GPT-oss 120B — VRAM, Speed & Local Setup

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Written by Jakub Rusinowski · Last updated April 1, 2026

OpenAI's first large open-weight release. Dense 120B model at Q4_K_M fits in ~65 GB VRAM — dual RTX 3090 or single A100 80GB. Matches GPT-4o on MMLU (87.4%), MATH (78.9%), and HumanEval (88.5%). The go-to choice for enterprise teams that need GPT-4-class quality without API costs or data privacy concerns.

GPT-oss 120B needs about 71 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters	116.8B (5.1B active)
Context window	131,072
Architecture	Mixture-of-Experts (128 experts, top-4, MXFP4)
Provider	OpenAI
Licence	Apache 2.0
Specified at	Q4_K_M
System RAM	128 GB
Record updated	2026-04-01

Licence

Apache-2.0 — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), at 8K context. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

Quant	Weights	VRAM needed	Est. speed	Fit on 24 GB
Q2_K	38.4 GB	39.8 GB	~31 tok/s (est.)	Offloads to system RAM (slow)
Q3_K_M	49.8 GB	51.2 GB	~27 tok/s (est.)	Offloads to system RAM (slow)
Q4_K_M	70.5 GB	71.9 GB	—	Won't fit
Q5_K_M	82.8 GB	84.2 GB	—	Won't fit
Q6_K	95.8 GB	97.2 GB	—	Won't fit
Q8_0	124.1 GB	125.5 GB	—	Won't fit
F16	233.6 GB	235.0 GB	—	Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the GPT-oss 120B VRAM calculator.

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Recommended GPU

The cheapest catalogued GPU that runs GPT-oss 120B is the AMD Ryzen AI Max+ 395 (96 GB).

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Ryzen AI Max+ 395 Laptop (Strix Halo, up to 128GB)

96 GB VRAM · 120 W board power

2026 prices are volatile — check the current listing.

Check price on Amazon

How to Run GPT-oss 120B

Install Ollama, then run:

ollama run gpt-oss:120b

Weights on Hugging Face: openai/gpt-oss-120b.

Published Benchmark Scores

Quality scores as published by the model's authors or an independent evaluator — not throughput, and not measured by us.

Benchmark	Score	Provenance
MMLU	87.4 / 100 %	reported
MATH	78.9 / 100 %	reported
HumanEval	88.5 / 100 %	reported

Best for: enterprise, privacy sensitive, on premise, general purpose.

Can I Run GPT-oss 120B on My GPU?

Other GPT-OSS Sizes

GPT-OSS 20B — about 13 GB VRAM at Q4_K_M

GPT-oss 120B — Frequently Asked Questions

How much VRAM does GPT-oss 120B need?

About 71 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does GPT-oss 120B run on an RTX 4090 (24 GB)?

No. GPT-oss 120B needs about 71 GB at Q4_K_M, more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.

How do I run GPT-oss 120B locally?

Install Ollama and run `ollama run gpt-oss:120b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does GPT-OSS come in?

GPT-oss 120B (71 GB), GPT-OSS 20B (13 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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