# Can I run gpt-oss-120b on a Mac Studio 256GB?

> Yes. gpt-oss-120b at Q8_0 needs about 143.8 GB; Mac Studio 256GB has 192.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/mac-studio-m5-ultra-256/

[Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [gpt-oss-120b](https://whatcanirunai.vercel.app/models/gpt-oss-120b/) / gpt-oss-120b on a Mac Studio 256GB

**Yes.** gpt-oss-120b at Q8\_0 needs about 143.8 GB, and the Mac Studio M5 Ultra 256GB has 192.0 GB usable at a \~4k context.

# Can I run gpt-oss-120b on a Mac Studio 256GB?

gpt-oss-120b at Q8\_0 needs about 143.8 GB, and the Mac Studio M5 Ultra 256GB has 192.0 GB usable. That figure is 256 GB of unified memory on the spec sheet, of which 192.0 GB is usable for weights and cache, at 1200 GB/s memory bandwidth. 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

Q8\_0 Fits with headroom

Memory needed

143.8 GB at Q8\_0, \~4k context

Usable unified memory

192.0 GB of 256 GB unified memory

Est. tokens/sec

up to 177 estimate

[gpt-oss-120b](https://whatcanirunai.vercel.app/models/gpt-oss-120b/) · [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/mac-studio-m5-ultra-256/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 192.0 GB | Verdict       |
| -------- | ---------------- | ------------- |
| Q4\_K\_M | 82.5 GB          | Q4Fits        |
| Q8\_0    | 143.8 GB         | Q8Fits        |
| FP16     | 269.7 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **up to 177 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 45–80% unified-memory 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-120b on a Mac Studio 256GB at Q8\_0__
| Context | KV cache (FP16) | Total at Q8\_0 | Verdict |
| ------- | --------------- | -------------- | ------- |
| 4k      | 0.2 GB          | 143.8 GB       | Fits    |
| 8k      | 0.3 GB          | 143.9 GB       | Fits    |
| 16k     | 0.6 GB          | 144.2 GB       | Fits    |
| 32k     | 1.2 GB          | 144.8 GB       | Fits    |
| 64k     | 2.4 GB          | 146.0 GB       | Fits    |
| 128k    | 4.8 GB          | 148.4 GB       | Fits    |

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-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 dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/dual-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 512GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/mac-studio-m5-ultra-512/)Fits

## Other models on a Mac Studio 256GB

* [Llama 3.3 70B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/mac-studio-m5-ultra-256/)Fits
* [R1 Distill 70B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/mac-studio-m5-ultra-256/)Fits
* [Qwen3-235B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/mac-studio-m5-ultra-256/)Fits
* [DeepSeek-R1 on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1/mac-studio-m5-ultra-256/)Won’t fit
* [DeepSeek-V3.2 on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-v3.2/mac-studio-m5-ultra-256/)Won’t fit
* [Mixtral 8x22B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/mac-studio-m5-ultra-256/)Tight

## Quick answers

Can I run gpt-oss-120b on a Mac Studio 256GB?

Yes. gpt-oss-120b at Q8\_0 needs about 143.8 GB, and the Mac Studio M5 Ultra 256GB has 192.0 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 a Mac Studio 256GB?

Our estimate is up to 177 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.

---
Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/