# Can I run Mistral Small 24B on an RTX 3060 12GB? > No. Mistral Small 24B needs about 17.7 GB at Q4_K_M, and RTX 3060 12GB has 12.0 GB usable. What to run instead. Source: https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-3060-12gb/ [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 3060 12GB **No.** Mistral Small 24B at Q4\_K\_M needs about 17.7 GB, and the GeForce RTX 3060 12GB has 12.0 GB usable at a \~4k context. # Can I run Mistral Small 24B on an RTX 3060 12GB? Mistral Small 24B at Q4\_K\_M needs about 17.7 GB, and the GeForce RTX 3060 12GB has 12.0 GB usable. That figure is 12 GB of VRAM on the spec sheet, of which 12.0 GB is usable for weights and cache, at 360 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 12.0 GB of 12 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 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3060-12gb/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) ## Memory at each quant | Quant | Need vs 12.0 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tokens per second No speed estimate: Mistral Small 24B does not fit in the 12.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 3060 12GB 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 [R1 Distill 14B](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-14b/rtx-3060-12gb/) instead (11.3 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)). * 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 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/). ## Other hardware for Mistral Small 24B * [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 * [Mistral Small 24B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-4090/)Fits ## Other models on an RTX 3060 12GB * [Qwen3-32B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3060-12gb/)Won’t fit * [Qwen3-30B-A3B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3060-12gb/)Won’t fit * [Qwen3.8-27B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-3060-12gb/)Won’t fit * [Gemma 3 27B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-3060-12gb/)Won’t fit * [Gemma 4 31B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3060-12gb/)Won’t fit * [gpt-oss-20b on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3060-12gb/)Won’t fit ## Quick answers Can I run Mistral Small 24B on an RTX 3060 12GB? No. Mistral Small 24B at Q4\_K\_M needs about 17.7 GB, and the GeForce RTX 3060 12GB has 12.0 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 3060 12GB? 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/