# Can I run Mistral Small 24B on a Mac mini 24GB? > Yes, but it is tight. Mistral Small 24B at Q4_K_M needs about 17.7 GB; Mac mini 24GB has 18.0 GB usable. Estimated tokens/sec and context headroom. Source: https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/mac-mini-m6-24/ [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 a Mac mini 24GB **Yes, but it is tight.** Mistral Small 24B at Q4\_K\_M needs about 17.7 GB, and the Mac mini M6 24GB has 18.0 GB usable at a \~4k context. # Can I run Mistral Small 24B on a Mac mini 24GB? Mistral Small 24B at Q4\_K\_M needs about 17.7 GB, and the Mac mini M6 24GB has 18.0 GB usable. That figure is 24 GB of unified memory on the spec sheet, of which 18.0 GB is usable for weights and cache, at 170 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 Q4\_K\_M Fits, short context only Memory needed 17.7 GB at Q4\_K\_M, \~4k context Usable unified memory 18.0 GB of 24 GB unified memory Est. tokens/sec 5–9 estimate [Mistral-Small-3.2-24B-Instruct-2506](https://whatcanirunai.vercel.app/models/mistral-small-3.2-24b-instruct-2506/) · [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/mac-mini-m6-24/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) ## Memory at each quant | Quant | Need vs 18.0 GB | Verdict | | -------- | --------------- | ------------- | | Q4\_K\_M | 17.7 GB | Q4Tight | | Q8\_0 | 30.3 GB | Q8Won’t fit | | FP16 | 56.2 GB | FP16Won’t fit | ## Estimated tokens per second Estimate: **5–9 tokens/sec** at Q4\_K\_M. This is a bandwidth-derived range, not a bench. Efficiency is a 45–80% unified-memory band from [How we calculate](https://whatcanirunai.vercel.app/method/). ## Context headroom At Q4\_K\_M, this machine has headroom out to about **4k 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 a Mac mini 24GB at Q4\_K\_M__ | Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict | | ------- | --------------- | ----------------- | --------- | | 4k | 0.7 GB | 17.7 GB | Tight | | 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/mac-mini-m6-24/) instead (15.6 GB at Q4). It is a different model, not a drop-in, but it does fit this machine. * Q8\_0 needs 30.3 GB and does not fit here. The [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/) has 32.0 GB usable, enough for Q8. * 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: [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/). ## 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 a Mac mini 24GB * [Qwen3-32B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/mac-mini-m6-24/)Won’t fit * [Qwen3-30B-A3B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/mac-mini-m6-24/)Won’t fit * [Qwen3.8-27B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/mac-mini-m6-24/)Won’t fit * [Gemma 3 27B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/mac-mini-m6-24/)Won’t fit * [Gemma 4 31B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/mac-mini-m6-24/)Won’t fit * [gpt-oss-20b on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/mac-mini-m6-24/)Fits ## Quick answers Can I run Mistral Small 24B on a Mac mini 24GB? Yes, but it is tight. Mistral Small 24B at Q4\_K\_M needs about 17.7 GB, and the Mac mini M6 24GB has 18.0 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 a Mac mini 24GB? Our estimate is 5–9 tokens per second at Q4\_K\_M. It is not a measured benchmark. What context length can I use? At Q4\_K\_M, our KV estimate supports up to about 4k 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/