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🔀 #259 - Prepare wk_b tensors of DeepSeek models on the fly

Author ikawrakow
State ❌ Closed
Created 2025-03-15
Updated 2025-03-17

Description

This enables usage of MLA also for model files that were converted with mainline llama.cpp and hence to not contain the tensors required for MLA.

MLA requires two additional tensors per layer: wk_v and wk_b. wk_v is just a view of half of the wkv_b tensor, so it is not actually necessary to have it in the model file. wk_b is a transposed version of the other half of wkv_b. If wk_b is missing in the model file, this PR computes it while loading the model. The newly created tensors are stored on the same back-end where the corresponding wkv_b tensor is stored.

In principle we could remove the preparation of wk_v and wk_b from convert_hf_to_gguf.py, but I decided have some more thorough testing in the wild before doing so.

Oh, when wkv_b is not quantized, wk_b uses the same type as wkv_b (fp16 or bf16). But if wkb_b is quantized, then wk_b becomes Q8_0, irrespectively of the wkv_b type. Transposing a quantized tensor requires dequantization to fp32, so to avoid a potential precision loss if wkv_b was quantized with low bpw, we simply use Q8_0 for wk_b.

💬 Conversation

👤 ubergarm commented the 2025-03-15 at 16:27:08:

Thanks for pushing this branch, I decided to try this first before downloading/generating my own MLA quant.

Not sure if it only works for certain quantizations? It throws an assertion error for me when trying the unsloth R1 671B UD-Q2_K_XL. Here are the details:

# Build the experimental branch  `ik/prepare_wk_b`
# Debugging symbols and CUDA backend enabled
git pull
git checkout ik/prepare_wk_b
cmake -B ./build -DCMAKE_BUILD_TYPE=Debug -DGGML_CUDA=ON -DGGML_BLAS=OFF
cmake --build ./build --config Debug -j $(nproc)

# try it with existing non-MLA quant
CUDA_VISIBLE_DEVICES="0," \
gdb ./build/bin/llama-server
(gdb) run \
      --alias unsloth/DeepSeek-R1-UD-Q2_K_XL \
      --model /mnt/raid/models/unsloth/DeepSeek-R1-GGUF/DeepSeek-R1-UD-Q2_K_XL/DeepSeek-R1-UD-Q2_K_XL-00001-of-00005.gguf \
      --ctx-size 4096 \
      --parallel 1 \
      -mla 2 -fa \
      -amb 2048 \
      -fmoe \
      -rtr \
      --n-gpu-layers 63 \
      --override-tensor exps=CPU \
      --threads 24 \
      --host 127.0.0.1 \
      --port 8080

.
.
.
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 = 205716.00 MiB
llm_load_tensors:  CUDA_Host buffer size =   497.11 MiB
llm_load_tensors:      CUDA0 buffer size =  9885.95 MiB
....................................................................................................
============ llm_load_tensors: need to compute 61 wk_b tensors
llama-server: /home/w/projects/ik_llama.cpp/ggml/src/ggml.c:4306: ggml_row_size: Assertion `ne % ggml_blck_size(type) == 0' failed.

