HomeCan I run it?gpt-oss-20bgpt-oss-20b on an RTX 4090
Yes. gpt-oss-20b at Q4_K_M needs about 15.6 GB, and the GeForce RTX 4090 has 24.0 GB usable at a ~4k context.
Can I run gpt-oss-20b on an RTX 4090?
gpt-oss-20b at Q4_K_M needs about 15.6 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. 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
- Q4_K_M Fits with headroom
- Memory needed
- 15.6 GB at Q4_K_M, ~4k context
- Usable VRAM
- 24.0 GB of 24 GB VRAM
- Est. tokens/sec
- up to 393 estimate
Memory at each quant
| Quant | Need vs 24.0 GB | Verdict |
|---|---|---|
| Q4_K_M | 15.6 GB | Q4Fits |
| Q8_0 | 26.6 GB | Q8Won’t fit |
| FP16 | 49.1 GB | FP16Won’t fit |
Estimated tokens per second
Estimate: up to 393 tokens/sec at Q4_K_M (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 Q4_K_M, 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.
| Context | KV cache (FP16) | Total at Q4_K_M | Verdict |
|---|---|---|---|
| 4k | 0.1 GB | 15.6 GB | Fits |
| 8k | 0.2 GB | 15.7 GB | Fits |
| 16k | 0.4 GB | 15.9 GB | Fits |
| 32k | 0.8 GB | 16.3 GB | Fits |
| 64k | 1.6 GB | 17.1 GB | Fits |
| 128k | 3.2 GB | 18.7 GB | Fits |
What to do instead
- Q8_0 needs 26.6 GB and does not fit here. The GeForce RTX 5090 has 32.0 GB usable, enough for Q8.
- 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 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
- Mistral Small 24B on an RTX 4090Fits
Quick answers
- Can I run gpt-oss-20b on an RTX 4090?
- Yes. gpt-oss-20b at Q4_K_M needs about 15.6 GB, and the GeForce RTX 4090 has 24.0 GB usable.
- How much VRAM does gpt-oss-20b need?
- About 15.6 GB at Q4_K_M, 26.6 GB at Q8_0 and 49.1 GB at FP16, estimated for a ~4k context.
- How fast is gpt-oss-20b on an RTX 4090?
- Our estimate is up to 393 tokens per second at Q4_K_M, as an upper bound for this MoE. It is not a measured benchmark.
- What context length can I use?
- At Q4_K_M, our KV estimate supports up to about 128k tokens before the cache pushes the total over usable memory.