gpt-sw3-40b TPS calculator

Open weights AI Sweden 40B parameters March 2023

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

126 of 818 cards that can run it

Smallest card that fits

Tesla M40 24 GB

24 GB · Q3_K_M · 7.0 tok/s

Fastest card

B200

84.7 tok/s · 180 GB

Which GPUs can run gpt-sw3-40b?

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

126 cards match

Calculating

					Needs	Quantisation	Fit
84.7 tok/s 51–136 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	43.5 GB	Q8_0	Comfortable
84.7 tok/s 51–136 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	43.5 GB	Q8_0	Comfortable
67.6 tok/s 41–108 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	43.5 GB	Q8_0	Comfortable
67.6 tok/s 41–108 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	43.5 GB	Q8_0	Comfortable
54.1 tok/s 32–87 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	43.5 GB	Q8_0	Comfortable
51.8 tok/s 31–83 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	43.5 GB	Q8_0	Comfortable
51.8 tok/s 31–83 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	43.5 GB	Q8_0	Comfortable
49.6 tok/s 30–79 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	43.5 GB	Q8_0	Comfortable
45.7 tok/s 27–73 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	24.9 GB	Q4_K_M	Tight
45.7 tok/s 27–73 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	24.9 GB	Q4_K_M	Tight
44.0 tok/s 26–70 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	43.5 GB	Q8_0	Comfortable
44.0 tok/s 26–70 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	43.5 GB	Q8_0	Comfortable
44.0 tok/s 26–70 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	43.5 GB	Q8_0	Comfortable
43.8 tok/s 26–70 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	24.9 GB	Q4_K_M	Tight
43.8 tok/s 26–70 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	24.9 GB	Q4_K_M	Tight
41.7 tok/s 25–67 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	43.5 GB	Q8_0	Comfortable
38.3 tok/s 23–61 · low confidence	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	20.2 GB	Q3_K_M	Tight
35.6 tok/s 21–57 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	43.5 GB	Q8_0	Comfortable
35.6 tok/s 21–57 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	43.5 GB	Q8_0	Comfortable
35.6 tok/s 21–57 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	43.5 GB	Q8_0	Comfortable
35.6 tok/s 21–57 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	43.5 GB	Q8_0	Comfortable
35.6 tok/s 21–57 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	43.5 GB	Q8_0	Comfortable
34.9 tok/s 21–56 · low confidence	A30X NVIDIA	24 GB	1,220 GB/s	Apr 2021	20.2 GB	Q3_K_M	Tight
28.9 tok/s 17–46 · low confidence	GeForce RTX 3090 Ti NVIDIA	24 GB	1,010 GB/s	Jan 2022	20.2 GB	Q3_K_M	Tight
28.9 tok/s 17–46 · low confidence	GeForce RTX 4090 NVIDIA	24 GB	1,010 GB/s	Sep 2022	20.2 GB	Q3_K_M	Tight

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: AI Sweden
Country: Sweden
Published: 1 March 2023
Authors: AI Sweden

What it does

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

Domain: Language
Task: Language modeling/generation, Chat
Approach: Self-supervised learning

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: 40B
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: 7.7 × 10²² FLOP
How it was established: Operation counting

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 (restricted use)
Training code: Unreleased

How it is classified

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

Record confidence: Confident

Sources

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

Reference: gpt-sw3-40b
Last updated: 28 November 2025

The extremes

The ten fastest GPUs for gpt-sw3-40b

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-sw3-40b

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

Tesla M40 24 GB

Memory needed

20.2 GB

Fastest

84.7 tok/s

With 40B parameters, gpt-sw3-40b lands in the range a serious desktop card can handle once the weights are compressed. 126 of the cards we track can run it.

The entry point is the Tesla M40 24 GB: 24 GB of memory, Q3_K_M compression, roughly 7.0 tokens per second.

At the other end, a B200 generates roughly 84.7 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

Background

gpt-sw3-40b was published by AI Sweden, in Sweden, in March 2023.

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

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

Reading the throughput figures

The median result is around 21.2 tokens per second; 106 cards produce text faster than most people read it.

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.

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.

Training and provenance

Training it took roughly 7.7 × 10²² FLOP of computation — a measure of what producing the model cost, not of how fast it answers.

Step by step

How to choose a GPU for gpt-sw3-40b

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

01

Start from the memory column

Every card here has been checked against gpt-sw3-40b — around 20.2 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
02

Decide how long your conversations run

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context gpt-sw3-40b can slip off a card that handles short questions easily.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy of gpt-sw3-40b — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for gpt-sw3-40b follows memory bandwidth, not core counts, which is why the B200 tops it at 84.7 tok/s.
05

Check the fit verdict before buying

Tight means gpt-sw3-40b loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
06

Check the card from the other side

Following a card through to its own page shows every other model it can hold, which is the question that follows once gpt-sw3-40b is settled.

Answers

gpt-sw3-40b — common questions

How many parameters does gpt-sw3-40b have?

gpt-sw3-40b has 40B parameters. 40B. 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-sw3-40b?

gpt-sw3-40b was published by AI Sweden, based in Sweden.

When was gpt-sw3-40b released?

gpt-sw3-40b was published in March 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

What is gpt-sw3-40b used for?

gpt-sw3-40b works in Language, and is recorded as handling language modeling/generation, Chat. 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.

Where can I download gpt-sw3-40b?

The weights for gpt-sw3-40b 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-sw3-40b?

Around 7.7 × 10²² FLOP. 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-sw3-40b 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 gpt-sw3-40b is rarely worth using — the nearest miss we calculate is short by 6.9 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run gpt-sw3-40b faster?

Two cards buy memory rather than speed. That matters for gpt-sw3-40b only if one card cannot hold it — 126 can, so a second adds little.

Why does the quantisation differ between cards for gpt-sw3-40b?

Each card is shown running the least-compressed copy it can hold, and gpt-sw3-40b appears at 4 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these gpt-sw3-40b speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 51–136 tok/s on the B200, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

What GPU do I need to run gpt-sw3-40b?

The smallest card in our catalogue that holds gpt-sw3-40b is the Tesla M40 24 GB, with 24 GB of memory. It runs the model at Q3_K_M using about 20.2 GB, and produces roughly 7.0 tokens per second. 126 cards in total can run it.

How fast is gpt-sw3-40b on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 84.7 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 106 of the cards that can run gpt-sw3-40b clear that.

How much VRAM does gpt-sw3-40b need?

About 20.2 GB at Q3_K_M 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.

Can I run gpt-sw3-40b on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q3_K_M, using about 20.2 GB and generating roughly 38.3 tokens per second — a tight fit.

Is gpt-sw3-40b open source?

Its weights are published, so gpt-sw3-40b 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.

Source

Original publication

Record last updated 28 November 2025

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