Yes — with 189.7 GB to spare
Mistral Small 3.2 24B at Q4_K_M fits your CPU only · DDR5 8-channel server entirely on the GPU at 8K context, at an estimated 9.7 tokens per second. There is room for its full 128K window.
Fully on GPU
8K context
Q4_K_M · 13.3 GB
Apache 2.0
Released Jun 2025
Vision
Apache-2.0, vision-capable, and the most 24 GB-friendly of the 2025 generalists.
The VRAM budget
weights 13.3 GB
Weights 13.3 GB
KV cache @ 8K 1.25 GB
Runtime overhead 0.6 GB
Free 189.7 GB of 204.8 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 23.4 GB | 25.2 GB | 128K | 5.5 | −0.1% ppl | Long context |
| Q6_K | 18.0 GB | 19.9 GB | 128K | 7.1 | −0.4% ppl | Long context |
| Q5_K_M | 15.6 GB | 17.4 GB | 128K | 8.3 | −0.8% ppl | Long context |
| Q4_K_M | 13.3 GB | 15.1 GB | 128K | 9.7 | −1.9% ppl | Recommended |
| Q3_K_M | 10.7 GB | 12.6 GB | 128K | 12 | −5.4% ppl | Long context |
| Q2_K | 9.2 GB | 11.1 GB | 128K | 14 | −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 mistralai/Mistral-Small-3.2-24B-Instruct-2506:Q4_K_M \
-c 8192 -ngl 99
The engine underneath most of the others. Every knob is exposed. More on llama.cpp.
01Download is 13.3 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.