Smaller alternatives
Models that need less memory than Mistral Small 3.2 24B and score about as well or better.
Mistral Small 3.2 24B
Vision
Mistral AI · 24B · Dense
Mistral Small 3.2 24B is a 24B model from Mistral AI with a 128K-token context. At Q4_K_M with an 8K context it needs about 14.9 GB; 20 GB leaves room for longer chats.
Quantization options
| Quant | Bits | Memory | Quality | On your hardware |
|---|---|---|---|---|
| Q2_K | 3.16 | — | Noticeably worse | … |
| Q3_K_M | 4 | — | Some loss | … |
| Q4_K_M | 4.89 | — | Good | … |
| Q5_K_M | 5.7 | — | Good | … |
| Q6_K | 6.56 | — | Near original | … |
| Q8_0 | 8.5 | — | Near original | … |
| F16 | 16 | — | Original | … |
Memory by context length
At Q4_K_M. The context cache grows with every token the model keeps in mind.
| Context | Context cache | Total |
|---|---|---|
| 4K | 0.6 GB | 14.3 GB |
| 8K | 1.3 GB | 14.9 GB |
| 32K | 5 GB | 18.6 GB |
| 128K | 20 GB | 33.6 GB |
Next steps
Compare it with
Scored models of a similar size, side by side on your device.
Least hardware that runs it well
Desktop devices with the least memory that give Mistral Small 3.2 24B grade A or S at Q4_K_M.
Can I run Mistral Small 3.2 24B locally?
Can I run Mistral Small 3.2 24B locally?
Yes, if your GPU or Mac has about 14.9 GB free for it at Q4_K_M. Open this page on that computer to see the grade for your exact hardware, then start it with llama.cpp, Ollama or LM Studio.
How much memory does Mistral Small 3.2 24B need?
About 14.9 GB at Q4_K_M with an 8K context and 24.9 GB at Q8_0. Lower quantizations fit smaller cards with some loss in quality; longer contexts add to the total.