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ik_llama.cpp/github-data/issues/596 - Bug_ Lastest commit broke llama-cli on Windows - mmq.cuh_107_ fatal err.md
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### 🐛 [#596](https://github.com/ikawrakow/ik_llama.cpp/issues/596) - Bug: Lastest commit broke llama-cli on Windows - mmq.cuh:107: fatal error
| **Author** | `Thireus` |
| :--- | :--- |
| **State** | ❌ **Closed** |
| **Created** | 2025-07-10 |
| **Updated** | 2025-07-13 |
---
#### Description
### What happened?
Some changes made in commit 283753cabcabd30eb2cfb93739d9c1679200bf1f are causing llama-cli to crash. Which wasn't happening before this commit.
```
CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0,2,1 llama-cli -m DeepSeek-R1-0528-THIREUS.gguf -mla 3 -fa -amb 1024 -fmoe -ctk f16 -c 16384 -ngl 99 -ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" -ot "blk\.(7|8|9)\.ffn_.*=CUDA1" -ot "blk\.(10|11|12)\.ffn_.*=CUDA2" -ot exps=CPU -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 36 --main-gpu 0 -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n'
```
### Name and Version
https://github.com/ikawrakow/ik_llama.cpp/commit/283753cabcabd30eb2cfb93739d9c1679200bf1f#diff-f591a6af9587b282030c7387e32a880973e68370ee6ee3918bd5cd008d1fb89d
### What operating system are you seeing the problem on?
Windows
### Relevant log output
```shell
...
Tensor blk.60.ffn_gate_exps.weight buffer type overriden to CPU
Tensor blk.60.ffn_down_exps.weight buffer type overriden to CPU
Tensor blk.60.ffn_up_exps.weight buffer type overriden to CPU
llm_load_tensors: offloading 61 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 62/62 layers to GPU
llm_load_tensors: CPU buffer size = 210434.50 MiB
llm_load_tensors: CUDA_Host buffer size = 938.98 MiB
llm_load_tensors: CUDA0 buffer size = 17242.99 MiB
llm_load_tensors: CUDA1 buffer size = 12195.88 MiB
llm_load_tensors: CUDA2 buffer size = 14471.99 MiB
....................................................................................................
llama_new_context_with_model: n_ctx = 16384
llama_new_context_with_model: n_batch = 4096
llama_new_context_with_model: n_ubatch = 4096
llama_new_context_with_model: flash_attn = 1
llama_new_context_with_model: mla_attn = 3
llama_new_context_with_model: attn_max_b = 1024
llama_new_context_with_model: fused_moe = 1
llama_new_context_with_model: ser = -1, 0
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 0.025
llama_kv_cache_init: CUDA0 KV buffer size = 450.00 MiB
llama_kv_cache_init: CUDA1 KV buffer size = 342.00 MiB
llama_kv_cache_init: CUDA2 KV buffer size = 306.00 MiB
llama_new_context_with_model: KV self size = 1098.00 MiB, c^KV (f16): 1098.00 MiB, kv^T: not used
llama_new_context_with_model: CUDA_Host output buffer size = 0.49 MiB
llama_new_context_with_model: pipeline parallelism enabled (n_copies=1)
llama_new_context_with_model: CUDA0 compute buffer size = 4496.02 MiB
llama_new_context_with_model: CUDA1 compute buffer size = 3152.02 MiB
llama_new_context_with_model: CUDA2 compute buffer size = 3152.03 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 368.05 MiB
llama_new_context_with_model: graph nodes = 4202
llama_new_context_with_model: graph splits = 178
system_info: n_threads = 36 / 36 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
sampling:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
xtc_probability = 0.000, xtc_threshold = 1.000, top_n_sigma = 0.000
sampling order:
CFG -> Penalties -> dry -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> xtc -> top_n_sigma -> temperature
generate: n_ctx = 16384, n_batch = 4096, n_predict = -1, n_keep = 1
<|begin?of?sentence|><|User|>What is the solution of x+5=-2?<|Assistant|><think>
D:\a\ik_llama.cpp\ik_llama.cpp\ggml\src\ggml-cuda\mmq.cuh:107: fatal error
```
---
#### 💬 Conversation
👤 **ikawrakow** commented the **2025-07-10** at **07:52:57**:<br>
What is the quantization mix being used?
