Home / Can I run it? / Qwen3-8B / Qwen3-8B on an RTX 3080

Yes. Qwen3-8B at Q4_K_M needs about 6.7 GB, and the GeForce RTX 3080 10GB has 10.0 GB usable at a ~4k context.

Can I run Qwen3-8B on an RTX 3080?

Qwen3-8B at Q4_K_M needs about 6.7 GB, and the GeForce RTX 3080 10GB has 10.0 GB usable. That figure is 10 GB of VRAM on the spec sheet, of which 10.0 GB is usable for weights and cache, at 760 GB/s memory bandwidth. Alibaba’s 8B Qwen3. A strong small dense model, and a common upgrade from older 7B–8B weights. These are estimates from our formula, not measured benchmarks.

Quant that fits

Q4_K_M Fits with headroom

Memory needed

6.7 GB at Q4_K_M, ~4k context

Usable VRAM

10.0 GB of 10 GB VRAM

Est. tokens/sec

92–130 estimate

Qwen3-8B · GeForce RTX 3080 10GB · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Estimated tokens per second

Estimate: 92–130 tokens/sec at Q4_K_M. This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from How we calculate.

Context headroom

At Q4_K_M, this machine has headroom out to about 16k tokens under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

From the model’s config. Try other sizes in the VRAM calculator or see the context length guide.

What to do instead

• Q8_0 needs 11.0 GB and does not fit here. The GeForce RTX 3060 12GB has 12.0 GB usable, enough for Q8.
• See the best GPU for this model family and the Qwen3-8B model page.

Other hardware for Qwen3-8B

• Qwen3-8B on an RTX 3060 12GBTight
• Qwen3-8B on an RTX 3070 TiFits
• Qwen3-8B on an RTX 4060 Ti 16GBFits
• Qwen3-8B on an RTX 4070 Ti SuperFits
• Qwen3-8B on an RTX 4090Fits
• Qwen3-8B on an RTX 5070 TiFits

Other models on an RTX 3080

• Qwen3-32B on an RTX 3080Won’t fit
• Qwen3-30B-A3B on an RTX 3080Won’t fit
• Qwen3.8-27B on an RTX 3080Won’t fit
• Gemma 3 27B on an RTX 3080Won’t fit
• Gemma 4 31B on an RTX 3080Won’t fit
• gpt-oss-20b on an RTX 3080Won’t fit