Home / Can I run it? / Qwen3-8B / Qwen3-8B on an RTX 4090

Yes. Qwen3-8B at FP16 needs about 19.8 GB, and the GeForce RTX 4090 has 24.0 GB usable at a ~4k context.

Can I run Qwen3-8B on an RTX 4090?

Qwen3-8B at FP16 needs about 19.8 GB, and the GeForce RTX 4090 has 24.0 GB usable. That figure is 24 GB of VRAM on the spec sheet, of which 24.0 GB is usable for weights and cache, at 1008 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

FP16 Fits with headroom

Memory needed

19.8 GB at FP16, ~4k context

Usable VRAM

24.0 GB of 24 GB VRAM

Est. tokens/sec

37–52 estimate

Qwen3-8B · GeForce RTX 4090 · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Estimated tokens per second

Estimate: 37–52 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

At FP16, 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

• 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 3080Fits
• Qwen3-8B on an RTX 4060 Ti 16GBFits
• Qwen3-8B on an RTX 4070 Ti SuperFits
• Qwen3-8B on an RTX 5070 TiFits

Other models on an RTX 4090

• Qwen3-32B on an RTX 4090Tight
• Qwen3-30B-A3B on an RTX 4090Tight
• Qwen3.8-27B on an RTX 4090Fits
• Gemma 3 27B on an RTX 4090Fits
• Gemma 4 31B on an RTX 4090Tight
• gpt-oss-20b on an RTX 4090Fits