# Can I run Qwen3-235B on an RTX 5090? | What Can I Run AI? > No. Qwen3-235B needs about 164.9 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/qwen3-235b-a22b/rtx-5090/ [Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Qwen3-235B](https://whatcanirunai.vercel.app/models/qwen3-235b-a22b/) / Qwen3-235B on an RTX 5090 **No.** Qwen3-235B at Q4\_K\_M needs about 164.9 GB, and the GeForce RTX 5090 has 32.0 GB usable at a \~4k context. # Can I run Qwen3-235B on an RTX 5090? Qwen3-235B at Q4\_K\_M needs about 164.9 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. Qwen3’s large Mixture-of-Experts (235B total, about 22B active). It needs a high-memory unified machine at Q4, not a single consumer GPU. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks. Quant that fits None of Q4, Q8 or FP16 needs 164.9 GB Memory needed 164.9 GB at Q4\_K\_M, \~4k context Usable VRAM 32.0 GB of 32 GB VRAM Est. tokens/sec n/a does not fit [Qwen3-235B-A22B](https://whatcanirunai.vercel.app/models/qwen3-235b-a22b/) · [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 | 164.9 GB | Q4Won’t fit | | Q8\_0 | 288.3 GB | Q8Won’t fit | | FP16 | 541.7 GB | FP16Won’t fit | ## Estimated tokens per second No speed estimate: Qwen3-235B does not fit in the 32.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 Qwen3-235B on an RTX 5090 at Q4\_K\_M__ | Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict | | ------- | --------------- | ----------------- | --------- | | 4k | 0.8 GB | 164.9 GB | Won’t fit | | 8k | 1.6 GB | 165.7 GB | Won’t fit | | 16k | 3.2 GB | 167.3 GB | Won’t fit | | 32k | 6.3 GB | 170.4 GB | Won’t fit | | 64k | 12.6 GB | 176.7 GB | Won’t fit | | 128k | 25.2 GB | 189.3 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 Qwen3-235B at Q4, you need about 164.9 GB. The [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/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. * See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/qwen3/) and the [Qwen3-235B-A22B model page](https://whatcanirunai.vercel.app/models/qwen3-235b-a22b/). ## Other hardware for Qwen3-235B * [Qwen3-235B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/mac-studio-m5-ultra-256/)Fits * [Qwen3-235B on a Mac Studio 512GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/mac-studio-m5-ultra-512/)Fits * [Qwen3-235B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/dual-rtx-3090/)Won’t fit * [Qwen3-235B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/rtx-4090/)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 ## 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 Qwen3-235B on an RTX 5090? No. Qwen3-235B at Q4\_K\_M needs about 164.9 GB, and the GeForce RTX 5090 has 32.0 GB usable. How much VRAM does Qwen3-235B need? About 164.9 GB at Q4\_K\_M, 288.3 GB at Q8\_0 and 541.7 GB at FP16, estimated for a \~4k context. How fast is Qwen3-235B 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/