SEER TPS calculator

Open weights Facebook AI Research,INRIA 1.3B parameters July 2021

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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q8_0 · 28.4 tok/s

Fastest card

B200

2,606 tok/s · 180 GB

Which GPUs can run SEER?

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.

Search cards

Card memory

Also show cards that cannot run it

818 cards match

Calculating

					Needs	Quantisation	Fit
2,606 tok/s 1,564–4,170 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	2.1 GB	Q8_0	Comfortable
2,606 tok/s 1,564–4,170 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	2.1 GB	Q8_0	Comfortable
2,081 tok/s 1,249–3,330 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	2.1 GB	Q8_0	Comfortable
2,081 tok/s 1,249–3,330 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	2.1 GB	Q8_0	Comfortable
1,664 tok/s 999–2,663 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	2.1 GB	Q8_0	Comfortable
1,593 tok/s 956–2,549 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.1 GB	Q8_0	Comfortable
1,593 tok/s 956–2,549 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.1 GB	Q8_0	Comfortable
1,525 tok/s 915–2,440 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	2.1 GB	Q8_0	Comfortable
1,353 tok/s 812–2,165 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	2.1 GB	Q8_0	Comfortable
1,353 tok/s 812–2,165 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	2.1 GB	Q8_0	Comfortable
1,353 tok/s 812–2,165 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	2.1 GB	Q8_0	Comfortable
1,284 tok/s 770–2,054 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	2.1 GB	Q8_0	Comfortable
1,095 tok/s 657–1,751 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.1 GB	Q8_0	Comfortable
1,095 tok/s 657–1,751 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	2.1 GB	Q8_0	Comfortable
1,095 tok/s 657–1,751 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	2.1 GB	Q8_0	Comfortable
1,095 tok/s 657–1,751 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.1 GB	Q8_0	Comfortable
1,095 tok/s 657–1,751 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	2.1 GB	Q8_0	Comfortable
834 tok/s 500–1,334 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	2.1 GB	Q8_0	Comfortable
834 tok/s 500–1,334 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	2.1 GB	Q8_0	Comfortable
695 tok/s 417–1,111 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	2.1 GB	Q8_0	Comfortable
680 tok/s 408–1,088 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	2.1 GB	Q8_0	Comfortable
665 tok/s 399–1,063 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	2.1 GB	Q8_0	Comfortable
665 tok/s 399–1,063 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	2.1 GB	Q8_0	Comfortable
665 tok/s 399–1,063 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	2.1 GB	Q8_0	Comfortable
665 tok/s 399–1,063 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	2.1 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: Facebook AI Research,INRIA
Organisation type: Industry,Academia
Country: United States of America, France
Published: 29 July 2021
Authors: Priya Goyal, Mathilde Caron, Benjamin Lefaudeux, Min Xu, Pengchao Wang, Vivek Pai, Mannat Singh, Vitaliy Liptchinsky, Ishan Misra, Armand Joulin, Piotr Bojanowski

What it does

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

Domain: Vision
Task: Image embedding, Image classification
Approach: Self-supervised learning
Numerical format: FP16

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: 1.3B
Training data: 1,000,000,000 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: 1.8 × 10²² 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 V100
Chips used: 512
Chip-hours: 98,304
Wall-clock time: 196 hours (8.1 days)
Power draw: 310.4 kW
Compute cost: $34,114

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 (non-commercial)
Training code: Open (non-commercial)

How it is classified

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

Why it is tracked: SOTA improvement
Record confidence: Confident
Citations: 301

Sources

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

Reference: Self-supervised Pretraining of Visual Features in the Wild
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run SEER

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 SEER

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 C1080

Memory needed

2.1 GB

Fastest

2,606 tok/s

SEER is small enough at 1.3B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The entry point is the Tesla C1080: 4 GB of memory, Q8_0 compression, roughly 28.4 tokens per second.

The quickest result comes from a B200 at around 2,606 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Where it came from

SEER was published by Facebook AI Research,INRIA, in United States of America, in July 2021. industry,Academia is the category the publisher falls under.

It works in Vision, and is recorded as doing image embedding, Image classification.

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

Understanding the speeds

The median result is around 73.2 tokens per second; 797 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.

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.

What went into building it

Training it took roughly 1.8 × 10²² FLOP of computation, on NVIDIA V100 — a measure of what producing the model cost, not of how fast it answers.

It was trained on about 1,000,000,000 tokens of text.

Its inclusion criterion is sOTA improvement.

Step by step

How to choose a GPU for SEER

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 SEER — around 2.1 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
02

Set the context length you will work at

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason SEER stops fitting a card that seemed fine.
03

Set a quality floor

Each card runs the least-compressed copy it can hold — Q8_0 on the smallest card that fits. Setting a floor drops the cards that only manage SEER by squeezing it further than you would want.
04

Sort by speed

The speed ordering for SEER is effectively an ordering by memory bandwidth, which is why the B200 tops it at 2,606 tok/s.
05

Read the fit column last

The fit column separates cards that just manage SEER from those with room to spare. Buy for the second if the context might grow.
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 SEER is settled.

Answers

SEER — common questions

Can I run SEER on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 2.1 GB and generating roughly 368 tokens per second — a comfortable fit.

Can I run SEER on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 2.1 GB and generating roughly 437 tokens per second — a comfortable fit.

Is SEER open source?

Its weights are published, so SEER 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 SEER have?

SEER has 1.3B parameters. From abstract: " Our final SElf-supERvised (SEER) model, a RegNetY with 1.3B parameters...". 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 SEER?

SEER was published by Facebook AI Research,INRIA, based in United States of America, categorised as industry,Academia.

When was SEER released?

SEER was published in July 2021. 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 SEER used for?

SEER works in Vision, and is recorded as handling image embedding, Image 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.

Where can I download SEER?

The weights for SEER 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 SEER?

Around 1.8 × 10²² FLOP, on NVIDIA V100. 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 SEER if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down. Our figures for SEER assume it is fully resident.

Would two GPUs run SEER faster?

Two cards buy memory rather than speed. That matters for SEER only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for SEER?

A larger card holds a more accurate copy. Across the cards that run SEER, 1 compression levels are used; the floor control above pins it to one.

How accurate are these SEER 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 1,564–4,170 tok/s on the B200 rather than a single number.

What GPU do I need to run SEER?

The smallest card in our catalogue that holds SEER is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 2.1 GB, and produces roughly 28.4 tokens per second. 818 cards in total can run it.

How fast is SEER on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 2,606 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 797 of the cards that can run SEER clear that.

How much VRAM does SEER need?

About 2.1 GB at Q8_0 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 SEER on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 2.1 GB and generating roughly 485 tokens per second — a comfortable fit.

Can I run SEER on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 2.1 GB and generating roughly 297 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 25 May 2026

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