# Can I run Mixtral 8x22B on an RTX 5090? | What Can I Run AI? > No. Mixtral 8x22B needs about 99.0 GB at Q4_K_M, and RTX 5090 has 32.0 GB usable. What to run instead. Source: https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/rtx-5090/ [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 an RTX 5090 **No.** Mixtral 8x22B at Q4\_K\_M needs about 99.0 GB, and the GeForce RTX 5090 has 32.0 GB usable at a \~4k context. # Can I run Mixtral 8x22B on an RTX 5090? Mixtral 8x22B at Q4\_K\_M needs about 99.0 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. 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 VRAM 32.0 GB of 32 GB VRAM Est. tokens/sec n/a does not fit [Mixtral-8x22B-Instruct-v0.1](https://whatcanirunai.vercel.app/models/mixtral-8x22b-instruct-v0.1/) · [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 | 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## Estimated tokens per second No speed estimate: Mixtral 8x22B does not fit in the 32.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 an RTX 5090 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/can-i-run/deepseek-r1-distill-qwen-32b/rtx-5090/) 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: [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/). ## 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 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 * [Mixtral 8x22B on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/mac-mini-m5-pro-64/)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 Mixtral 8x22B on an RTX 5090? No. Mixtral 8x22B at Q4\_K\_M needs about 99.0 GB, and the GeForce RTX 5090 has 32.0 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 an RTX 5090? 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/