### 🗣️ [#397](https://github.com/ikawrakow/ik_llama.cpp/discussions/397) - KV split while using `-sm row`
| **Author** | `pt13762104` |
| :--- | :--- |
| **Created** | 2025-05-08 |
| **Updated** | 2025-05-08 |
---
#### Description
I have found that ik_llama.cpp does NOT support kv-split while using `-sm row`, which is a limitation compared to llama.cpp. Is there any way to do this or it's just not implemented yet?
Example output:
```
INFO [ main] build info | tid="137884088823808" timestamp=1746690385 build=3673 commit="4084ca73"
INFO [ main] system info | tid="137884088823808" timestamp=1746690385 n_threads=2 n_threads_batch=-1 total_threads=4 system_info="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 | "
llama_model_loader: loaded meta data with 32 key-value pairs and 707 tensors from /root/Qwen3-32B-UD-Q5_K_XL.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen3
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Qwen3-32B
llama_model_loader: - kv 3: general.basename str = Qwen3-32B
llama_model_loader: - kv 4: general.quantized_by str = Unsloth
llama_model_loader: - kv 5: general.size_label str = 32B
llama_model_loader: - kv 6: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 7: qwen3.block_count u32 = 64
llama_model_loader: - kv 8: qwen3.context_length u32 = 40960
llama_model_loader: - kv 9: qwen3.embedding_length u32 = 5120
llama_model_loader: - kv 10: qwen3.feed_forward_length u32 = 25600
llama_model_loader: - kv 11: qwen3.attention.head_count u32 = 64
llama_model_loader: - kv 12: qwen3.attention.head_count_kv u32 = 8
llama_model_loader: - kv 13: qwen3.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 14: qwen3.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 15: qwen3.attention.key_length u32 = 128
llama_model_loader: - kv 16: qwen3.attention.value_length u32 = 128
llama_model_loader: - kv 17: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 18: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 19: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 20: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 21: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 22: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 23: tokenizer.ggml.padding_token_id u32 = 151654
llama_model_loader: - kv 24: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 25: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>...
llama_model_loader: - kv 26: general.quantization_version u32 = 2
llama_model_loader: - kv 27: general.file_type u32 = 17
llama_model_loader: - kv 28: quantize.imatrix.file str = Qwen3-32B-GGUF/imatrix_unsloth.dat
llama_model_loader: - kv 29: quantize.imatrix.dataset str = unsloth_calibration_Qwen3-32B.txt
llama_model_loader: - kv 30: quantize.imatrix.entries_count i32 = 448
llama_model_loader: - kv 31: quantize.imatrix.chunks_count i32 = 32
llama_model_loader: - type f32: 257 tensors
llama_model_loader: - type q4_K: 28 tensors
llama_model_loader: - type q5_K: 300 tensors
llama_model_loader: - type q6_K: 122 tensors
llm_load_vocab: special tokens cache size = 26
llm_load_vocab: token to piece cache size = 0.9311 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen3
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 151936
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 40960
llm_load_print_meta: n_embd = 5120
llm_load_print_meta: n_layer = 64
llm_load_print_meta: n_head = 64
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_swa_pattern = 1
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
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 = 25600
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 40960
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 = ?B
llm_load_print_meta: model ftype = Q5_K - Medium
llm_load_print_meta: model params = 32.762 B
llm_load_print_meta: model size = 21.603 GiB (5.664 BPW)
llm_load_print_meta: repeating layers = 20.510 GiB (5.646 BPW, 31.206 B parameters)
llm_load_print_meta: general.name = Qwen3-32B
llm_load_print_meta: BOS token = 11 ','
llm_load_print_meta: EOS token = 151645 '<|im_end|>'
llm_load_print_meta: PAD token = 151654 '<|vision_pad|>'
llm_load_print_meta: LF token = 148848 'ÄĬ'
llm_load_print_meta: EOT token = 151645 '<|im_end|>'
llm_load_print_meta: max token length = 256
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 2 CUDA devices:
Device 0: Tesla T4, compute capability 7.5, VMM: yes
Device 1: Tesla T4, compute capability 7.5, VMM: yes
llm_load_tensors: ggml ctx size = 0.95 MiB
llm_load_tensors: offloading 64 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 65/65 layers to GPU
llm_load_tensors: CUDA_Split buffer size = 21608.65 MiB
llm_load_tensors: CPU buffer size = 510.04 MiB
llm_load_tensors: CUDA0 buffer size = 2.58 MiB
..................................................................................................
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_batch = 4096
llama_new_context_with_model: n_ubatch = 1024
llama_new_context_with_model: flash_attn = 1
llama_new_context_with_model: mla_attn = 0
llama_new_context_with_model: attn_max_b = 0
llama_new_context_with_model: fused_moe = 0
llama_new_context_with_model: ser = -1, 0
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 2048.00 MiB # where is CUDA1?
llama_new_context_with_model: KV self size = 2048.00 MiB, K (f16): 1024.00 MiB, V (f16): 1024.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 1.16 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 633.50 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 52.01 MiB
llama_new_context_with_model: graph nodes = 1734
llama_new_context_with_model: graph splits = 2
INFO [ init] initializing slots | tid="137884088823808" timestamp=1746690394 n_slots=1
INFO [ init] new slot | tid="137884088823808" timestamp=1746690394 id_slot=0 n_ctx_slot=8192
INFO [ main] model loaded | tid="137884088823808" timestamp=1746690394
INFO [ main] chat template | tid="137884088823808" timestamp=1746690394 chat_example="<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi there<|im_end|>\n<|im_start|>user\nHow are you?<|im_end|>\n<|im_start|>assistant\n" built_in=true
INFO [ main] HTTP server listening | tid="137884088823808" timestamp=1746690394 n_threads_http="3" port="8080" hostname="127.0.0.1"
INFO [ update_slots] all slots are idle | tid="137884088823808" timestamp=1746690394
^C
INFO [ update_slots] all slots are idle | tid="137884088823808" timestamp=1746690402
```
---
#### 🗣️ Discussion
👤 **ikawrakow** replied the **2025-05-08** at **08:08:16**:
I have never looked into splitting the KV cache when using `-sm row`, so the behavior is whatever the behavior of `llama.cpp` was when I forked last year.
Out of curiosity: does `-sm row` give you a better performance compared to `-sm layer` ?
> 👤 **pt13762104** replied the **2025-05-08** at **08:36:42**:
> Yes. About 1.5x better