# Can I run Mixtral 8x22B on a Mac mini 64GB? > No. Mixtral 8x22B needs about 99.0 GB at Q4_K_M, and Mac mini 64GB has 48.0 GB usable. What to run instead. Source: https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/mac-mini-m5-pro-64/ [Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Mixtral 8x22B](https://whatcanirunai.vercel.app/models/mixtral-8x22b-instruct-v0.1/) / Mixtral 8x22B on a Mac mini 64GB **No.** Mixtral 8x22B at Q4\_K\_M needs about 99.0 GB, and the Mac mini M5 Pro 64GB has 48.0 GB usable at a \~4k context. # Can I run Mixtral 8x22B on a Mac mini 64GB? Mixtral 8x22B at Q4\_K\_M needs about 99.0 GB, and the Mac mini M5 Pro 64GB has 48.0 GB usable. That figure is 64 GB of unified memory on the spec sheet, of which 48.0 GB is usable for weights and cache, at 307 GB/s memory bandwidth. Mixtral 8x22B, a large Mixture-of-Experts. Total weights need a high-memory box; only a fraction is active per token. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks. Quant that fits None of Q4, Q8 or FP16 needs 99.0 GB Memory needed 99.0 GB at Q4\_K\_M, \~4k context Usable unified memory 48.0 GB of 64 GB unified memory Est. tokens/sec n/a does not fit [Mixtral-8x22B-Instruct-v0.1](https://whatcanirunai.vercel.app/models/mixtral-8x22b-instruct-v0.1/) · [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/mac-mini-m5-pro-64/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) ## Memory at each quant | Quant | Need vs 48.0 GB | Verdict | | -------- | --------------- | ------------- | | Q4\_K\_M | 99.0 GB | Q4Won’t fit | | Q8\_0 | 172.8 GB | Q8Won’t fit | | FP16 | 324.4 GB | FP16Won’t fit | ## Estimated tokens per second No speed estimate: Mixtral 8x22B does not fit in the 48.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 Mixtral 8x22B on a Mac mini 64GB at Q4\_K\_M__ | Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict | | ------- | --------------- | ----------------- | --------- | | 4k | 0.9 GB | 99.0 GB | Won’t fit | | 8k | 1.9 GB | 100.0 GB | Won’t fit | | 16k | 3.8 GB | 101.9 GB | Won’t fit | | 32k | 7.5 GB | 105.6 GB | Won’t fit | | 64k | 15.0 GB | 113.1 GB | Won’t fit | | 128k | 30.1 GB | 128.2 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 32B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/) instead (23.8 GB at Q4). It is a different model, not a drop-in, but it does fit this machine. * To hold Mixtral 8x22B at Q4, you need about 99.0 GB. The [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/mac-studio-m5-ultra-256/) has 192.0 GB usable ($9499 Apple list price, M5 Ultra 30C CPU/64C GPU, 256GB/1TB). * CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure. * Full hardware list: [Mixtral-8x22B-Instruct-v0.1](https://whatcanirunai.vercel.app/models/mixtral-8x22b-instruct-v0.1/). Specs for this machine: [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/). ## Other hardware for Mixtral 8x22B * [Mixtral 8x22B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/rtx-4090/)Won’t fit * [Mixtral 8x22B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/rtx-5090/)Won’t fit * [Mixtral 8x22B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/dual-rtx-3090/)Won’t fit * [Mixtral 8x22B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/mac-studio-m5-ultra-256/)Tight ## Other models on a Mac mini 64GB * [Llama 3.3 70B on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/mac-mini-m5-pro-64/)Won’t fit * [R1 Distill 70B on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/mac-mini-m5-pro-64/)Won’t fit * [gpt-oss-120b on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/mac-mini-m5-pro-64/)Won’t fit * [Qwen3-235B on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/mac-mini-m5-pro-64/)Won’t fit * [GLM-4.5-Air on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/glm-4.5-air/mac-mini-m5-pro-64/)Won’t fit ## Quick answers Can I run Mixtral 8x22B on a Mac mini 64GB? No. Mixtral 8x22B at Q4\_K\_M needs about 99.0 GB, and the Mac mini M5 Pro 64GB has 48.0 GB usable. How much VRAM does Mixtral 8x22B need? About 99.0 GB at Q4\_K\_M, 172.8 GB at Q8\_0 and 324.4 GB at FP16, estimated for a \~4k context. How fast is Mixtral 8x22B on a Mac mini 64GB? 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/