Thread 1 "llama-server" received signal SIGABRT, Aborted.
Download failed: Invalid argument.  Continuing without source file ./nptl/./nptl/pthread_kill.c.
__pthread_kill_implementation (no_tid=0, signo=6, threadid=<optimized out>) at ./nptl/pthread_kill.c:44
warning: 44     ./nptl/pthread_kill.c: No such file or directory
(gdb) bt
#0  __pthread_kill_implementation (no_tid=0, signo=6, threadid=<optimized out>) at ./nptl/pthread_kill.c:44
#1  __pthread_kill_internal (signo=6, threadid=<optimized out>) at ./nptl/pthread_kill.c:78
#2  __GI___pthread_kill (threadid=<optimized out>, signo=signo@entry=6) at ./nptl/pthread_kill.c:89
#3  0x00007fffd6e4527e in __GI_raise (sig=sig@entry=6) at ../sysdeps/posix/raise.c:26
#4  0x00007fffd6e288ff in __GI_abort () at ./stdlib/abort.c:79
#5  0x00007fffd6e2881b in __assert_fail_base (fmt=0x7fffd6fd01e8 "%s%s%s:%u: %s%sAssertion `%s' failed.\n%n",
    assertion=assertion@entry=0x7fffd81b1ed8 "ne % ggml_blck_size(type) == 0",
    file=file@entry=0x7fffd81b11a0 "/home/w/projects/ik_llama.cpp/ggml/src/ggml.c", line=line@entry=4306,
    function=function@entry=0x7fffd81b6c58 <__PRETTY_FUNCTION__.74> "ggml_row_size") at ./assert/assert.c:96
#6  0x00007fffd6e3b517 in __assert_fail (assertion=0x7fffd81b1ed8 "ne % ggml_blck_size(type) == 0",
    file=0x7fffd81b11a0 "/home/w/projects/ik_llama.cpp/ggml/src/ggml.c", line=4306, function=0x7fffd81b6c58 <__PRETTY_FUNCTION__.74> "ggml_row_size")
    at ./assert/assert.c:105
#7  0x00007fffd76634b9 in ggml_row_size (type=GGML_TYPE_Q6_K, ne=128) at /home/w/projects/ik_llama.cpp/ggml/src/ggml.c:4306
#8  0x00007ffff7a9ad7b in llm_load_tensors (ml=..., model=..., n_gpu_layers=63, split_mode=LLAMA_SPLIT_MODE_LAYER, main_gpu=0,
    tensor_split=0x7fffffffd0f0, use_mlock=false, progress_callback=0x7ffff7ac1229 <_FUN(float, void*)>, progress_callback_user_data=0x7fffffffbc08)
    at /home/w/projects/ik_llama.cpp/src/llama.cpp:8160
#9  0x00007ffff7aadedc in llama_model_load (
    fname="/mnt/raid/models/unsloth/DeepSeek-R1-GGUF/DeepSeek-R1-UD-Q2_K_XL/DeepSeek-R1-UD-Q2_K_XL-00001-of-00005.gguf", model=..., params=...)
    at /home/w/projects/ik_llama.cpp/src/llama.cpp:8343
#10 0x00007ffff7ac1451 in llama_load_model_from_file (
    path_model=0x5555566a4cb0 "/mnt/raid/models/unsloth/DeepSeek-R1-GGUF/DeepSeek-R1-UD-Q2_K_XL/DeepSeek-R1-UD-Q2_K_XL-00001-of-00005.gguf",
    params=...) at /home/w/projects/ik_llama.cpp/src/llama.cpp:18134
#11 0x000055555572fcbe in llama_init_from_gpt_params (params=...) at /home/w/projects/ik_llama.cpp/common/common.cpp:2197
#12 0x000055555561ff52 in server_context::load_model (this=0x7fffffffd080, params_=...)
    at /home/w/projects/ik_llama.cpp/examples/server/server.cpp:682
#13 0x00005555555f2eec in main (argc=26, argv=0x7fffffffdf18) at /home/w/projects/ik_llama.cpp/examples/server/server.cpp:2628

👤 ikawrakow commented the 2025-03-15 at 16:37:09:

Sorry about that. Hope the fix I just pushed will work.

👤 ubergarm commented the 2025-03-15 at 17:11:41:

All good, happy to try this out. Great, it does startup okay now!

However, I tried 64k context and threw about 8k prompt at it, and the generation seem wonky. Same for shorter prompts and also at 8k context.

I'm happy to download and try a smaller working test quant, or try any other combination of arguments etc.

Observations

  • 64k context uses about 34GiB of 48GiB VRAM
  • 8k context uses about 14GiB of 48GiB VRAM
  • Same issue with and without -rtr

Long Prompt Test with 64k context

>>> User:

(ask a question and copy paste in about 8k from a book)

>>> Assistant:

<think>QQZZJJQQHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH^C
Response cancelled.

Short Prompt Test with 64k context

>>> User:

Count from 1 to 10 in French.

>>> Assistant:

<think>zzzzbbkk and kAAHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH^C
Response cancelled.

Short Prompt Test with 8k context

>>> User:

Count from 1 to 10 in French.

>>> Assistant:

<think>SS and AAkk,	.0
-
kk,	.3
>
HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH^C
Response cancelled.

