# Can I run Mixtral 8x22B on dual RTX 3090s? | What Can I Run? > No. Mixtral 8x22B needs about 99.0 GB at Q4_K_M, and dual RTX 3090s has 48.0 GB usable. What to run instead. Source: https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/dual-rtx-3090/ [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 dual RTX 3090s **No.** Mixtral 8x22B at Q4\_K\_M needs about 99.0 GB, and dual RTX 3090s have 48.0 GB usable at a \~4k context. # Can I run Mixtral 8x22B on dual RTX 3090s? Mixtral 8x22B at Q4\_K\_M needs about 99.0 GB, and dual RTX 3090s have 48.0 GB usable. Two cards add memory, not decode speed; the speed estimate uses one RTX 3090’s 936 GB/s. 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 48.0 GB of 48 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 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/) · [Multi-GPU](https://whatcanirunai.vercel.app/multi-gpu-for-local-llm/) · [Dual 3090 build](https://whatcanirunai.vercel.app/builds/dual-used-rtx-3090-70b/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3090/) · [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## Estimated tokens per second No speed estimate: Mixtral 8x22B does not fit in the 48.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 dual RTX 3090s 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: [GeForce RTX 3090 (used as a pair)](https://whatcanirunai.vercel.app/hardware/rtx-3090/). ## 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 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 dual RTX 3090s * [Llama 3.3 70B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/dual-rtx-3090/)Won’t fit * [R1 Distill 70B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/dual-rtx-3090/)Won’t fit * [gpt-oss-120b on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/dual-rtx-3090/)Won’t fit * [Qwen3-235B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/dual-rtx-3090/)Won’t fit * [DeepSeek-R1 on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1/dual-rtx-3090/)Won’t fit * [GLM-4.5-Air on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/glm-4.5-air/dual-rtx-3090/)Won’t fit ## Quick answers Can I run Mixtral 8x22B on dual RTX 3090s? No. Mixtral 8x22B at Q4\_K\_M needs about 99.0 GB, and dual RTX 3090s have 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 dual RTX 3090s? 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/