HomeCan I run it?Mistral Small 24BMistral Small 24B on an RTX 5090
Yes, but it is tight. Mistral Small 24B at Q8_0 needs about 30.3 GB, and the GeForce RTX 5090 has 32.0 GB usable at a ~4k context.
Can I run Mistral Small 24B on an RTX 5090?
Mistral Small 24B at Q8_0 needs about 30.3 GB, and the GeForce RTX 5090 has 32.0 GB usable. That figure is 32 GB of VRAM on the spec sheet, of which 32.0 GB is usable for weights and cache, at 1792 GB/s memory bandwidth. Mistral’s 24B Small instruct model, a dense mid-size alternative to the 27B–32B class. These are estimates from our formula, not measured benchmarks.
- Quant that fits
- Q8_0 Fits, short context only
- Memory needed
- 30.3 GB at Q8_0, ~4k context
- Usable VRAM
- 32.0 GB of 32 GB VRAM
- Est. tokens/sec
- 42–60 estimate
Memory at each quant
| Quant | Need vs 32.0 GB | Verdict |
|---|---|---|
| Q4_K_M | 17.7 GB | Q4Fits |
| Q8_0 | 30.3 GB | Q8Tight |
| FP16 | 56.2 GB | FP16Won’t fit |
Estimated tokens per second
Estimate: 42–60 tokens/sec at Q8_0. This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from How we calculate.
Context headroom
At Q8_0, this machine has headroom out to about 8k tokens under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.
| Context | KV cache (FP16) | Total at Q8_0 | Verdict |
|---|---|---|---|
| 4k | 0.7 GB | 30.3 GB | Tight |
| 8k | 1.3 GB | 31.0 GB | Tight |
| 16k | 2.7 GB | 32.4 GB | Won’t fit |
| 32k | 5.4 GB | 35.0 GB | Won’t fit |
| 64k | 10.7 GB | 40.4 GB | Won’t fit |
| 128k | 21.5 GB | 51.1 GB | Won’t fit |
What to do instead
- Run gpt-oss-20b instead (15.6 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
- Keep context short, close other GPU work, and avoid a higher quant. The VRAM calculator shows how the KV cache grows.
- Full hardware list: Mistral-Small-3.2-24B-Instruct-2506. Specs for this machine: GeForce RTX 5090.
Other hardware for Mistral Small 24B
- Mistral Small 24B on an RTX 3060 12GBWon’t fit
- Mistral Small 24B on an RTX 3070 TiWon’t fit
- Mistral Small 24B on an RTX 3080Won’t fit
- Mistral Small 24B on an RTX 3090Fits
- Mistral Small 24B on an RTX 4060 Ti 16GBWon’t fit
- Mistral Small 24B on an RTX 4070 Ti SuperWon’t fit
Other models on an RTX 5090
- Qwen3-32B on an RTX 5090Fits
- Qwen3-30B-A3B on an RTX 5090Fits
- Qwen3.8-27B on an RTX 5090Fits
- Gemma 3 27B on an RTX 5090Fits
- Gemma 4 31B on an RTX 5090Fits
- gpt-oss-20b on an RTX 5090Fits
Quick answers
- Can I run Mistral Small 24B on an RTX 5090?
- Yes, but it is tight. Mistral Small 24B at Q8_0 needs about 30.3 GB, and the GeForce RTX 5090 has 32.0 GB usable.
- How much VRAM does Mistral Small 24B need?
- About 17.7 GB at Q4_K_M, 30.3 GB at Q8_0 and 56.2 GB at FP16, estimated for a ~4k context.
- How fast is Mistral Small 24B on an RTX 5090?
- Our estimate is 42–60 tokens per second at Q8_0. It is not a measured benchmark.
- What context length can I use?
- At Q8_0, our KV estimate supports up to about 8k tokens before the cache pushes the total over usable memory.