GPT-OSS-Safeguard-20B TPS calculator

Open weights OpenAI 20B 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

293 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Quadro K6000

12 GB · Q3_K_M · 14.0 tok/s

Fastest card

B200

169 tok/s · 180 GB

Which GPUs can run GPT-OSS-Safeguard-20B?

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

293 cards match

Calculating

					Needs	Quantisation	Fit
169 tok/s 102–271 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	22.1 GB	Q8_0	Comfortable
169 tok/s 102–271 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	22.1 GB	Q8_0	Comfortable
135 tok/s 81–216 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	22.1 GB	Q8_0	Comfortable
135 tok/s 81–216 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	22.1 GB	Q8_0	Comfortable
108 tok/s 65–173 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	22.1 GB	Q8_0	Comfortable
104 tok/s 62–166 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	22.1 GB	Q8_0	Comfortable
104 tok/s 62–166 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	22.1 GB	Q8_0	Comfortable
99.1 tok/s 59–159 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	22.1 GB	Q8_0	Comfortable
88.0 tok/s 53–141 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	22.1 GB	Q8_0	Comfortable
88.0 tok/s 53–141 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	22.1 GB	Q8_0	Comfortable
88.0 tok/s 53–141 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	22.1 GB	Q8_0	Comfortable
83.4 tok/s 50–134 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	22.1 GB	Q8_0	Comfortable
71.2 tok/s 43–114 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	22.1 GB	Q8_0	Comfortable
71.2 tok/s 43–114 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	22.1 GB	Q8_0	Comfortable
71.2 tok/s 43–114 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	22.1 GB	Q8_0	Comfortable
71.2 tok/s 43–114 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	22.1 GB	Q8_0	Comfortable
71.2 tok/s 43–114 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	22.1 GB	Q8_0	Comfortable
55.2 tok/s 33–88 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	12.8 GB	Q4_K_M	Tight
54.2 tok/s 33–87 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	22.1 GB	Q8_0	Comfortable
54.2 tok/s 33–87 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	22.1 GB	Q8_0	Comfortable
52.1 tok/s 31–83 · low confidence	GeForce RTX 3080 12 GB NVIDIA	12 GB	912 GB/s	Jan 2022	10.5 GB	Q3_K_M	Tight
52.1 tok/s 31–83 · low confidence	GeForce RTX 3080 Ti NVIDIA	12 GB	912 GB/s	May 2021	10.5 GB	Q3_K_M	Tight
46.9 tok/s 28–75 · low confidence	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	12.8 GB	Q4_K_M	Tight
45.2 tok/s 27–72 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	22.1 GB	Q8_0	Comfortable
44.2 tok/s 27–71 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	22.1 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: 20B
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-20B

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-20B

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

The hardware side

Minimum card

Quadro K6000

Memory needed

10.5 GB

Fastest

169 tok/s

GPT-OSS-Safeguard-20B reaches a parameter count of 20B. That lands in the range a serious desktop card can handle once the weights are compressed. The number of cards we track that can run it: 293.

At the low end it is handled by Quadro K6000, with a memory capacity of 12 GB, running it at a compression of Q3_K_M and producing around 14.0 tokens per second.

The fastest we calculate for it is B200, generating roughly 169 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

About this model

GPT-OSS-Safeguard-20B 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.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.

How fast it runs, and why

Across every card that can run it, the middle of the range sits at 20.6 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 248 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.

Its internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.

Step by step

How to choose a GPU for GPT-OSS-Safeguard-20B

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 able to hold GPT-OSS-Safeguard-20B, needing around 10.5 GB at a compression of Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
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-20B.
03

Decide how much compression you will accept

Compression is what makes a model fit smaller cards, at some cost in accuracy, 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-20B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 169 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of GPT-OSS-Safeguard-20B. 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

Check the card from the other side

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-20B.

Answers

GPT-OSS-Safeguard-20B — common questions

GPT-OSS-Safeguard-20B— how many parameters does it have?

It has a parameter count of 20B. 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-20B— who created it?

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

GPT-OSS-Safeguard-20B— when was it released?

It was published in October 2025.

GPT-OSS-Safeguard-20B— 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.

GPT-OSS-Safeguard-20B— 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-20B— can I run it if it does not fit in my GPU?

It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 2.9 GB. Every figure here assumes the whole model is resident on the card.

GPT-OSS-Safeguard-20B— would two GPUs run it faster?

Capacity adds across cards; throughput does not. The number of cards already holding it on their own: 293. So a second card is rarely the answer here.

GPT-OSS-Safeguard-20B— why does the quantisation differ between cards?

Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 4. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

GPT-OSS-Safeguard-20B— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 102–271 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

GPT-OSS-Safeguard-20B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Quadro K6000, with a memory capacity of 12 GB. It runs the model at a compression of Q3_K_M using about 10.5 GB, and produces roughly 14.0 tokens per second. The number of cards able to run it in total: 293.

GPT-OSS-Safeguard-20B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 169 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: 248.

GPT-OSS-Safeguard-20B— how much VRAM does it need?

It needs about 10.5 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-20B— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of Q3_K_M, using about 10.5 GB and generating roughly 52.1 tokens per second. The fit is tight.

GPT-OSS-Safeguard-20B— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q4_K_M, using about 12.8 GB and generating roughly 55.2 tokens per second. The fit is tight.

GPT-OSS-Safeguard-20B— can I run it on a GPU holding 24 GB?

Yes. The card GeForce RTX 5090 D V2, holding 24 GB, runs it at a compression of Q6_K, using about 17.5 GB and generating roughly 41.2 tokens per second. The fit is comfortable.

GPT-OSS-Safeguard-20B— 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.

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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