# Can I run gpt-oss-20b on an RTX 3070 Ti? | What Can I Run AI

> No. gpt-oss-20b needs about 15.6 GB at Q4_K_M, and RTX 3070 Ti has 8.0 GB usable. What to run instead.

Source: https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3070-ti/

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**No.** gpt-oss-20b at Q4\_K\_M needs about 15.6 GB, and the GeForce RTX 3070 Ti has 8.0 GB usable at a \~4k context.

# Can I run gpt-oss-20b on an RTX 3070 Ti?

gpt-oss-20b at Q4\_K\_M needs about 15.6 GB, and the GeForce RTX 3070 Ti has 8.0 GB usable. That figure is 8 GB of VRAM on the spec sheet, of which 8.0 GB is usable for weights and cache, at 608 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

None of Q4, Q8 or FP16 needs 15.6 GB

Memory needed

15.6 GB at Q4\_K\_M, \~4k context

Usable VRAM

8.0 GB of 8 GB VRAM

Est. tokens/sec

n/a does not fit

[gpt-oss-20b](https://whatcanirunai.vercel.app/models/gpt-oss-20b/) · [GeForce RTX 3070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3070-ti/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3070-ti/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 8.0 GB | Verdict       |
| -------- | -------------- | ------------- |
| Q4\_K\_M | 15.6 GB        | Q4Won’t fit   |
| Q8\_0    | 26.6 GB        | Q8Won’t fit   |
| FP16     | 49.1 GB        | FP16Won’t fit |

## Estimated tokens per second

No speed estimate: gpt-oss-20b does not fit in the 8.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.

## Context headroom

Even at a short \~4k context, the weights do not fit. Extending context would only increase the gap.

__Context headroom for gpt-oss-20b on an RTX 3070 Ti at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 0.1 GB          | 15.6 GB           | Won’t fit |
| 8k      | 0.2 GB          | 15.7 GB           | Won’t fit |
| 16k     | 0.4 GB          | 15.9 GB           | Won’t fit |
| 32k     | 0.8 GB          | 16.3 GB           | Won’t fit |
| 64k     | 1.6 GB          | 17.1 GB           | Won’t fit |
| 128k    | 3.2 GB          | 18.7 GB           | Won’t fit |

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

* Run [Qwen3-8B](https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rtx-3070-ti/) instead (6.7 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* To hold gpt-oss-20b at Q4, you need about 15.6 GB. The [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4060-ti-16gb/) has 16.0 GB usable ($499 list price).
* CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure.
* 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 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
* [gpt-oss-20b on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4090/)Fits

## Other models on an RTX 3070 Ti

* [Qwen3-32B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3070-ti/)Won’t fit
* [Qwen3-30B-A3B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3070-ti/)Won’t fit
* [Qwen3.8-27B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-3070-ti/)Won’t fit
* [Gemma 3 27B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-3070-ti/)Won’t fit
* [Gemma 4 31B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3070-ti/)Won’t fit
* [Mistral Small 24B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-3070-ti/)Won’t fit

## Quick answers

Can I run gpt-oss-20b on an RTX 3070 Ti?

No. gpt-oss-20b at Q4\_K\_M needs about 15.6 GB, and the GeForce RTX 3070 Ti has 8.0 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 3070 Ti?

We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow.

What context length can I use?

The weights already do not fit, so longer context is not the limiting factor.

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