Yes — with 183.8 GB to spare
DeepSeek-R1-Distill-Qwen 32B at Q4_K_M fits your CPU only · DDR5 8-channel server entirely on the GPU at 8K context, at an estimated 7.0 tokens per second. There is room for its full 128K window.
Fully on GPU
8K context
Q4_K_M · 18.4 GB
MIT
Released Jan 2025
MIT-licensed and close to the 70B distill on maths. A 24 GB card handles it at Q4.
The VRAM budget
weights 18.4 GB
Weights 18.4 GB
KV cache @ 8K 2.00 GB
Runtime overhead 0.6 GB
Free 183.8 GB of 204.8 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 32.5 GB | 35.1 GB | 128K | 4.0 | −0.1% ppl | Long context |
| Q6_K | 25.0 GB | 27.6 GB | 128K | 5.1 | −0.4% ppl | Long context |
| Q5_K_M | 21.7 GB | 24.3 GB | 128K | 5.9 | −0.8% ppl | Long context |
| Q4_K_M | 18.4 GB | 21.0 GB | 128K | 7.0 | −1.9% ppl | Recommended |
| Q3_K_M | 14.9 GB | 17.5 GB | 128K | 8.6 | −5.4% ppl | Long context |
| Q2_K | 12.8 GB | 15.4 GB | 128K | 10 | −15% ppl | Long context |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.
How to run it
$ llama-server \
-hf deepseek-ai/DeepSeek-R1-Distill-Qwen-32B:Q4_K_M \
-c 8192 -ngl 99
The engine underneath most of the others. Every knob is exposed. More on llama.cpp.
01Download is 18.4 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.
02With no GPU, thread count matters more than clock. Start at one thread per physical core.
03There is room to go to the model's full 128K context on this card.