# Can I run gpt-oss-120b on dual RTX 3090s? | What Can I Run?

> No. gpt-oss-120b needs about 82.5 GB at Q4_K_M, and dual RTX 3090s has 48.0 GB usable. What to run instead.

Source: https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/dual-rtx-3090/

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**No.** gpt-oss-120b at Q4\_K\_M needs about 82.5 GB, and dual RTX 3090s have 48.0 GB usable at a \~4k context.

# Can I run gpt-oss-120b on dual RTX 3090s?

gpt-oss-120b at Q4\_K\_M needs about 82.5 GB, and dual RTX 3090s have 48.0 GB usable. Two cards add memory, not decode speed; the speed estimate uses one RTX 3090’s 936 GB/s. OpenAI’s open 120B-class gpt-oss model. It needs large unified memory at Q4, with a much smaller active set per token. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

None of Q4, Q8 or FP16 needs 82.5 GB

Memory needed

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

Usable VRAM

48.0 GB of 48 GB VRAM

Est. tokens/sec

n/a does not fit

[gpt-oss-120b](https://whatcanirunai.vercel.app/models/gpt-oss-120b/) · [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/) · [Multi-GPU](https://whatcanirunai.vercel.app/multi-gpu-for-local-llm/) · [Dual 3090 build](https://whatcanirunai.vercel.app/builds/dual-used-rtx-3090-70b/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3090/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 48.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 82.5 GB         | Q4Won’t fit   |
| Q8\_0    | 143.8 GB        | Q8Won’t fit   |
| FP16     | 269.7 GB        | FP16Won’t fit |

## Estimated tokens per second

No speed estimate: gpt-oss-120b does not fit in the 48.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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-120b on dual RTX 3090s at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 0.2 GB          | 82.5 GB           | Won’t fit |
| 8k      | 0.3 GB          | 82.6 GB           | Won’t fit |
| 16k     | 0.6 GB          | 82.9 GB           | Won’t fit |
| 32k     | 1.2 GB          | 83.5 GB           | Won’t fit |
| 64k     | 2.4 GB          | 84.7 GB           | Won’t fit |
| 128k    | 4.8 GB          | 87.1 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 [R1 Distill 32B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/) instead (23.8 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* To hold gpt-oss-120b at Q4, you need about 82.5 GB. The [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/) has 96.0 GB usable ($6699 Apple list price, 14-inch M5 Max 18C CPU/40C GPU, 128GB/2TB).
* 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-120b model page](https://whatcanirunai.vercel.app/models/gpt-oss-120b/).

## Other hardware for gpt-oss-120b

* [gpt-oss-120b on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/rtx-4090/)Won’t fit
* [gpt-oss-120b on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/rtx-5090/)Won’t fit
* [gpt-oss-120b on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/rtx-3090/)Won’t fit
* [gpt-oss-120b on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/mac-mini-m5-pro-64/)Won’t fit
* [gpt-oss-120b on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/mac-studio-m5-ultra-256/)Fits
* [gpt-oss-120b on a Mac Studio 512GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/mac-studio-m5-ultra-512/)Fits

## Other models on dual RTX 3090s

* [Llama 3.3 70B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/dual-rtx-3090/)Won’t fit
* [R1 Distill 70B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/dual-rtx-3090/)Won’t fit
* [Qwen3-235B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/dual-rtx-3090/)Won’t fit
* [DeepSeek-R1 on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1/dual-rtx-3090/)Won’t fit
* [Mixtral 8x22B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/dual-rtx-3090/)Won’t fit
* [GLM-4.5-Air on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/glm-4.5-air/dual-rtx-3090/)Won’t fit

## Quick answers

Can I run gpt-oss-120b on dual RTX 3090s?

No. gpt-oss-120b at Q4\_K\_M needs about 82.5 GB, and dual RTX 3090s have 48.0 GB usable.

How much VRAM does gpt-oss-120b need?

About 82.5 GB at Q4\_K\_M, 143.8 GB at Q8\_0 and 269.7 GB at FP16, estimated for a \~4k context.

How fast is gpt-oss-120b on dual RTX 3090s?

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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