Server with 64k context

$ ./build/bin/llama-server --version
version: 3595 (fc03b9ad)

CUDA_VISIBLE_DEVICES="0," \
./build/bin/llama-server \
    --alias unsloth/DeepSeek-R1-UD-Q2_K_XL \
    --model /mnt/raid/models/unsloth/DeepSeek-R1-GGUF/DeepSeek-R1-UD-Q2_K_XL/DeepSeek-R1-UD-Q2_K_XL-00001-of-00005.gguf \
    --ctx-size 65536 \
    --parallel 1 \
    -mla 2 -fa \
    -amb 2048 \
    -fmoe \
    -rtr \
    --n-gpu-layers 63 \
    --override-tensor exps=CPU \
    --threads 24 \
    --host 127.0.0.1 \
    --port 8080

.
.
.
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 = 205716.00 MiB
llm_load_tensors:  CUDA_Host buffer size =   497.11 MiB
llm_load_tensors:      CUDA0 buffer size =  9885.95 MiB
....................................................................................................
============ llm_load_tensors: need to compute 61 wk_b tensors
Computed blk.0.attn_k_b.weight as 128 x 512 x 128
Computed blk.1.attn_k_b.weight as 128 x 512 x 128
Computed blk.2.attn_k_b.weight as 128 x 512 x 128
Computed blk.3.attn_k_b.weight as 128 x 512 x 128
.
.
.
Computed blk.58.attn_k_b.weight as 128 x 512 x 128
Computed blk.59.attn_k_b.weight as 128 x 512 x 128
Computed blk.60.attn_k_b.weight as 128 x 512 x 128
============ Repacked 174 tensors
llama_new_context_with_model: n_ctx      = 65536
llama_new_context_with_model: n_batch    = 2048
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: flash_attn = 1
llama_new_context_with_model: mla_attn   = 2
llama_new_context_with_model: attn_max_b = 2048
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: layer 0: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 1: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 2: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 3: n_embd_head_qk_rope = 64, kv_lora_rank = 512
.
.
.
llama_kv_cache_init: layer 58: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 59: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 60: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init:      CUDA0 KV buffer size =  4392.00 MiB
llama_new_context_with_model: KV self size  = 4392.00 MiB, c^KV (f16): 4392.00 MiB, kv^T: not used
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.99 MiB
llama_new_context_with_model:      CUDA0 compute buffer size = 19857.00 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =   240.01 MiB
llama_new_context_with_model: graph nodes  = 3548
llama_new_context_with_model: graph splits = 118
INFO [                    init] initializing slots | tid="136342914363392" timestamp=1742057505 n_slots=1
INFO [                    init] new slot | tid="136342914363392" timestamp=1742057505 id_slot=0 n_ctx_slot=65536
INFO [                    main] model loaded | tid="136342914363392" timestamp=1742057505
INFO [                    main] chat template | tid="136342914363392" timestamp=1742057505 chat_example="You are a helpful assistant\n\n<｜User｜>Hell
o<｜Assistant｜>Hi there<｜end▁of▁sentence｜><｜User｜>How are you?<｜Assistant｜>" built_in=true
INFO [                    main] HTTP server listening | tid="136342914363392" timestamp=1742057505 n_threads_http="47" port="8080" hostname="127.0.0.1
"
INFO [            update_slots] all slots are idle | tid="136342914363392" timestamp=1742057505
INFO [      log_server_request] request | tid="136329442553856" timestamp=1742057524 remote_addr="127.0.0.1" remote_port=45946 status=200 method="GET"
 path="/v1/models" params={}
INFO [   launch_slot_with_task] slot is processing task | tid="136342914363392" timestamp=1742057604 id_slot=0 id_task=0
INFO [            update_slots] kv cache rm [p0, end) | tid="136342914363392" timestamp=1742057604 id_slot=0 id_task=0 p0=0
INFO [            update_slots] kv cache rm [p0, end) | tid="136342914363392" timestamp=1742057622 id_slot=0 id_task=0 p0=2048
INFO [            update_slots] kv cache rm [p0, end) | tid="136342914363392" timestamp=1742057643 id_slot=0 id_task=0 p0=4096
INFO [            update_slots] kv cache rm [p0, end) | tid="136342914363392" timestamp=1742057665 id_slot=0 id_task=0 p0=6144
INFO [            update_slots] kv cache rm [p0, end) | tid="136342914363392" timestamp=1742057691 id_slot=0 id_task=0 p0=8192
INFO [      log_server_request] request | tid="136329450946560" timestamp=1742057722 remote_addr="127.0.0.1" remote_port=56568 status=200 method="POST
" path="/v1/chat/completions" params={}
INFO [            update_slots] slot released | tid="136342914363392" timestamp=1742057722 id_slot=0 id_task=0 n_ctx=65536 n_past=8988 n_system_tokens
=0 n_cache_tokens=8988 truncated=false
INFO [            update_slots] all slots are idle | tid="136342914363392" timestamp=1742057722

👤 ubergarm commented the 2025-03-15 at 17:17:59:

Confirmed similar wonky generations using ./build/bin/llama-cli to take my client out of the picture.

