# Can I run Mistral Small 24B on an RTX 5070 Ti? > No. Mistral Small 24B needs about 17.7 GB at Q4_K_M, and RTX 5070 Ti has 16.0 GB usable. What to run instead. Source: https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-5070-ti/ [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 5070 Ti **No.** Mistral Small 24B at Q4\_K\_M needs about 17.7 GB, and the GeForce RTX 5070 Ti has 16.0 GB usable at a \~4k context. # Can I run Mistral Small 24B on an RTX 5070 Ti? Mistral Small 24B at Q4\_K\_M needs about 17.7 GB, and the GeForce RTX 5070 Ti has 16.0 GB usable. That figure is 16 GB of VRAM on the spec sheet, of which 16.0 GB is usable for weights and cache, at 896 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 None of Q4, Q8 or FP16 needs 17.7 GB Memory needed 17.7 GB at Q4\_K\_M, \~4k context Usable VRAM 16.0 GB of 16 GB VRAM Est. tokens/sec n/a does not fit [Mistral-Small-3.2-24B-Instruct-2506](https://whatcanirunai.vercel.app/models/mistral-small-3.2-24b-instruct-2506/) · [GeForce RTX 5070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-5070-ti/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5070-ti/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) ## Memory at each quant | Quant | Need vs 16.0 GB | Verdict | | -------- | --------------- | ------------- | | Q4\_K\_M | 17.7 GB | Q4Won’t fit | | Q8\_0 | 30.3 GB | Q8Won’t fit | | FP16 | 56.2 GB | FP16Won’t fit | ## Estimated tokens per second No speed estimate: Mistral Small 24B does not fit in the 16.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload. ## Context headroom Even at a short \~4k context, the weights do not fit. Extending context would only increase the gap. __Context headroom for Mistral Small 24B on an RTX 5070 Ti at Q4\_K\_M__ | Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict | | ------- | --------------- | ----------------- | --------- | | 4k | 0.7 GB | 17.7 GB | Won’t fit | | 8k | 1.3 GB | 18.4 GB | Won’t fit | | 16k | 2.7 GB | 19.8 GB | Won’t fit | | 32k | 5.4 GB | 22.4 GB | Won’t fit | | 64k | 10.7 GB | 27.8 GB | Won’t fit | | 128k | 21.5 GB | 38.5 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-5070-ti/) instead (15.6 GB at Q4). It is a different model, not a drop-in, but it does fit this machine. * To hold Mistral Small 24B at Q4, you need about 17.7 GB. The [Mac mini M6 24GB](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/mac-mini-m6-24/) has 18.0 GB usable ($1299 Apple list price, M6 12C CPU/12C GPU, 24GB/512GB (MHQM4LL/A)). * Our estimate is only 1.7 GB over this machine’s usable memory, so a tighter quant or a shorter context may work in practice. We still mark it as a no under [the published formula](https://whatcanirunai.vercel.app/method/). * CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure. * 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 5070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-5070-ti/). ## 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 5070 Ti * [Qwen3-32B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-5070-ti/)Won’t fit * [Qwen3-30B-A3B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-5070-ti/)Won’t fit * [Qwen3.8-27B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-5070-ti/)Won’t fit * [Gemma 3 27B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-5070-ti/)Won’t fit * [Gemma 4 31B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-5070-ti/)Won’t fit * [gpt-oss-20b on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-5070-ti/)Tight ## Quick answers Can I run Mistral Small 24B on an RTX 5070 Ti? No. Mistral Small 24B at Q4\_K\_M needs about 17.7 GB, and the GeForce RTX 5070 Ti has 16.0 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 5070 Ti? We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow. What context length can I use? The weights already do not fit, so longer context is not the limiting factor. --- Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/