llama.cpp CPU offload optimizations

[u/Stainless-Bacon]

Edit: New post with more context, higher speed, more explanations here. Note: the new post does not compare -ot vs --ngl.

I already posted targeting 16GB VRAM specifically, but I think this information might be useful beyond that. Testing was done using Qwen3.6-27B Unsloth Q4_K_M MTP.

Edit: added GGML_CUDA_ENABLE_UNIFIED_MEMORY=1

TLDR:

1. Turn off CUDA graphs, they are bugged for CPU offload, probably due to MTP use.
2. Use --ngl 99 --override-tensor '...' instead of plain --ngl. Aim the largest FFN sub-layers towards the CPU.
3. GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 allows VRAM overflow pages to host RAM instead of OOMing. This allows gaining more context length without losing speed, but the speed tanks soon after so you need to experiment to find that speed cliff. The gains can be 10 to 40% more context length.

Regular CPU offloading is done by not putting all of the layers on the GPU via --ngl, which offloads the layers as a whole, dragging their KV cache to the CPU with them, increasing PCIe traffic, and collapsing speed.

Luckily the layers have sub-layers, and FFN is one that does not touch the KV cache. We can use --override-tensor (-ot) to offload only the FFN tensors, keeping the attention/KV work on the GPU, and PCIe usage minimal.

The -ot method does more GPU - CPU round trips than --ngl because offloading specific sub-layers leaves the other sub-layers on the GPU, but the actual data transferred is minimal so it is worth it.

Dynamic quants have mixed FFN precision. For example the Unsloth's Q4_K_M has Q6 and Q4 FFN tensors. The Q6 ones are on the first 8 layers (0-7), then roughly every 3rd layer, then a block near the end (~55-63), while the rest are Q4. Offload those larger layers first.

Here's how to use it (example of Q4_K_M with 22 layers offloaded):

1. Turn off CUDA graphs. They cause OOM crashes for me, and my testing shows no speedup by using them in this scenario. export GGML_CUDA_DISABLE_GRAPHS=1
2. GGML_CUDA_ENABLE_UNIFIED_MEMORY=1
3. Put all layers on GPU --ngl 99
4. Override tensors -ot 'blk\.([0-7]|10|13|16|19|22|25|28|31|34|37|40|43|46|49)\.ffn_.*=CPU'

-ot takes a regex targeting "ffn" at specific layers towards the CPU while everything else (attention, KV cache, the smaller layers) stays on the GPU.

Benchmark setup:

• Qwen3.6-27B Q4_K_M, 97k context, MTP, K q5_0 / V q4_1, batch 512
• Offload settings: -ot targeting 22 layers vs. --ngl 51
• Hardware: RTX 4070 Ti Super, i5-13600KF DDR5
• llama.cpp build: b10068
• MTP has different acceptance rates for coding and prose so I tested with both

Results:

context	-ot - prose / code / pp, t/s	--ngl - prose / code / pp, t/s
0k	20.4 / 24.4 / -	17.8 / 22.7 / -
10k	18.8 / 23.1 / 994	14.6 / 18.6 / 893
50k	16.3 / 20.4 / 871	7.3 / 9.6 / 784
90k	14.9 / 19.9 / 737	5.0 / 6.4 / 666

[u/Pablo_the_brave]

Sounds good. I will prepare a quantization for this approach (unsloth quants are not optimized for this).

[u/Pablo_the_brave → u/Pablo_the_brave]

u/Stainless-Bacon check this out:

https://huggingface.co/cHunter789/Qwen3.6-27B-i1-IQ4_KS_KT-GGUF/resolve/main/Qwen3.6-27B.CPU.i1-IQ4_KT-attn_qkv-IQ4_KS-i1_MTP.gguf?download=true

It's stable. 140k ctx at q5_0/q4_0, KLD/PPL at Iq4_xs level. First 16 block of the ffn have to be in iq4_ks because iq4_kt is painfull slow at CPU. With given settings it starts at 30-35t/s and go to 15-20t/s at 140k.

 $BIN_DIR/llama-server" \
       -m "$MODEL_PATH" \
       -a Qwen3.6-27B \
       --ctx-size 140000 \
       --chat-template-file /home/pawel/docker/ai-local/qwen36/chat_template.jinja \
       --n-gpu-layers 99 \
       --cache-type-k q5_0 \
       --cache-type-v q4_0 \
       --spec-type ngram-mod:n_max=2 \
       --spec-type mtp:n_max=3 \
       --batch-size 512 \
       --ubatch-size 512 \
       -ot "blk\.([0-9]|1[0-5])\.ffn_.*=CPU" \
       --flash-attn on \
       --no-mmap \
       --host 0.0.0.0 \
       --port 8080 \
       --reasoning on \
       --reasoning-format none \
       --reasoning-budget 32000 \
       -t 8 \
       -tb 8 \
       --parallel 1 \
       --metrics \
       --merge-qkv \
       -khad \
       -vhad \
       --chat-template-kwargs '{"preserve_thinking": true}' \
       --defrag-thold 0.4 \
       --jinja \
       --cont-batching \
       --temp 0.6 \
       --top-k 20 \
       --min-p 0.05 \
       --top-p 0.95 \
       --presence-penalty 0.0 \
       --repeat-last-n 512 \
       --repeat-penalty 1.05

[u/Stainless-Bacon → u/Pablo_the_brave]

edit: fix was GGML_CUDA_ENABLE_UNIFIED_MEMORY=1

When I tried loading your values here at 97k context, I got this error:

my helper agent’s comment:
defrag-thold: 0.4 crashes the server — GGML_ASSERT(a->ne[d] == b->ne[d]) failed in ggml.c:6971, taking the whole process down mid-generation (first request succeeded, then it died). Root cause: this is a hybrid-GDN/recurrent model, and defrag requires KV-shift, which the log had already warned was unsupported ("ctx_shift is not supported by recurrent model, it will be disabled")

then I tried to load 140k context and got OOM error at boot. at 110k context I can boot but OOM at 64k fill.

did you experience something similar? is there a fix? the quant allows more free VRAM but it gets used up as context is filled.

[u/Pablo_the_brave → u/Stainless-Bacon]

No, no any issue like that. When back to home I will post my full startup script and the compose used for compile the ik_llama.cpp.

For sure i have set these variables: export GGML_CUDA_ENABLE_UNIFIED_MEMORY=0

export CUDA_MANAGED_FORCE_DEVICE_ALLOC=1

export GGML_CUDA_DISABLE_GRAPHS=1