👤 ikawrakow commented the 2025-03-15 at 17:41:33:

Yes, I see similar behavior with DeepSeek-Lite. I broke something somewhere and need to investigate. I got confused and tested with options that did not actually trigger the usage of the computed tensors.

👤 saood06 commented the 2025-03-16 at 00:44:48:

Also currently trying some other combinations. This one with -mla 1 spammed the logs like so:

CUDA_VISIBLE_DEVICES="0," \
./build/bin/llama-cli \
    --alias unsloth/DeepSeek-R1-UD-Q2_K_XL \
    --model /mnt/raid/models/unsloth/DeepSeek-R1-GGUF/DeepSeek-R1-UD-Q2_K_XL/DeepSeek-R1-UD-Q2_K_XL-00001-of-00005.gguf \
    --ctx-size 8192 \
    --parallel 1 \
    -mla 1 -fa \
    --n-gpu-layers 63 \
    --override-tensor exps=CPU \
    --threads 24

Unsupported KV type combination for head_sizes 576 / 512
Unsupported KV type combination for head_sizes 576 / 512
Unsupported KV type combination for head_sizes 576 / 512
Unsupported KV type combination for head_sizes 576 / 512

I think this is because -mla 1 -fa is currently only supported on the CPU and not on CUDA

👤 ikawrakow commented the 2025-03-16 at 06:25:30:

@ubergarm Thank you for playing with this, it is very helpful.

I think I finally fixed the issue with mla = 2, so It should work now with Unsloth's models (or any other model created with mainline llama.cpp).

I'm surprised by the giant CUDA compute buffer for a context of 65k. This basically renders the mla=2, fa=1 option useless for anyone not being lucky enough to have a 48 GB GPU. The KV buffer size is exactly as expected (576 * n_ctx * 61 * sizeof(f16). For long contexts most of the compute buffer goes into operations with the KV cache in one layer, so I was expecting it to be only marginally larger than the 2788 MiB I observe at 65k tokens for DeepSeek-Lite as the cache size per layer is the same. I guess, I need to look into this more closely.

-mla 1 -fa only works on the CPU. I haven't been able to adapt the existing FA kernel to work correctly with head sizes > 256. I guess, I need to write a new CUDA kernel for this case.

👤 ubergarm commented the 2025-03-16 at 14:38:44:

@ikawrakow

I appreciate all your discussions in the various PRs, each one a treasure trove of knowledge!

I think I finally fixed the issue with mla = 2, so It should work now with Unsloth's models (or any other model created with mainline llama.cpp).

I'll give this a try again and confirm. If it works, then I can easily compare perplexity of my new custom quants against the unsloth one I have been using with similar mla=2 fa=1 options.

-mla 1 -fa only works on the CPU.

Perfect, I'll add a note in my rough guide. I still haven't fully grokk'd the implications of -mla 1 vs -mla 2 yet so I'll eventually compare them both on CPU and simply use -mla 2 for CUDA no problemo.

👤 ubergarm commented the 2025-03-16 at 15:03:50:

WIP

Update Branch

# update
git checkout ik/prepare_wk_b
git pull
git rev-parse --short HEAD
f2fb15de
# rebuild and confirm
./build/bin/llama-server --version
version: 3596 (f2fb15de)

Test

# Uses about 22GiB VRAM @ 32k context
CUDA_VISIBLE_DEVICES="0," \
./build/bin/llama-server \
    --alias unsloth/DeepSeek-R1-UD-Q2_K_XL \
    --model /mnt/raid/models/unsloth/DeepSeek-R1-GGUF/DeepSeek-R1-UD-Q2_K_XL/DeepSeek-R1-UD-Q2_K_XL-00001-of-00005.gguf \
    --ctx-size 32768 \
    -ctk q8_0 -ctv q8_0 \
    -mla 2 -fa \
    -amb 2048 \
    -fmoe \
    --n-gpu-layers 63 \
    --override-tensor exps=CPU \
    --parallel 1 \
    --threads 24 \
    --host 127.0.0.1 \
    --port 8080