---
👤 **Thireus** commented the **2025-07-11** at **18:24:49**:<br>
This one: https://github.com/Thireus/GGUF-Tool-Suite/blob/main/recipe_examples/DeepSeek-R1-0528.THIREUS-3.1027bpw-3.3372ppl.242GB-GGUF_11GB-GPU_231GB-CPU.3c88ec6_adc8101.recipe
Just compiled the latest commit and still happening:
`D:\a\ik_llama.cpp\ik_llama.cpp\ggml\src\ggml-cuda\mmq.cuh:107: fatal error`
Edit: Link edited.
---
👤 **ikawrakow** commented the **2025-07-12** at **06:47:34**:<br>
The link you posted gives 404. But even if it worked, we know that the HF tensor viewer does not work when the model contains `ik_llama.cpp` specific quantization types.
How hard is it to to post the portion of the log that tells us how many tensors there are from what type?
---
👤 **Thireus** commented the **2025-07-12** at **07:19:00**:<br>
I'm not sure what you mean by "HF tensor viewer", I'm not using it.
Sorry, didn't realise I had missed that portion of the logs, here is another one:
```
CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0,2,1 ~/ik_llama-main-b3912-18b0375-bin-win-cuda-12.8-x64-avx512/llama-cli -m model.gguf -mla 3 -fa \
-amb 1024 \
-fmoe \
-ctk f16 \
-c 16384 \
-ngl 99 \
-ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" -ot "blk\.(7|8|9)\.ffn_.*=CUDA1" -ot "blk\.(10|11|12)\.ffn_.*=CUDA2" \
-ot exps=CPU \
-b 4096 -ub 4096 \
--warmup-batch \
--no-mmap \
--threads 36 \
--main-gpu 0 \
-p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 3 CUDA devices:
Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes
Device 1: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
Device 2: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
Log start
main: build = 1 (18b0375)
main: built with MSVC 19.44.35209.0 for
main: seed = 1752263940
llama_model_loader: Max stdio successfully set to 2048
llama_model_loader: additional 1147 GGUFs metadata loaded.
llama_model_loader: loaded meta data with 49 key-value pairs and 1147 tensors from DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.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 R1 0528
llama_model_loader: - kv 3: general.version str = 0528
llama_model_loader: - kv 4: general.basename str = DeepSeek-R1
llama_model_loader: - kv 5: general.size_label str = 256x21B
llama_model_loader: - kv 6: general.license str = mit
llama_model_loader: - kv 7: deepseek2.block_count u32 = 61
llama_model_loader: - kv 8: deepseek2.context_length u32 = 163840
llama_model_loader: - kv 9: deepseek2.embedding_length u32 = 7168
llama_model_loader: - kv 10: deepseek2.feed_forward_length u32 = 18432
llama_model_loader: - kv 11: deepseek2.attention.head_count u32 = 128
llama_model_loader: - kv 12: deepseek2.attention.head_count_kv u32 = 128
llama_model_loader: - kv 13: deepseek2.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 14: deepseek2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 15: deepseek2.expert_used_count u32 = 8
llama_model_loader: - kv 16: general.file_type u32 = 32
llama_model_loader: - kv 17: deepseek2.leading_dense_block_count u32 = 3
llama_model_loader: - kv 18: deepseek2.vocab_size u32 = 129280
llama_model_loader: - kv 19: deepseek2.attention.q_lora_rank u32 = 1536
llama_model_loader: - kv 20: deepseek2.attention.kv_lora_rank u32 = 512
llama_model_loader: - kv 21: deepseek2.attention.key_length u32 = 192
llama_model_loader: - kv 22: deepseek2.attention.value_length u32 = 128
llama_model_loader: - kv 23: deepseek2.expert_feed_forward_length u32 = 2048
llama_model_loader: - kv 24: deepseek2.expert_count u32 = 256
llama_model_loader: - kv 25: deepseek2.expert_shared_count u32 = 1
llama_model_loader: - kv 26: deepseek2.expert_weights_scale f32 = 2.500000
llama_model_loader: - kv 27: deepseek2.expert_weights_norm bool = true
llama_model_loader: - kv 28: deepseek2.expert_gating_func u32 = 2
llama_model_loader: - kv 29: deepseek2.rope.dimension_count u32 = 64
llama_model_loader: - kv 30: deepseek2.rope.scaling.type str = yarn
llama_model_loader: - kv 31: deepseek2.rope.scaling.factor f32 = 40.000000
llama_model_loader: - kv 32: deepseek2.rope.scaling.original_context_length u32 = 4096
llama_model_loader: - kv 33: deepseek2.rope.scaling.yarn_log_multiplier f32 = 0.100000
llama_model_loader: - kv 34: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 35: tokenizer.ggml.pre str = deepseek-v3
llama_model_loader: - kv 36: tokenizer.ggml.tokens arr[str,129280] = ["<｜begin▁of▁sentence｜>", "<<3C>...
