Home / Can I run it? / Gemma 3 4B / Gemma 3 4B on an RTX 4090

Yes. Gemma 3 4B at FP16 needs about 10.9 GB, and the GeForce RTX 4090 has 24.0 GB usable at a ~4k context.

Can I run Gemma 3 4B on an RTX 4090?

Gemma 3 4B at FP16 needs about 10.9 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. 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 with headroom

Memory needed

10.9 GB at FP16, ~4k context

Usable VRAM

24.0 GB of 24 GB VRAM

Est. tokens/sec

70–100 estimate

gemma-3-4b-it · GeForce RTX 4090 · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Estimated tokens per second

Estimate: 70–100 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

• 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 Arc B580Tight
• Gemma 3 4B on a Mac mini 16GBTight

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