# Can I run Qwen3-32B on an Arc B580? | What Can I Run AI? > No. Qwen3-32B needs about 23.8 GB at Q4_K_M, and Arc B580 has 12.0 GB usable. What to run instead. Source: https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/arc-b580/ [Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/) / Qwen3-32B on an Arc B580 **No.** Qwen3-32B at Q4\_K\_M needs about 23.8 GB, and the Intel Arc B580 has 12.0 GB usable at a \~4k context. # Can I run Qwen3-32B on an Arc B580? Qwen3-32B at Q4\_K\_M needs about 23.8 GB, and the Intel Arc B580 has 12.0 GB usable. That figure is 12 GB of VRAM on the spec sheet, of which 12.0 GB is usable for weights and cache, at 456 GB/s memory bandwidth. Alibaba’s dense 32B Qwen3, a common “best quality that still fits a 24 GB card” pick. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks. Quant that fits None of Q4, Q8 or FP16 needs 23.8 GB Memory needed 23.8 GB at Q4\_K\_M, \~4k context Usable VRAM 12.0 GB of 12 GB VRAM Est. tokens/sec n/a does not fit [Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/) · [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/arc-b580/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) ## Memory at each quant | Quant | Need vs 12.0 GB | Verdict | | -------- | --------------- | ------------- | | Q4\_K\_M | 23.8 GB | Q4Won’t fit | | Q8\_0 | 41.0 GB | Q8Won’t fit | | FP16 | 76.4 GB | FP16Won’t fit | ## Estimated tokens per second No speed estimate: Qwen3-32B does not fit in the 12.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 Qwen3-32B on an Arc B580 at Q4\_K\_M__ | Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict | | ------- | --------------- | ----------------- | --------- | | 4k | 1.1 GB | 23.8 GB | Won’t fit | | 8k | 2.1 GB | 24.9 GB | Won’t fit | | 16k | 4.3 GB | 27.1 GB | Won’t fit | | 32k | 8.6 GB | 31.4 GB | Won’t fit | | 64k | 17.2 GB | 39.9 GB | Won’t fit | | 128k | 34.4 GB | 57.1 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 14B](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-14b/arc-b580/) instead (11.3 GB at Q4). It is a different model, not a drop-in, but it does fit this machine. * To hold Qwen3-32B at Q4, you need about 23.8 GB. The [GeForce RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3090/) has 24.0 GB usable ($1499 list price). * 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 [Qwen3-32B model page](https://whatcanirunai.vercel.app/models/qwen3-32b/). ## Other hardware for Qwen3-32B * [Qwen3-32B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3060-12gb/)Won’t fit * [Qwen3-32B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3070-ti/)Won’t fit * [Qwen3-32B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3080-10gb/)Won’t fit * [Qwen3-32B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3090/)Tight * [Qwen3-32B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4060-ti-16gb/)Won’t fit * [Qwen3-32B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4070-ti-super/)Won’t fit ## Other models on an Arc B580 * [Qwen3-30B-A3B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/arc-b580/)Won’t fit * [Qwen3.8-27B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/arc-b580/)Won’t fit * [Gemma 3 27B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/arc-b580/)Won’t fit * [Gemma 4 31B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/arc-b580/)Won’t fit * [gpt-oss-20b on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/arc-b580/)Won’t fit * [Mistral Small 24B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/arc-b580/)Won’t fit ## Quick answers Can I run Qwen3-32B on an Arc B580? No. Qwen3-32B at Q4\_K\_M needs about 23.8 GB, and the Intel Arc B580 has 12.0 GB usable. How much VRAM does Qwen3-32B need? About 23.8 GB at Q4\_K\_M, 41.0 GB at Q8\_0 and 76.4 GB at FP16, estimated for a \~4k context. How fast is Qwen3-32B on an Arc B580? 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/