Logs

Open the details fold for complete logs. 👇

Collapsed Logs 
$ ./myscripts/api-server-DeepSeek-R1-UD-Q2_K_XL.sh
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA RTX A6000, compute capability 8.6, VMM: yes
INFO [                    main] build info | tid="137362671300608" timestamp=1742136822 build=3596 commit="f2fb15de"
INFO [                    main] system info | tid="137362671300608" timestamp=1742136822 n_threads=24 n_threads_batch=-1 total_threads=48 system_info=
"AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F
16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | "
llama_model_loader: additional 4 GGUFs metadata loaded.
llama_model_loader: loaded meta data with 48 key-value pairs and 1025 tensors from /mnt/raid/models/unsloth/DeepSeek-R1-GGUF/DeepSeek-R1-UD-Q2_K_XL/De
epSeek-R1-UD-Q2_K_XL-00001-of-00005.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 BF16
llama_model_loader: - kv   3:                       general.quantized_by str              = Unsloth
llama_model_loader: - kv   4:                         general.size_label str              = 256x20B
llama_model_loader: - kv   5:                           general.repo_url str              = https://huggingface.co/unsloth
llama_model_loader: - kv   6:                      deepseek2.block_count u32              = 61
llama_model_loader: - kv   7:                   deepseek2.context_length u32              = 163840
llama_model_loader: - kv   8:                 deepseek2.embedding_length u32              = 7168
llama_model_loader: - kv   9:              deepseek2.feed_forward_length u32              = 18432
llama_model_loader: - kv  10:             deepseek2.attention.head_count u32              = 128
llama_model_loader: - kv  11:          deepseek2.attention.head_count_kv u32              = 128
llama_model_loader: - kv  12:                   deepseek2.rope.freq_base f32              = 10000.000000
llama_model_loader: - kv  13: deepseek2.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  14:                deepseek2.expert_used_count u32              = 8
llama_model_loader: - kv  15:        deepseek2.leading_dense_block_count u32              = 3
llama_model_loader: - kv  16:                       deepseek2.vocab_size u32              = 129280
llama_model_loader: - kv  17:            deepseek2.attention.q_lora_rank u32              = 1536
llama_model_loader: - kv  18:           deepseek2.attention.kv_lora_rank u32              = 512
llama_model_loader: - kv  19:             deepseek2.attention.key_length u32              = 192
llama_model_loader: - kv  20:           deepseek2.attention.value_length u32              = 128
llama_model_loader: - kv  21:       deepseek2.expert_feed_forward_length u32              = 2048
llama_model_loader: - kv  22:                     deepseek2.expert_count u32              = 256
llama_model_loader: - kv  23:              deepseek2.expert_shared_count u32              = 1
llama_model_loader: - kv  24:             deepseek2.expert_weights_scale f32              = 2.500000
llama_model_loader: - kv  25:              deepseek2.expert_weights_norm bool             = true
llama_model_loader: - kv  26:               deepseek2.expert_gating_func u32              = 2
llama_model_loader: - kv  27:             deepseek2.rope.dimension_count u32              = 64
llama_model_loader: - kv  28:                deepseek2.rope.scaling.type str              = yarn
llama_model_loader: - kv  29:              deepseek2.rope.scaling.factor f32              = 40.000000
llama_model_loader: - kv  30: deepseek2.rope.scaling.original_context_length u32              = 4096
llama_model_loader: - kv  31: deepseek2.rope.scaling.yarn_log_multiplier f32              = 0.100000
llama_model_loader: - kv  32:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  33:                         tokenizer.ggml.pre str              = deepseek-v3
llama_model_loader: - kv  34:                      tokenizer.ggml.tokens arr[str,129280]  = ["<｜begin▁of▁sentence｜>", "<...
llama_model_loader: - kv  35:                  tokenizer.ggml.token_type arr[i32,129280]  = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  36:                      tokenizer.ggml.merges arr[str,127741]  = ["Ġ t", "Ġ a", "i n", "Ġ Ġ", "h e...
llama_model_loader: - kv  37:                tokenizer.ggml.bos_token_id u32              = 0
llama_model_loader: - kv  38:                tokenizer.ggml.eos_token_id u32              = 1
llama_model_loader: - kv  39:            tokenizer.ggml.padding_token_id u32              = 128815
llama_model_loader: - kv  40:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  41:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  42:                    tokenizer.chat_template str              = {% if not add_generation_prompt is de...
llama_model_loader: - kv  43:               general.quantization_version u32              = 2
llama_model_loader: - kv  44:                          general.file_type u32              = 10
llama_model_loader: - kv  45:                                   split.no u16              = 0
llama_model_loader: - kv  46:                        split.tensors.count i32              = 1025
llama_model_loader: - kv  47:                                split.count u16              = 5
