gpt-oss-120b TPS calculator

Open weights OpenAI 116.8B parameters August 2025

Each card below is assessed against this model at the context length and minimum quality you choose. Speed is an estimate for a single request, calculated from the card's memory bandwidth and the size of the model once compressed.

Calculated for this model

38 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX PRO 5000 72 GB Blackwell

72 GB · IQ4_XS · 11.9 tok/s

Fastest card

H100 NVL 94 GB

33.0 tok/s · 94 GB

Which GPUs can run gpt-oss-120b?

Set the inputs, read the answer

Context length tokens

A longer conversation needs more memory, which can push this model off smaller cards.

Minimum quality

Hides cards that would only fit the model by compressing it below this point.

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Card memory

Also show cards that cannot run it

38 cards match

Calculating

					Needs	Quantisation	Fit
33.0 tok/s 20–53 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	71.4 GB	Q4_K_M	Tight
29.0 tok/s 17–46 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	125.8 GB	Q8_0	Comfortable
29.0 tok/s 17–46 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	125.8 GB	Q8_0	Comfortable
28.1 tok/s 17–45 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	71.4 GB	Q4_K_M	Tight
28.1 tok/s 17–45 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	71.4 GB	Q4_K_M	Tight
28.1 tok/s 17–45 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	71.4 GB	Q4_K_M	Tight
26.9 tok/s 16–43 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	98.6 GB	Q6_K	Tight
23.2 tok/s 14–37 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	125.8 GB	Q8_0	Comfortable
23.2 tok/s 14–37 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	125.8 GB	Q8_0	Comfortable
21.9 tok/s 13–35 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	98.6 GB	Q6_K	Tight
21.8 tok/s 13–35 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	85.0 GB	Q5_K_M	Tight
21.8 tok/s 13–35 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	85.0 GB	Q5_K_M	Tight
17.7 tok/s 11–28 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	125.8 GB	Q8_0	Tight
17.7 tok/s 11–28 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	125.8 GB	Q8_0	Tight
17.1 tok/s 10–27 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	71.4 GB	Q4_K_M	Tight
17.1 tok/s 10–27 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	71.4 GB	Q4_K_M	Tight
17.1 tok/s 10–27 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	71.4 GB	Q4_K_M	Tight
17.1 tok/s 10–27 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	71.4 GB	Q4_K_M	Tight
17.1 tok/s 10–27 · low confidence	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	71.4 GB	Q4_K_M	Tight
17.1 tok/s 10–27 · low confidence	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	71.4 GB	Q4_K_M	Tight
17.0 tok/s 10–27 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	125.8 GB	Q8_0	Comfortable
16.2 tok/s 10–26 · low confidence	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	71.4 GB	Q4_K_M	Tight
16.2 tok/s 10–26 · low confidence	A800 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Nov 2022	71.4 GB	Q4_K_M	Tight
15.1 tok/s 9–24 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	125.8 GB	Q8_0	Comfortable
15.1 tok/s 9–24 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	125.8 GB	Q8_0	Comfortable

Speeds are estimates for a single request — one conversation at a time — calculated from memory bandwidth, model size and quantisation. Real throughput varies with the inference runtime and its version. Figures published by hardware vendors measure many simultaneous requests and are much higher.

On record

Full specification

Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.

Origin

Who built this model, where, and when it was published.

Organisation: OpenAI
Organisation type: Industry
Country: United States of America
Published: 5 August 2025

What it does

The problem areas the model was built for. A model can carry several of each.

Domain: Language
Task: Language modeling/generation, Question answering

Size

How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.

Parameters: 116.8B
Training data: tokens

Training compute

The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.

Training compute: 4.9 × 10²⁴ FLOP
How it was established: Hardware

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Training hardware: NVIDIA H100 SXM5 80GB

Availability

Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.

Weights: Open — downloadable
Model access: Open weights (unrestricted)

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Likely above 10²³ FLOP: Yes
Why it is tracked: Discretionary
Record confidence: Confident

Sources

Where this record came from and when it was last checked.

Reference: gpt-oss-120b & gpt-oss-20b Model Card
Last updated: 10 February 2026

The extremes

The ten fastest GPUs that run gpt-oss-120b

Ranked by estimated tokens per second, newest card first where speeds tie. Because generation is bound by memory bandwidth, this ordering follows bandwidth rather than any gaming benchmark.

