gpt-oss 120B memory requirements

116.8B parameters — 60.8 GiB of weights at FP16, 58.5 GiB at Q4_K_M, and 72.0 KiB of KV cache per token on top. Solved against 22 real accelerators; 5 of them can hold it.

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Parameters

116.8B

116,829,156,672 exactly

Weights, Q4_K_M

58.5 GiB

60.8 GiB unquantized

KV cache

72.0 KiB

per token · 36L × 8 KV × 64

Smallest card (Q4)

80 GB

H100 80GB (SXM5)

Weights at each precision

Precision	Bits/weight	Weights	Source	Notes
As released	4.47	60.8 GiB	measured file	Unquantized. Two bytes per parameter, exactly.
Q8_0	4.34	59.0 GiB	measured file	Effectively lossless; the usual reference point for quantized quality.
Q6_K	4.33	58.9 GiB	measured file	Quality loss is hard to measure on most benchmarks.
Q5_K_M	4.31	58.6 GiB	measured file	A middle point when Q4 fits with too little room for context.
Q4_K_M	4.30	58.5 GiB	measured file	The default choice for local inference — the best size/quality knee.
Q3_K_M	4.29	58.3 GiB	measured file	Measurable quality loss. Worth it only to make a model fit at all.

This release is already quantized. The published checkpoint measures 60.8 GiB against the 217.6 GiB that two bytes per parameter would imply, so the FP16 row above describes a file its authors never shipped. The measurement is the sum of the shard sizes in openai/gpt-oss-120b, not a figure derived from the dtype metadata — which reports this model's 4-bit tensors in a way that cannot be told apart from 8-bit ones.

Every accelerator

Largest quant that fits with at least 4k of context, the context it leaves, and the bandwidth ceiling on decode speed. Each row links to the worked page for that pairing.

Accelerator	Memory	Bandwidth	Best quant	Max context	Ceiling
Apple M3 Ultra (512GB)	512 GB	819 GB/s	FP16 / BF16	131,072	234 tok/s
H200 141GB (SXM)	141 GB	4800 GB/s	FP16 / BF16	131,072	1373 tok/s
Apple M4 Max (128GB)	128 GB	546 GB/s	FP16 / BF16	131,072	156 tok/s
H100 80GB (SXM5)	80 GB	3350 GB/s	FP16 / BF16	131,072	958 tok/s
A100 80GB (SXM)	80 GB	2039 GB/s	FP16 / BF16	131,072	583 tok/s
Jetson AGX Orin (64GB)	64 GB	204.8 GB/s	no fit	—	—
RTX 6000 Ada Generation	48 GB	960 GB/s	no fit	—	—
L40S	48 GB	864 GB/s	no fit	—	—
Apple M4 Pro (48GB)	48 GB	273 GB/s	no fit	—	—
A100 40GB (SXM)	40 GB	1555 GB/s	no fit	—	—
GeForce RTX 5090	32 GB	1792 GB/s	no fit	—	—
GeForce RTX 4090	24 GB	1008 GB/s	no fit	—	—
Radeon RX 7900 XTX	24 GB	960 GB/s	no fit	—	—
GeForce RTX 3090	24 GB	936 GB/s	no fit	—	—
Apple M4 (24GB)	24 GB	120 GB/s	no fit	—	—
GeForce RTX 5080	16 GB	960 GB/s	no fit	—	—
GeForce RTX 5070 Ti	16 GB	896 GB/s	no fit	—	—
GeForce RTX 4080 SUPER	16 GB	736 GB/s	no fit	—	—
GeForce RTX 4070 Ti SUPER	16 GB	672 GB/s	no fit	—	—
GeForce RTX 4060 Ti 16GB	16 GB	288 GB/s	no fit	—	—
GeForce RTX 3060 12GB	12 GB	360 GB/s	no fit	—	—
Jetson Orin Nano Super (8GB)	8 GB	102 GB/s	no fit	—	—

Questions about this model

How much VRAM does gpt-oss 120B need?

60.8 GiB for the weights at FP16 and 58.5 GiB at Q4_K_M, from an exact count of 116,829,156,672 parameters. On top of that, the KV cache costs 72.0 KiB per token of context — 293 MiB at 8k and 1.1 GiB at 32k, less than linear because 18 of 36 layers stop growing at a 128-token window.

What is the smallest GPU that runs gpt-oss 120B?

Of the 22 accelerators here, the smallest that holds Q4_K_M weights is the H100 80GB (SXM5) at 80 GB — 58.5 GiB of weights against 73.6 GiB usable, leaving room for 128k tokens. For unquantized weights you need at least an H100 80GB (SXM5).

Why is the KV cache for gpt-oss 120B the size it is?

Because it stores one key and one value vector per token, per layer: 36 layers × 8 KV heads × 64 dimensions × 2 (K and V) × 2 bytes = 72.0 KiB per token. Grouped-query attention shares those 8 KV heads across 64 query heads, cutting the cache by 8× against multi-head attention. None of this can be read off the parameter count.

Compare with

Method and limits. The parameter count and every architecture figure on this page are read from the model's own published config and the Hub's index over its tensor shapes, fetched 2026-08-07 — nothing here is recalled from a model's name. Weight bytes are the size of a real published file wherever one exists and the parameter count times the published llama.cpp bits-per-weight where it does not; every row says which. Speed figures are roofline bounds — memory bandwidth divided by bytes read per token — not benchmarks. Nothing on this page is written by a language model.