llama_model_loader: - kv 37: tokenizer.ggml.token_type arr[i32,129280] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 38: tokenizer.ggml.merges arr[str,127741] = ["Ġ t", "Ġ a", "i n", "Ġ Ġ", "h e...
llama_model_loader: - kv 39: tokenizer.ggml.bos_token_id u32 = 0
llama_model_loader: - kv 40: tokenizer.ggml.eos_token_id u32 = 1
llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 1
llama_model_loader: - kv 42: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 43: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 44: tokenizer.chat_template str = {% if not add_generation_prompt is de...
llama_model_loader: - kv 45: general.quantization_version u32 = 2
llama_model_loader: - kv 46: split.no u16 = 0
llama_model_loader: - kv 47: split.count u16 = 1148
llama_model_loader: - kv 48: split.tensors.count i32 = 1147
llama_model_loader: - type f32: 361 tensors
llama_model_loader: - type q8_0: 193 tensors
llama_model_loader: - type iq4_xs: 305 tensors
llama_model_loader: - type iq2_k: 40 tensors
llama_model_loader: - type iq3_k: 88 tensors
llama_model_loader: - type iq6_k: 101 tensors
llama_model_loader: - type iq4_ks: 20 tensors
llama_model_loader: - type iq1_m_r4: 26 tensors
llama_model_loader: - type iq5_k_r4: 13 tensors
llm_load_vocab: special tokens cache size = 818
llm_load_vocab: token to piece cache size = 0.8223 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = deepseek2
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 129280
llm_load_print_meta: n_merges = 127741
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 163840
llm_load_print_meta: n_embd = 7168
llm_load_print_meta: n_layer = 61
llm_load_print_meta: n_head = 128
llm_load_print_meta: n_head_kv = 128
llm_load_print_meta: n_rot = 64
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_swa_pattern = 1
llm_load_print_meta: n_embd_head_k = 192
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 24576
llm_load_print_meta: n_embd_v_gqa = 16384
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 18432
llm_load_print_meta: n_expert = 256
llm_load_print_meta: n_expert_used = 8
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = yarn
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 0.025
llm_load_print_meta: n_ctx_orig_yarn = 4096
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = 671B
llm_load_print_meta: model ftype = BF16
llm_load_print_meta: model params = 672.050 B
llm_load_print_meta: model size = 246.446 GiB (3.150 BPW)
llm_load_print_meta: repeating layers = 244.612 GiB (3.135 BPW, 670.196 B parameters)
llm_load_print_meta: general.name = DeepSeek R1 0528
llm_load_print_meta: BOS token = 0 '<｜begin▁of▁sentence｜>'
llm_load_print_meta: EOS token = 1 '<｜end▁of▁sentence｜>'
llm_load_print_meta: PAD token = 1 '<｜end▁of▁sentence｜>'
llm_load_print_meta: LF token = 131 'Ä'
llm_load_print_meta: max token length = 256
llm_load_print_meta: n_layer_dense_lead = 3
llm_load_print_meta: n_lora_q = 1536
llm_load_print_meta: n_lora_kv = 512
llm_load_print_meta: n_ff_exp = 2048
llm_load_print_meta: n_expert_shared = 1
llm_load_print_meta: expert_weights_scale = 2.5
llm_load_print_meta: expert_weights_norm = 1
llm_load_print_meta: expert_gating_func = sigmoid
llm_load_print_meta: rope_yarn_log_mul = 0.1000
llm_load_tensors: ggml ctx size = 1.87 MiB
Tensor blk.3.ffn_norm.weight buffer type overriden to CUDA0
...
