Only with CPU offload
Mistral Small 3.2 24B at Q4_K_M needs 15.1 GB but only 10.6 GB is addressable, so about 34% of the layers would stream from system RAM at roughly 60 GB/s. Expect around 6.4 tokens per second — usable for batch work, painful for chat.
66% 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
Over budget 4.5 GB past 10.6 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 23.4 GB | 25.2 GB | — | ~2.3 | −0.1% ppl | 14.6 GB over |
| Q6_K | 18.0 GB | 19.9 GB | — | ~3.5 | −0.4% ppl | 9.3 GB over |
| Q5_K_M | 15.6 GB | 17.4 GB | — | ~4.6 | −0.8% ppl | 6.8 GB over |
| Q4_K_M | 13.3 GB | 15.1 GB | — | ~6.4 | −1.9% ppl | 4.5 GB over |
| Q3_K_M | 10.7 GB | 12.6 GB | — | ~12 | −5.4% ppl | 2.0 GB over |
| Q2_K | 9.2 GB | 11.1 GB | 5K | ~23 | −15% ppl | 0.5 GB over |
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 26
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.
02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.
03If it falls back to CPU silently, drop the context first, then step down a quantisation.