader: - kv 17: qwen35moe.block_count u32 = 40
llama_model_loader: - kv 18: qwen35moe.context_length u32 = 262144
llama_model_loader: - kv 19: qwen35moe.embedding_length u32 = 2048
llama_model_loader: - kv 20: qwen35moe.attention.head_count u32 = 16
llama_model_loader: - kv 21: qwen35moe.attention.head_count_kv u32 = 2
llama_model_loader: - kv 22: qwen35moe.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
llama_model_loader: - kv 23: qwen35moe.rope.freq_base f32 = 10000000.000000
llama_model_loader: - kv 24: qwen35moe.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 25: qwen35moe.expert_count u32 = 256
llama_model_loader: - kv 26: qwen35moe.expert_used_count u32 = 8
llama_model_loader: - kv 27: qwen35moe.attention.key_length u32 = 256
llama_model_loader: - kv 28: qwen35moe.attention.value_length u32 = 256
llama_model_loader: - kv 29: qwen35moe.expert_feed_forward_length u32 = 512
llama_model_loader: - kv 30: qwen35moe.expert_shared_feed_forward_length u32 = 512
llama_model_loader: - kv 31: qwen35moe.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 32: qwen35moe.ssm.state_size u32 = 128
llama_model_loader: - kv 33: qwen35moe.ssm.group_count u32 = 16
llama_model_loader: - kv 34: qwen35moe.ssm.time_step_rank u32 = 32
llama_model_loader: - kv 35: qwen35moe.ssm.inner_size u32 = 4096
llama_model_loader: - kv 36: qwen35moe.full_attention_interval u32 = 4
llama_model_loader: - kv 37: qwen35moe.rope.dimension_count u32 = 64
llama_model_loader: - kv 38: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 39: tokenizer.ggml.pre str = qwen35
llama_model_loader: - kv 40: tokenizer.ggml.tokens arr[str,248320] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 41: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 42: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 43: tokenizer.ggml.eos_token_id u32 = 248046
llama_model_loader: - kv 44: tokenizer.ggml.padding_token_id u32 = 248055
llama_model_loader: - kv 45: tokenizer.ggml.bos_token_id u32 = 248044
llama_model_loader: - kv 46: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 47: tokenizer.chat_template str = {%- set image_count = namespace(value...
llama_model_loader: - kv 48: general.quantization_version u32 = 2
llama_model_loader: - kv 49: general.file_type u32 = 15
llama_model_loader: - kv 50: quantize.imatrix.file str = Qwen3.6-35B-A3B-GGUF/imatrix_unsloth....
llama_model_loader: - kv 51: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.6-35B-A3B.txt
llama_model_loader: - kv 52: quantize.imatrix.entries_count u32 = 510
llama_model_loader: - kv 53: quantize.imatrix.chunks_count u32 = 76
llama_model_loader: - type f32: 361 tensors
llama_model_loader: - type q8_0: 251 tensors
llama_model_loader: - type q4_K: 80 tensors
llama_model_loader: - type q5_K: 37 tensors
llama_model_loader: - type q6_K: 4 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 20.60 GiB (5.11 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 248044 ('<|endoftext|>')
load: - 248046 ('<|im_end|>')
load: - 248063 ('<|fim_pad|>')
load: - 248064 ('<|repo_name|>')
load: - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch = qwen35moe
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 262144
print_info: n_embd = 2048
print_info: n_embd_inp = 2048
print_info: n_layer = 40
print_info: n_head = 16
print_info: n_head_kv = 2
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
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 = 0
print_info: n_expert = 256
print_info: n_expert_used = 8
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 = 40
print_info: rope scaling = linear
print_info: freq_base_train = 10000000.0
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 = [11, 11, 10, 0]
print_info: ssm_d_conv = 4
print_info: ssm_d_inner = 4096
print_info: ssm_d_state = 128
print_info: ssm_dt_rank = 32
print_info: ssm_n_group = 16
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 35B.A3B
print_info: model params = 34.66 B
print_info: general.name = Qwen3.6-35B-A3B
print_info: vocab type = BPE
print_info: n_vocab = 248320
print_info: n_merges = 247587
print_info: BOS token = 248044 '<|endoftext|>'
print_info: EOS token = 248046 '<|im_end|>'
print_info: EOT token = 248046 '<|im_end|>'
