# Can I run gpt-oss-20b on an RTX 5090? | What Can I Run AI?

> Yes. gpt-oss-20b at Q8_0 needs about 26.6 GB; RTX 5090 has 32.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-5090/

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**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](https://whatcanirunai.vercel.app/method/), 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](https://whatcanirunai.vercel.app/models/gpt-oss-20b/) · [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5090/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 32.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 15.6 GB         | Q4Fits        |
| Q8\_0    | 26.6 GB         | Q8Fits        |
| FP16     | 49.1 GB         | FP16Won’t fit |

## 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](https://whatcanirunai.vercel.app/method/).

## 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.

__Context headroom for gpt-oss-20b on an RTX 5090 at Q8\_0__
| Context | KV cache (FP16) | Total at Q8\_0 | Verdict |
| ------- | --------------- | -------------- | ------- |
| 4k      | 0.1 GB          | 26.6 GB        | Fits    |
| 8k      | 0.2 GB          | 26.7 GB        | Fits    |
| 16k     | 0.4 GB          | 26.9 GB        | Fits    |
| 32k     | 0.8 GB          | 27.3 GB        | Fits    |
| 64k     | 1.6 GB          | 28.1 GB        | Fits    |
| 128k    | 3.2 GB          | 29.7 GB        | Tight   |

From the model’s config. Try other sizes in the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) or see the [context length guide](https://whatcanirunai.vercel.app/context-length/).

## What to do instead

* See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/gpt-oss/) and the [gpt-oss-20b model page](https://whatcanirunai.vercel.app/models/gpt-oss-20b/).

## Other hardware for gpt-oss-20b

* [gpt-oss-20b on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3060-12gb/)Won’t fit
* [gpt-oss-20b on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3070-ti/)Won’t fit
* [gpt-oss-20b on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3080-10gb/)Won’t fit
* [gpt-oss-20b on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3090/)Fits
* [gpt-oss-20b on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4060-ti-16gb/)Tight
* [gpt-oss-20b on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4070-ti-super/)Tight

## Other models on an RTX 5090

* [Qwen3-32B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-5090/)Fits
* [Qwen3-30B-A3B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-5090/)Fits
* [Qwen3.8-27B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-5090/)Fits
* [Gemma 3 27B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-5090/)Fits
* [Gemma 4 31B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-5090/)Fits
* [Mistral Small 24B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-5090/)Tight

## Quick answers

Can I run 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.

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 5090?

Our estimate is up to 398 tokens per second at Q8\_0, as an upper bound for this MoE. It is not a measured benchmark.

What context length can I use?

At Q8\_0, our KV estimate supports up to about 128k tokens before the cache pushes the total over usable memory.

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