GPT-OSS-Safeguard-120B TPS calculator

Open weights OpenAI 120B parameters October 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 · Q3_K_M · 12.8 tok/s

Fastest card

H100 NVL 94 GB

32.1 tok/s · 94 GB

Which GPUs can run GPT-OSS-Safeguard-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
32.1 tok/s 19–51 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	73.3 GB	Q4_K_M	Tight
29.1 tok/s 17–47 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	66.3 GB	IQ4_XS	Tight
29.1 tok/s 17–47 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	66.3 GB	IQ4_XS	Tight
28.2 tok/s 17–45 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	129.2 GB	Q8_0	Comfortable
28.2 tok/s 17–45 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	129.2 GB	Q8_0	Comfortable
27.4 tok/s 16–44 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	73.3 GB	Q4_K_M	Tight
27.4 tok/s 16–44 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	73.3 GB	Q4_K_M	Tight
27.4 tok/s 16–44 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	73.3 GB	Q4_K_M	Tight
26.2 tok/s 16–42 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	101.2 GB	Q6_K	Tight
25.1 tok/s 15–40 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	101.2 GB	Q6_K	Comfortable
25.1 tok/s 15–40 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	101.2 GB	Q6_K	Comfortable
22.6 tok/s 14–36 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	129.2 GB	Q8_0	Comfortable
22.6 tok/s 14–36 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	129.2 GB	Q8_0	Comfortable
21.3 tok/s 13–34 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	101.2 GB	Q6_K	Tight
17.7 tok/s 11–28 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	66.3 GB	IQ4_XS	Tight
17.7 tok/s 11–28 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	66.3 GB	IQ4_XS	Tight
17.7 tok/s 11–28 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	66.3 GB	IQ4_XS	Tight
17.7 tok/s 11–28 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	66.3 GB	IQ4_XS	Tight
17.7 tok/s 11–28 · low confidence	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	66.3 GB	IQ4_XS	Tight
17.7 tok/s 11–28 · low confidence	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	66.3 GB	IQ4_XS	Tight
16.8 tok/s 10–27 · low confidence	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	66.3 GB	IQ4_XS	Tight
16.8 tok/s 10–27 · low confidence	A800 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Nov 2022	66.3 GB	IQ4_XS	Tight
16.5 tok/s 10–26 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	129.2 GB	Q8_0	Comfortable
14.7 tok/s 9–23 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	129.2 GB	Q8_0	Comfortable
14.7 tok/s 9–23 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	129.2 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: 29 October 2025

What it does

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

Domain: Language
Task: Safety/Classification

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: 120B
Training data: tokens

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)

The extremes

The ten fastest GPUs that run GPT-OSS-Safeguard-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-Safeguard-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

59.3 GB

Fastest

32.1 tok/s

GPT-OSS-Safeguard-120B reaches a parameter count of 120B. That puts it above consumer hardware, into the range where a card is bought for this purpose rather than repurposed for it. The number of cards we track that can hold it: 38.

The entry point is RTX PRO 5000 72 GB Blackwell, with a memory capacity of 72 GB, running it at a compression of Q3_K_M and producing around 12.8 tokens per second.

The quickest result comes from H100 NVL 94 GB, generating roughly 32.1 tokens per second on the strength of a memory bandwidth of 3,940 GB/s.

Where it came from

GPT-OSS-Safeguard-120B was published by OpenAI, in the country recorded as United States of America, during October 2025. It comes out of an organisation categorised as industry.

It works in the domain of Language, and is recorded as performing the task of safety/Classification.

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

Half the cards that hold it manage more than 17.7 tokens per second. Producing text faster than most people read it: 36 of them.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.

Step by step

How to choose a GPU for GPT-OSS-Safeguard-120B

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

01

Read the memory figure first

The table lists every card able to hold GPT-OSS-Safeguard-120B, needing around 59.3 GB at a compression of Q3_K_M. That figure, not the headline performance of a card, is what decides whether it runs.
02

Match the context to your actual use

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-Safeguard-120B.
03

Choose how far you will compress it

Each card runs the least-compressed copy it can hold, reaching a compression of Q3_K_M on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
04

Rank by throughput rather than spec sheet

The speed ordering is effectively an ordering by memory bandwidth, for GPT-OSS-Safeguard-120B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is H100 NVL 94 GB, at 32.1 tok/s.
05

Check the fit verdict before buying

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of GPT-OSS-Safeguard-120B. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
06

Open the card you have settled on

Every card name links to its own page, which runs the same calculation across the whole model catalogue. A card is usually bought for more than one model, so it is worth a look before buying for GPT-OSS-Safeguard-120B.

Answers

GPT-OSS-Safeguard-120B — common questions

GPT-OSS-Safeguard-120B— where can I download it?

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

GPT-OSS-Safeguard-120B— can I run it if it does not fit in my GPU?

Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded model is rarely worth using. The nearest miss we calculate falls short by 15.7 GB. Every figure here assumes the whole model is resident on the card.

GPT-OSS-Safeguard-120B— would two GPUs run it faster?

A second card roughly doubles the memory available but not the generation rate. The number already able to run it alone: 38. So a second card is rarely the answer here.

GPT-OSS-Safeguard-120B— why does the quantisation differ between cards?

Each card is shown running the least-compressed copy it can hold. The number of distinct compression levels across the cards that fit it: 5. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

GPT-OSS-Safeguard-120B— how accurate are these speed estimates?

These are estimates with real error bars, and any of them could reasonably land anywhere in its published range depending on which runtime you use. The fastest result here: 19–51 tok/s on H100 NVL 94 GB. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

GPT-OSS-Safeguard-120B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is RTX PRO 5000 72 GB Blackwell, with a memory capacity of 72 GB. It runs the model at a compression of Q3_K_M using about 59.3 GB, and produces roughly 12.8 tokens per second. The number of cards able to run it in total: 38.

GPT-OSS-Safeguard-120B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is H100 NVL 94 GB, at about 32.1 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and the number of cards clearing that: 36.

GPT-OSS-Safeguard-120B— how much VRAM does it need?

It needs about 59.3 GB at a compression of Q3_K_M, 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.

GPT-OSS-Safeguard-120B— is it open source?

Its weights are published, so it 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.

GPT-OSS-Safeguard-120B— how many parameters does it have?

It has a parameter count of 120B. Safety/classification model, Apache 2.0. 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.

GPT-OSS-Safeguard-120B— who created it?

It was published by OpenAI, based in United States of America, an organisation categorised as industry.

GPT-OSS-Safeguard-120B— when was it released?

It was published in October 2025.

GPT-OSS-Safeguard-120B— what is it used for?

It works in the domain of Language, and is recorded as handling the task of safety/Classification. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.

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