llama_model_loader: - type  f32:  361 tensors
llama_model_loader: - type q2_K:  171 tensors
llama_model_loader: - type q3_K:    3 tensors
llama_model_loader: - type q4_K:  306 tensors
llama_model_loader: - type q6_K:  184 tensors
llm_load_vocab: special tokens cache size = 819
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_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      = Q2_K - Medium
llm_load_print_meta: model params     = 671.026 B
llm_load_print_meta: model size       = 211.034 GiB (2.701 BPW)
llm_load_print_meta: repeating layers = 209.841 GiB (2.694 BPW, 669.173 B parameters)
llm_load_print_meta: general.name     = DeepSeek R1 BF16
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        = 128815 '<｜PAD▁TOKEN｜>'
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 =    0.85 MiB
Tensor blk.3.ffn_gate_exps.weight buffer type overriden to CPU
Tensor blk.3.ffn_down_exps.weight buffer type overriden to CPU
Tensor blk.3.ffn_up_exps.weight buffer type overriden to CPU
Tensor blk.4.ffn_gate_exps.weight buffer type overriden to CPU
Tensor blk.4.ffn_down_exps.weight buffer type overriden to CPU
Tensor blk.4.ffn_up_exps.weight buffer type overriden to CPU
Tensor blk.5.ffn_gate_exps.weight buffer type overriden to CPU
Tensor blk.5.ffn_down_exps.weight buffer type overriden to CPU
Tensor blk.5.ffn_up_exps.weight buffer type overriden to CPU
Tensor blk.6.ffn_gate_exps.weight buffer type overriden to CPU
Tensor blk.6.ffn_down_exps.weight buffer type overriden to CPU
Tensor blk.6.ffn_up_exps.weight buffer type overriden to CPU
Tensor blk.7.ffn_gate_exps.weight buffer type overriden to CPU
Tensor blk.7.ffn_down_exps.weight buffer type overriden to CPU
Tensor blk.7.ffn_up_exps.weight buffer type overriden to CPU
Tensor blk.8.ffn_gate_exps.weight buffer type overriden to CPU
.
.
.
Tensor blk.59.ffn_up_exps.weight buffer type overriden to CPU
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 = 205716.00 MiB
llm_load_tensors:        CPU buffer size =   497.11 MiB
llm_load_tensors:      CUDA0 buffer size =  9885.95 MiB
....................................................................................................
============ llm_load_tensors: need to compute 61 wk_b tensors
Computed blk.0.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.1.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.2.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.3.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.4.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.5.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.6.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.7.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.8.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.9.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.10.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.11.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.12.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.13.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.14.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.15.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.16.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.17.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.18.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.19.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.20.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.21.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.22.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.23.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.24.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.25.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.26.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.27.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.28.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.29.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.30.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.31.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.32.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.33.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.34.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.35.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.36.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.37.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.38.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.39.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.40.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.41.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.42.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.43.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.44.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.45.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.46.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.47.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.48.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.49.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.50.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.51.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.52.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.53.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.54.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.55.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.56.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.57.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.58.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.59.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
