# Can I run Qwen3-30B-A3B on an Arc B580? | What Can I Run AI? > No. Qwen3-30B-A3B needs about 22.3 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/qwen3-30b-a3b/arc-b580/ [Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Qwen3-30B-A3B](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b/) / Qwen3-30B-A3B on an Arc B580 **No.** Qwen3-30B-A3B at Q4\_K\_M needs about 22.3 GB, and the Intel Arc B580 has 12.0 GB usable at a \~4k context. # Can I run Qwen3-30B-A3B on an Arc B580? Qwen3-30B-A3B at Q4\_K\_M needs about 22.3 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. A Qwen3 Mixture-of-Experts with 30B total weights and about 3B active per token, so it needs 30B-class memory but can decode closer to a small dense model. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks. Quant that fits None of Q4, Q8 or FP16 needs 22.3 GB Memory needed 22.3 GB at Q4\_K\_M, \~4k context Usable VRAM 12.0 GB of 12 GB VRAM Est. tokens/sec n/a does not fit [Qwen3-30B-A3B](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b/) · [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 | 22.3 GB | Q4Won’t fit | | Q8\_0 | 38.3 GB | Q8Won’t fit | | FP16 | 71.2 GB | FP16Won’t fit | ## Estimated tokens per second No speed estimate: Qwen3-30B-A3B does not fit in the 12.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 Qwen3-30B-A3B on an Arc B580 at Q4\_K\_M__ | Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict | | ------- | --------------- | ----------------- | --------- | | 4k | 0.4 GB | 22.3 GB | Won’t fit | | 8k | 0.8 GB | 22.7 GB | Won’t fit | | 16k | 1.6 GB | 23.5 GB | Won’t fit | | 32k | 3.2 GB | 25.1 GB | Won’t fit | | 64k | 6.4 GB | 28.3 GB | Won’t fit | | 128k | 12.9 GB | 34.8 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 Qwen3-30B-A3B at Q4, you need about 22.3 GB. The [GeForce RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/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 [Qwen3-30B-A3B model page](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b/). ## Other hardware for Qwen3-30B-A3B * [Qwen3-30B-A3B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3060-12gb/)Won’t fit * [Qwen3-30B-A3B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3070-ti/)Won’t fit * [Qwen3-30B-A3B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3080-10gb/)Won’t fit * [Qwen3-30B-A3B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3090/)Tight * [Qwen3-30B-A3B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-4060-ti-16gb/)Won’t fit * [Qwen3-30B-A3B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-4070-ti-super/)Won’t fit ## Other models on an Arc B580 * [Qwen3-32B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/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 Qwen3-30B-A3B on an Arc B580? No. Qwen3-30B-A3B at Q4\_K\_M needs about 22.3 GB, and the Intel Arc B580 has 12.0 GB usable. How much VRAM does Qwen3-30B-A3B need? About 22.3 GB at Q4\_K\_M, 38.3 GB at Q8\_0 and 71.2 GB at FP16, estimated for a \~4k context. How fast is Qwen3-30B-A3B 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/