Home / Can I run it? / gpt-oss-20b / gpt-oss-20b on an RTX 5090

Yes. gpt-oss-20b at Q8_0 needs about 26.6 GB, and the GeForce RTX 5090 has 32.0 GB usable at a ~4k context.

Can I run gpt-oss-20b on an RTX 5090?

gpt-oss-20b at Q8_0 needs about 26.6 GB, and the GeForce RTX 5090 has 32.0 GB usable. That figure is 32 GB of VRAM on the spec sheet, of which 32.0 GB is usable for weights and cache, at 1792 GB/s memory bandwidth. OpenAI’s open 20B-class gpt-oss model. Only a few billion parameters are active per token, so decode can be quicker than a dense 20B. These are estimates from our formula, not measured benchmarks.

Quant that fits

Q8_0 Fits with headroom

Memory needed

26.6 GB at Q8_0, ~4k context

Usable VRAM

32.0 GB of 32 GB VRAM

Est. tokens/sec

up to 398 estimate

gpt-oss-20b · GeForce RTX 5090 · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Estimated tokens per second

Estimate: up to 398 tokens/sec at Q8_0 (upper bound: this is a Mixture-of-Experts, and only the active weights are counted). This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from How we calculate.

Context headroom

At Q8_0, this machine has headroom out to about 128k 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 gpt-oss-20b model page.

Other hardware for gpt-oss-20b

• gpt-oss-20b on an RTX 3060 12GBWon’t fit
• gpt-oss-20b on an RTX 3070 TiWon’t fit
• gpt-oss-20b on an RTX 3080Won’t fit
• gpt-oss-20b on an RTX 3090Fits
• gpt-oss-20b on an RTX 4060 Ti 16GBTight
• gpt-oss-20b on an RTX 4070 Ti SuperTight

Other models on an RTX 5090

• Qwen3-32B on an RTX 5090Fits
• Qwen3-30B-A3B on an RTX 5090Fits
• Qwen3.8-27B on an RTX 5090Fits
• Gemma 3 27B on an RTX 5090Fits
• Gemma 4 31B on an RTX 5090Fits
• Mistral Small 24B on an RTX 5090Tight