Computed blk.60.attn_v_b.weight as 128 x 512 x 128 and stored in buffer CUDA0
llama_new_context_with_model: n_ctx      = 32768
llama_new_context_with_model: n_batch    = 2048
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: flash_attn = 1
llama_new_context_with_model: mla_attn   = 2
llama_new_context_with_model: attn_max_b = 2048
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: layer 0: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 1: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 2: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 3: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 4: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 5: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 6: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 7: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 8: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 9: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 10: n_embd_head_qk_rope = 64, kv_lora_rank = 512
.
.
.
llama_kv_cache_init: layer 58: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 59: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init: layer 60: n_embd_head_qk_rope = 64, kv_lora_rank = 512
llama_kv_cache_init:      CUDA0 KV buffer size =  1166.65 MiB
llama_new_context_with_model: KV self size  = 1166.62 MiB, c^KV (q8_0): 1166.62 MiB, kv^T: not used
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.99 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =  8470.00 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =    78.01 MiB
llama_new_context_with_model: graph nodes  = 3548
llama_new_context_with_model: graph splits = 118
INFO [                    init] initializing slots | tid="137362671300608" timestamp=1742136993 n_slots=1
INFO [                    init] new slot | tid="137362671300608" timestamp=1742136993 id_slot=0 n_ctx_slot=32768
INFO [                    main] model loaded | tid="137362671300608" timestamp=1742136993
INFO [                    main] chat template | tid="137362671300608" timestamp=1742136993 chat_example="You are a helpful assistant\n\n<｜User｜>Hell
o<｜Assistant｜>Hi there<｜end▁of▁sentence｜><｜User｜>How are you?<｜Assistant｜>" built_in=true
INFO [                    main] HTTP server listening | tid="137362671300608" timestamp=1742136993 n_threads_http="47" port="8080" hostname="127.0.0.1
"
INFO [            update_slots] all slots are idle | tid="137362671300608" timestamp=1742136993
INFO [      log_server_request] request | tid="137360887316480" timestamp=1742137013 remote_addr="127.0.0.1" remote_port=35958 status=200 method="GET"
 path="/v1/models" params={}
INFO [   launch_slot_with_task] slot is processing task | tid="137362671300608" timestamp=1742137018 id_slot=0 id_task=0
INFO [            update_slots] kv cache rm [p0, end) | tid="137362671300608" timestamp=1742137018 id_slot=0 id_task=0 p0=0
INFO [           print_timings] prompt eval time     =     739.81 ms /    13 tokens (   56.91 ms per token,    17.57 tokens per second) | tid="1373626
71300608" timestamp=1742137056 id_slot=0 id_task=0 t_prompt_processing=739.81 n_prompt_tokens_processed=13 t_token=56.90846153846154 n_tokens_second=1
7.572079317662645
INFO [           print_timings] generation eval time =   37448.69 ms /   549 runs   (   68.21 ms per token,    14.66 tokens per second) | tid="1373626
71300608" timestamp=1742137056 id_slot=0 id_task=0 t_token_generation=37448.694 n_decoded=549 t_token=68.21255737704918 n_tokens_second=14.66005730400
1041
INFO [           print_timings]           total time =   38188.50 ms | tid="137362671300608" timestamp=1742137056 id_slot=0 id_task=0 t_prompt_process
ing=739.81 t_token_generation=37448.694 t_total=38188.504
INFO [            update_slots] slot released | tid="137362671300608" timestamp=1742137056 id_slot=0 id_task=0 n_ctx=32768 n_past=561 n_system_tokens=
0 n_cache_tokens=561 truncated=false
INFO [            update_slots] all slots are idle | tid="137362671300608" timestamp=1742137056
INFO [      log_server_request] request | tid="137349061144576" timestamp=1742137056 remote_addr="127.0.0.1" remote_port=39278 status=200 method="POST
" path="/v1/chat/completions" params={}
INFO [            update_slots] all slots are idle | tid="137362671300608" timestamp=1742137056
INFO [      log_server_request] request | tid="137349052751872" timestamp=1742137139 remote_addr="127.0.0.1" remote_port=52170 status=200 method="GET"
 path="/v1/models" params={}
INFO [   launch_slot_with_task] slot is processing task | tid="137362671300608" timestamp=1742137148 id_slot=0 id_task=551
INFO [            update_slots] kv cache rm [p0, end) | tid="137362671300608" timestamp=1742137148 id_slot=0 id_task=551 p0=2
INFO [            update_slots] kv cache rm [p0, end) | tid="137362671300608" timestamp=1742137179 id_slot=0 id_task=551 p0=2050
☝️

