Home / Can I run it? / Gemma 3 4B / Gemma 3 4B on an Arc B580

Yes, but it is tight. Gemma 3 4B at FP16 needs about 10.9 GB, and the Intel Arc B580 has 12.0 GB usable at a ~4k context.

Can I run Gemma 3 4B on an Arc B580?

Gemma 3 4B at FP16 needs about 10.9 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. Google’s small Gemma 3 instruct model, a good low-VRAM option with a different flavor from Llama 3.2 3B. These are estimates from our formula, not measured benchmarks.

Quant that fits

FP16 Fits, short context only

Memory needed

10.9 GB at FP16, ~4k context

Usable VRAM

12.0 GB of 12 GB VRAM

Est. tokens/sec

32–45 estimate

gemma-3-4b-it · Intel Arc B580 · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Estimated tokens per second

Estimate: 32–45 tokens/sec at FP16. This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from How we calculate.

Context headroom

We do not have a published KV config for this model, so the memory need is the ~4k-context estimate only. Use the VRAM calculator to explore longer chats.

What to do instead

• Run Llama 3.2 3B instead (3.2 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
• Keep context short, close other GPU work, and avoid a higher quant. The VRAM calculator shows how the KV cache grows.
• See the best GPU for this model family and the gemma-3-4b-it model page.

Other hardware for Gemma 3 4B

• Gemma 3 4B on an RTX 3060 12GBTight
• Gemma 3 4B on an RTX 3070 TiFits
• Gemma 3 4B on an RTX 4060 Ti 16GBFits
• Gemma 3 4B on an RTX 4090Fits
• Gemma 3 4B on a Mac mini 16GBTight

Other models on an Arc B580

• Qwen3-32B on an Arc B580Won’t fit
• Qwen3-30B-A3B on an Arc B580Won’t fit
• Qwen3.8-27B on an Arc B580Won’t fit
• Gemma 3 27B on an Arc B580Won’t fit
• Gemma 4 31B on an Arc B580Won’t fit
• gpt-oss-20b on an Arc B580Won’t fit