print_info: PAD token = 248055 '<|vision_pad|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 248060 '<|fim_prefix|>'
print_info: FIM SUF token = 248062 '<|fim_suffix|>'
print_info: FIM MID token = 248061 '<|fim_middle|>'
print_info: FIM PAD token = 248063 '<|fim_pad|>'
print_info: FIM REP token = 248064 '<|repo_name|>'
print_info: FIM SEP token = 248065 '<|file_sep|>'
print_info: EOG token = 248044 '<|endoftext|>'
print_info: EOG token = 248046 '<|im_end|>'
print_info: EOG token = 248063 '<|fim_pad|>'
print_info: EOG token = 248064 '<|repo_name|>'
print_info: EOG token = 248065 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 39 repeating layers to GPU
load_tensors: offloaded 41/41 layers to GPU
load_tensors: CUDA0 model buffer size = 20583.34 MiB
load_tensors: CUDA_Host model buffer size = 515.31 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 = 18176
llama_context: n_ctx_seq = 18176
llama_context: n_batch = 512
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 10000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (18176) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.95 MiB
llama_kv_cache: CUDA0 KV buffer size = 710.00 MiB
llama_kv_cache: size = 710.00 MiB ( 18176 cells, 10 layers, 1/1 seqs), K (f32): 355.00 MiB, V (f32): 355.00 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
llama_memory_recurrent: CUDA0 RS buffer size = 62.81 MiB
llama_memory_recurrent: size = 62.81 MiB ( 1 cells, 40 layers, 1 seqs), R (f32): 2.81 MiB, S (f32): 60.00 MiB
sched_reserve: reserving ...
sched_reserve: Flash Attention was auto, set to enabled
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: CUDA0 compute buffer size = 497.00 MiB
sched_reserve: CUDA_Host compute buffer size = 43.52 MiB
sched_reserve: graph nodes = 3749
sched_reserve: graph splits = 2
sched_reserve: reserve took 42.06 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
system_info: n_threads = 20 (n_threads_batch = 20) / 20 | CUDA : ARCHS = 1210 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | BLACKWELL_NATIVE_FP4 = 1 | CPU : NEON = 1 | ARM_FMA = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
ggml_aligned_malloc: insufficient memory (attempted to allocate 17592186044415.76 MB)
ggml_backend_cpu_buffer_type_alloc_buffer: failed to allocate buffer of size 18446744073709303808
alloc_tensor_range: failed to allocate CPU buffer of size 18446744073709303808
[New LWP 328830]
[New LWP 328829]
[New LWP 328827]
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Enable debuginfod for this session? (y or [n]) [answered N; input not from terminal]
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To make this setting permanent, add 'set debuginfod enabled off' to .gdbinit.
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/aarch64-linux-gnu/libthread_db.so.1".
0x0000f825a63e7b74 in __GI___wait4 (pid=328856, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
warning: 30 ../sysdeps/unix/sysv/linux/wait4.c: No such file or directory
#0 0x0000f825a63e7b74 in __GI___wait4 (pid=328856, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
30 in ../sysdeps/unix/sysv/linux/wait4.c
#1 0x0000f825a6875bac in ggml_print_backtrace () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#2 0x0000f825a688816c in ggml_uncaught_exception() () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#3 0x0000f825a666a570 in ?? () from /lib/aarch64-linux-gnu/libstdc++.so.6
#4 0x0000f825a6660e04 [PAC] in std::terminate() () from /lib/aarch64-linux-gnu/libstdc++.so.6
#5 0x0000f825a666a908 [PAC] in __cxa_throw () from /lib/aarch64-linux-gnu/libstdc++.so.6
#6 0x0000f825a666b030 [PAC] in operator new(unsigned long) () from /lib/aarch64-linux-gnu/libstdc++.so.6
#7 0x0000f825a6895958 [PAC] in std::vector<long, std::allocator >::_M_default_append(unsigned long) () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#8 0x0000f825a6893050 in ggml_opt_dataset_init () from /home/user/llama.cpp/build/bin/libggml-base.so.0
#9 0x0000afe927bc81a0 in common_opt_dataset_init(llama_context*, std::vector<int, std::allocator > const&, long) ()
#10 0x0000afe927b2e16c in main ()
[Inferior 1 (process 328826) detached]
terminate called after throwing an instance of 'std::bad_alloc'
what(): std::bad_alloc
Aborted (core dumped)