👤 ubergarm commented the 2025-03-16 at 21:49:12:

Confirmed it is working with three different unsloth quants on that intel6980P. Fastest CPU only speeds I've been able to achieve with this rig!

Benchmarks 
$ git rev-parse --short HEAD
f2fb15de
$ ./build/bin/llama-server --version
version: 3596 (f2fb15de)

$ sudo powerprofilesctl set performance
$ echo 0 | sudo tee /proc/sys/kernel/numa_balancing
$ cat /sys/kernel/mm/transparent_hugepage/enabled
[always] madvise never

# ran this with various number of threads for unsloth Q8_0, Q4_K_M, and UD-Q2_K_XL
numactl -N 0 -m 0 \
./build/bin/llama-bench \
    --model /mnt/ai/models/unsloth/DeepSeek-R1-GGUF/DeepSeek-R1-Q8_0/DeepSeek-R1.Q8_0-00001-of-00015.gguf \
    -ctk f16 -ctv f16 \
    -mla 2 -fa 1 \
    -amb 2048 \
    -fmoe 1 \
    -rtr 1 \
    --numa numactl \
    --threads 43,64,86,128
model size params backend threads fa mla amb rtr fmoe test t/s
deepseek2 671B Q4_K - Medium 376.65 GiB 671.03 B CPU 64 1 2 2048 1 1 pp512 93.08 ± 0.76
deepseek2 671B Q4_K - Medium 376.65 GiB 671.03 B CPU 64 1 2 2048 1 1 tg128 10.02 ± 0.00
deepseek2 671B Q4_K - Medium 376.65 GiB 671.03 B CPU 86 1 2 2048 1 1 pp512 114.34 ± 0.67
deepseek2 671B Q4_K - Medium 376.65 GiB 671.03 B CPU 86 1 2 2048 1 1 tg128 9.87 ± 0.00
deepseek2 671B Q4_K - Medium 376.65 GiB 671.03 B CPU 128 1 2 2048 1 1 pp512 143.04 ± 7.88
deepseek2 671B Q4_K - Medium 376.65 GiB 671.03 B CPU 128 1 2 2048 1 1 tg128 9.07 ± 0.00
model size params backend threads fa mla amb rtr fmoe test t/s
deepseek2 671B Q8_0 664.29 GiB 671.03 B CPU 43 1 2 2048 1 1 pp512 77.28 ± 0.14
deepseek2 671B Q8_0 664.29 GiB 671.03 B CPU 43 1 2 2048 1 1 tg128 6.50 ± 0.00
deepseek2 671B Q8_0 664.29 GiB 671.03 B CPU 64 1 2 2048 1 1 pp512 107.43 ± 6.55
deepseek2 671B Q8_0 664.29 GiB 671.03 B CPU 64 1 2 2048 1 1 tg128 7.52 ± 0.00
deepseek2 671B Q8_0 664.29 GiB 671.03 B CPU 86 1 2 2048 1 1 pp512 110.24 ± 4.70
deepseek2 671B Q8_0 664.29 GiB 671.03 B CPU 86 1 2 2048 1 1 tg128 7.37 ± 0.00
deepseek2 671B Q8_0 664.29 GiB 671.03 B CPU 128 1 2 2048 1 1 pp512 152.62 ± 6.02
deepseek2 671B Q8_0 664.29 GiB 671.03 B CPU 128 1 2 2048 1 1 tg128 7.01 ± 0.00
model size params backend threads fa mla amb rtr fmoe test t/s
deepseek2 671B Q2_K - Medium 211.03 GiB 671.03 B CPU 64 1 2 2048 1 1 pp512 101.23 ± 0.11
deepseek2 671B Q2_K - Medium 211.03 GiB 671.03 B CPU 64 1 2 2048 1 1 tg128 9.47 ± 0.01
deepseek2 671B Q2_K - Medium 211.03 GiB 671.03 B CPU 43 1 2 2048 1 1 pp512 76.69 ± 0.14
deepseek2 671B Q2_K - Medium 211.03 GiB 671.03 B CPU 43 1 2 2048 1 1 tg128 8.37 ± 0.00
deepseek2 671B Q2_K - Medium 211.03 GiB 671.03 B CPU 64 1 2 2048 1 1 pp512 98.91 ± 0.19
deepseek2 671B Q2_K - Medium 211.03 GiB 671.03 B CPU 64 1 2 2048 1 1 tg128 9.32 ± 0.01
deepseek2 671B Q2_K - Medium 211.03 GiB 671.03 B CPU 86 1 2 2048 1 1 pp512 118.22 ± 0.55
deepseek2 671B Q2_K - Medium 211.03 GiB 671.03 B CPU 86 1 2 2048 1 1 tg128 9.63 ± 0.00
deepseek2 671B Q2_K - Medium 211.03 GiB 671.03 B CPU 128 1 2 2048 1 1 pp512 147.49 ± 12.00
deepseek2 671B Q2_K - Medium 211.03 GiB 671.03 B CPU 128 1 2 2048 1 1 tg128 9.94 ± 0.00
deepseek2 671B Q2_K - Medium 211.03 GiB 671.03 B CPU 172 1 2 2048 1 1 pp512 113.38 ± 0.68
deepseek2 671B Q2_K - Medium 211.03 GiB 671.03 B CPU 172 1 2 2048 1 1 tg128 8.78 ± 0.00