Tensor blk.60.ffn_up_exps.weight buffer type overriden to CPU
llm_load_tensors: offloading 61 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 62/62 layers to GPU
llm_load_tensors: CPU buffer size = 209592.50 MiB
llm_load_tensors: CUDA_Host buffer size = 938.98 MiB
llm_load_tensors: CUDA0 buffer size = 16412.52 MiB
llm_load_tensors: CUDA1 buffer size = 11530.49 MiB
llm_load_tensors: CUDA2 buffer size = 13886.35 MiB
....................................................................................................
llama_new_context_with_model: n_ctx = 16384
llama_new_context_with_model: n_batch = 4096
llama_new_context_with_model: n_ubatch = 4096
llama_new_context_with_model: flash_attn = 1
llama_new_context_with_model: mla_attn = 3
llama_new_context_with_model: attn_max_b = 1024
llama_new_context_with_model: fused_moe = 1
llama_new_context_with_model: ser = -1, 0
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 0.025
llama_kv_cache_init: CUDA0 KV buffer size = 450.00 MiB
llama_kv_cache_init: CUDA1 KV buffer size = 342.00 MiB
llama_kv_cache_init: CUDA2 KV buffer size = 306.00 MiB
llama_new_context_with_model: KV self size = 1098.00 MiB, c^KV (f16): 1098.00 MiB, kv^T: not used
llama_new_context_with_model: CUDA_Host output buffer size = 0.49 MiB
llama_new_context_with_model: pipeline parallelism enabled (n_copies=1)
llama_new_context_with_model: CUDA0 compute buffer size = 4496.02 MiB
llama_new_context_with_model: CUDA1 compute buffer size = 3152.02 MiB
llama_new_context_with_model: CUDA2 compute buffer size = 3152.03 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 368.05 MiB
llama_new_context_with_model: graph nodes = 4200
llama_new_context_with_model: graph splits = 177
system_info: n_threads = 36 / 36 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
sampling:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
xtc_probability = 0.000, xtc_threshold = 1.000, top_n_sigma = 0.000
sampling order:
CFG -> Penalties -> dry -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> xtc -> top_n_sigma -> temperature
generate: n_ctx = 16384, n_batch = 4096, n_predict = -1, n_keep = 1
<|begin?of?sentence|><|User|>What is the solution of x+5=-2?<|Assistant|><think>
D:\a\ik_llama.cpp\ik_llama.cpp\ggml\src\ggml-cuda\mmq.cuh:107: fatal error
```
---
👤 **Thireus** commented the **2025-07-12** at **07:19:00**:<br>
Any of these won't work:
https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples
---
👤 **ikawrakow** commented the **2025-07-12** at **09:27:32**:<br>
Does #603 fix it for you?
There were two more commits after the commit that actually breaks it for your mix that uses `IQ1_M`, a typically not used quantization type.
---
👤 **Thireus** commented the **2025-07-12** at **11:08:41**:<br>
Thanks, I'll take a look now and will report back. It'll take a few hours.
---
👤 **Thireus** commented the **2025-07-12** at **18:35:40**:<br>
@ikawrakow, the fix is working! Thank you so much.
---
👤 **saood06** commented the **2025-07-13** at **07:56:18**:<br>
>The link you posted gives 404. But even if it worked, we know that the HF tensor viewer does not work when the model contains ik_llama.cpp specific quantization types.
>
>How hard is it to to post the portion of the log that tells us how many tensors there are from what type?
It no longer gives a 404 (I didn't see one). It is better than HF tensor viewer, it is a documented custom regex string.
---
👤 **saood06** commented the **2025-07-13** at **07:56:18**:<br>
>The link you posted gives 404. But even if it worked, we know that the HF tensor viewer does not work when the model contains ik_llama.cpp specific quantization types.
>
>How hard is it to to post the portion of the log that tells us how many tensors there are from what type?
It no longer gives a 404. It is better than HF tensor viewer, it is a documented custom regex string.
---
👤 **ikawrakow** commented the **2025-07-13** at **09:37:13**:<br>
> It no longer gives a 404 (I didn't see one). It is better than HF tensor viewer, it is a documented custom regex string.
Yes, I saw it after the link became accessible. That's how I knew what the issue was, and fixed it in #603.
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