The smallest GPUs that still run gpt-oss-120b

The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.

What the numbers mean

Hardware requirements in practice

Minimum card

RTX PRO 5000 72 GB Blackwell

Memory needed

64.6 GB

Fastest

33.0 tok/s

gpt-oss-120b sits at 116.8B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 38 of the cards we track can hold it.

The entry point is the RTX PRO 5000 72 GB Blackwell: 72 GB of memory, IQ4_XS compression, roughly 11.9 tokens per second.

The quickest result comes from a H100 NVL 94 GB at around 33.0 tokens per second — its 3,940 GB/s of bandwidth is what buys that.

Where it came from

gpt-oss-120b was published by OpenAI, in United States of America, in August 2025. It comes out of industry.

It works in Language, and is recorded as doing language modeling/generation, Question answering.

The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.

Understanding the speeds

Across every card that can run it, the middle of the range is about 17.1 tokens per second, and 36 of them clear the ten tokens per second that roughly matches reading speed.

It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

What went into building it

Training it took roughly 4.9 × 10²⁴ FLOP of computation, on NVIDIA H100 SXM5 80GB — a measure of what producing the model cost, not of how fast it answers.

The reason it appears in this catalogue at all is discretionary.

Step by step

How to choose a GPU for gpt-oss-120b

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

01

Check what it needs before anything else

The table lists every card that can hold gpt-oss-120b — around 64.6 GB at IQ4_XS. That figure, not the card's headline performance, is what decides whether it runs.
02

Decide how long your conversations run

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for gpt-oss-120b.
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold — IQ4_XS on the smallest card that fits. Setting a floor drops the cards that only manage gpt-oss-120b by squeezing it further than you would want.
04

Sort by speed

Sort by speed to see how cards rank for gpt-oss-120b. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the H100 NVL 94 GB tops it at 33.0 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs gpt-oss-120b but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Open the card you have settled on

Following a card through to its own page shows every other model it can hold, which is the question that follows once gpt-oss-120b is settled.

Answers

gpt-oss-120b — common questions

How accurate are these gpt-oss-120b speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as 20–53 tok/s on the H100 NVL 94 GB rather than a single number.

What GPU do I need to run gpt-oss-120b?

The smallest card in our catalogue that holds gpt-oss-120b is the RTX PRO 5000 72 GB Blackwell, with 72 GB of memory. It runs the model at IQ4_XS using about 64.6 GB, and produces roughly 11.9 tokens per second. 38 cards in total can run it.

How fast is gpt-oss-120b on a GPU?

It depends on the card. The quickest we calculate is a H100 NVL 94 GB at about 33.0 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 36 of the cards that can run gpt-oss-120b clear that.

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

About 64.6 GB at IQ4_XS compression, which is what the smallest card that runs it uses. Less compression needs more: the figures in the memory column above are recalculated for each card, because each one holds the least-compressed version it can.

Is gpt-oss-120b open source?

Its weights are published, so gpt-oss-120b can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.

How many parameters does gpt-oss-120b have?

gpt-oss-120b has 116.8B parameters. Total parameters: 116.83B. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.

Who created gpt-oss-120b?

gpt-oss-120b was published by OpenAI, based in United States of America, categorised as industry.

When was gpt-oss-120b released?

gpt-oss-120b was published in August 2025.

What is gpt-oss-120b used for?

gpt-oss-120b works in Language, and is recorded as handling language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download gpt-oss-120b?

The weights for gpt-oss-120b are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

How much compute was used to train gpt-oss-120b?

Around 4.9 × 10²⁴ FLOP, on NVIDIA H100 SXM5 80GB. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.

Can I run gpt-oss-120b if it does not fit in my GPU?

It can be split between the card and system memory, but gpt-oss-120b generates painfully slowly that way — the nearest miss we calculate is short by 13.8 GB. Nothing on this page assumes offloading.

Would two GPUs run gpt-oss-120b faster?

Capacity adds across cards; throughput does not. Since 38 of the cards we track already hold gpt-oss-120b on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for gpt-oss-120b?

Because capacity varies, so does how hard gpt-oss-120b has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.

Source

Original publication

Record last updated 10 February 2026

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