# 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; RTX 5090 has 32.0 GB usable. Estimated tokens/sec and context headroom. Source: https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-5090/ [Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Mistral Small 24B](https://whatcanirunai.vercel.app/models/mistral-small-3.2-24b-instruct-2506/) / 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 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](https://whatcanirunai.vercel.app/method/), 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 [Mistral-Small-3.2-24B-Instruct-2506](https://whatcanirunai.vercel.app/models/mistral-small-3.2-24b-instruct-2506/) · [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5090/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) ## 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](https://whatcanirunai.vercel.app/method/). ## 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 headroom for Mistral Small 24B on an RTX 5090 at Q8\_0__ | 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 | From the model’s config. Try other sizes in the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) or see the [context length guide](https://whatcanirunai.vercel.app/context-length/). ## What to do instead * Run [gpt-oss-20b](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-5090/) 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](https://whatcanirunai.vercel.app/vram-calculator/) shows how the KV cache grows. * Full hardware list: [Mistral-Small-3.2-24B-Instruct-2506](https://whatcanirunai.vercel.app/models/mistral-small-3.2-24b-instruct-2506/). Specs for this machine: [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/). ## Other hardware for Mistral Small 24B * [Mistral Small 24B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-3060-12gb/)Won’t fit * [Mistral Small 24B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-3070-ti/)Won’t fit * [Mistral Small 24B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-3080-10gb/)Won’t fit * [Mistral Small 24B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-3090/)Fits * [Mistral Small 24B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-4060-ti-16gb/)Won’t fit * [Mistral Small 24B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-4070-ti-super/)Won’t fit ## Other models on an RTX 5090 * [Qwen3-32B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-5090/)Fits * [Qwen3-30B-A3B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-5090/)Fits * [Qwen3.8-27B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-5090/)Fits * [Gemma 3 27B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-5090/)Fits * [Gemma 4 31B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-5090/)Fits * [gpt-oss-20b on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-5090/)Fits ## 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. --- Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/