ESM1-670M (UR100) TPS calculator

Open weights Facebook AI Research,New York University (NYU) 669.2M parameters August 2020

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 · 55.1 tok/s

Fastest card

B200

5,063 tok/s · 180 GB

Which GPUs can run ESM1-670M (UR100)?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
5,063 tok/s 3,038–8,101 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.4 GB	Q8_0	Comfortable
5,063 tok/s 3,038–8,101 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.4 GB	Q8_0	Comfortable
4,043 tok/s 2,426–6,469 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.4 GB	Q8_0	Comfortable
4,043 tok/s 2,426–6,469 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.4 GB	Q8_0	Comfortable
3,233 tok/s 1,940–5,173 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.4 GB	Q8_0	Comfortable
3,095 tok/s 1,857–4,952 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.4 GB	Q8_0	Comfortable
3,095 tok/s 1,857–4,952 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.4 GB	Q8_0	Comfortable
2,962 tok/s 1,777–4,739 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.4 GB	Q8_0	Comfortable
2,629 tok/s 1,577–4,206 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.4 GB	Q8_0	Comfortable
2,629 tok/s 1,577–4,206 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.4 GB	Q8_0	Comfortable
2,629 tok/s 1,577–4,206 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.4 GB	Q8_0	Comfortable
2,494 tok/s 1,496–3,990 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.4 GB	Q8_0	Comfortable
2,127 tok/s 1,276–3,402 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.4 GB	Q8_0	Comfortable
2,127 tok/s 1,276–3,402 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.4 GB	Q8_0	Comfortable
2,127 tok/s 1,276–3,402 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.4 GB	Q8_0	Comfortable
2,127 tok/s 1,276–3,402 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.4 GB	Q8_0	Comfortable
2,127 tok/s 1,276–3,402 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.4 GB	Q8_0	Comfortable
1,619 tok/s 972–2,591 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.4 GB	Q8_0	Comfortable
1,619 tok/s 972–2,591 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.4 GB	Q8_0	Comfortable
1,349 tok/s 810–2,159 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.4 GB	Q8_0	Comfortable
1,321 tok/s 792–2,113 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.4 GB	Q8_0	Comfortable
1,291 tok/s 775–2,066 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.4 GB	Q8_0	Comfortable
1,291 tok/s 775–2,066 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.4 GB	Q8_0	Comfortable
1,291 tok/s 775–2,066 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.4 GB	Q8_0	Comfortable
1,291 tok/s 775–2,066 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.4 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,New York University (NYU)
Organisation type: Industry,Academia
Country: United States of America, France
Published: 31 August 2020
Authors: Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus

What it does

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

Domain: Biology
Task: Proteins, Protein or nucleotide language model (pLM/nLM)

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: 669.2M
Training data: 127,897,600,000 tokens
Epochs: 0.3

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: 1.4 × 10²⁰ FLOP
How it was established: Operation counting

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: 128
Power draw: 78.2 kW
Compute cost: $308

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)
Training code: Unreleased

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: Highly cited
Record confidence: Confident
Citations: 2,637

Sources

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

Reference: Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Last updated: 11 February 2026

The extremes

The ten fastest GPUs that run ESM1-670M (UR100)

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 ESM1-670M (UR100)

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

What you need to run it

Minimum card

Tesla C1080

Memory needed

1.4 GB

Fastest

5,063 tok/s

ESM1-670M (UR100) is small enough at 669.2M parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The least hardware that works is a Tesla C1080. Its 4 GB is enough at Q8_0 compression, giving roughly 55.1 tokens per second.

At the other end, a B200 generates roughly 5,063 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

Where it came from

ESM1-670M (UR100) was published by Facebook AI Research,New York University (NYU), in United States of America, in August 2020. industry,Academia is the category the publisher falls under.

It works in Biology, and is recorded as doing proteins, Protein or nucleotide language model (pLM/nLM).

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

Understanding the speeds

Across every card that can run it, the middle of the range is about 142.2 tokens per second, and 809 of them clear the ten tokens per second that roughly matches reading speed.

Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.

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.

What went into building it

Training it took roughly 1.4 × 10²⁰ FLOP of computation, on NVIDIA V100 — a measure of what producing the model cost, not of how fast it answers.

Around 127,897,600,000 tokens went into training it.

The reason it appears in this catalogue at all is highly cited.

Step by step

How to choose a GPU for ESM1-670M (UR100)

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 ESM1-670M (UR100) — around 1.4 GB at Q8_0. That figure, not the card's headline performance, 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 ESM1-670M (UR100).
03

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy of ESM1-670M (UR100) — Q8_0 on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for ESM1-670M (UR100). It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 5,063 tok/s.
05

Read the fit column last

The fit column separates cards that just manage ESM1-670M (UR100) from those with room to spare. Buy for the second if the context might grow.
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 ESM1-670M (UR100) is settled.

Answers

ESM1-670M (UR100) — common questions

Can I run ESM1-670M (UR100) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.4 GB and generating roughly 848 tokens per second — a comfortable fit.

Is ESM1-670M (UR100) open source?

Its weights are published, so ESM1-670M (UR100) 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 ESM1-670M (UR100) have?

ESM1-670M (UR100) has 669.2M parameters. See Table 1. 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 ESM1-670M (UR100)?

ESM1-670M (UR100) was published by Facebook AI Research,New York University (NYU), based in United States of America, categorised as industry,Academia.

When was ESM1-670M (UR100) released?

ESM1-670M (UR100) was published in August 2020. 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 ESM1-670M (UR100) used for?

ESM1-670M (UR100) works in Biology, and is recorded as handling proteins, Protein or nucleotide language model (pLM/nLM). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download ESM1-670M (UR100)?

The weights for ESM1-670M (UR100) 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 ESM1-670M (UR100)?

Around 1.4 × 10²⁰ 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 ESM1-670M (UR100) if it does not fit in my GPU?

It can be split between the card and system memory, but ESM1-670M (UR100) generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run ESM1-670M (UR100) faster?

Two cards buy memory rather than speed. That matters for ESM1-670M (UR100) only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for ESM1-670M (UR100)?

A larger card holds a more accurate copy. Across the cards that run ESM1-670M (UR100), 1 compression levels are used; the floor control above pins it to one.

How accurate are these ESM1-670M (UR100) 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 3,038–8,101 tok/s on the B200 rather than a single number.

What GPU do I need to run ESM1-670M (UR100)?

The smallest card in our catalogue that holds ESM1-670M (UR100) is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.4 GB, and produces roughly 55.1 tokens per second. 818 cards in total can run it.

How fast is ESM1-670M (UR100) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 5,063 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 809 of the cards that can run ESM1-670M (UR100) clear that.

How much VRAM does ESM1-670M (UR100) need?

About 1.4 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 ESM1-670M (UR100) on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.4 GB and generating roughly 943 tokens per second — a comfortable fit.

Can I run ESM1-670M (UR100) on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.4 GB and generating roughly 577 tokens per second — a comfortable fit.

Can I run ESM1-670M (UR100) on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.4 GB and generating roughly 715 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 11 February 2026

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