GenSLM TPS calculator

Open weights University of Chicago,NVIDIA,Harvard University,Cerebras Systems,Technical University of Munich,California Institute of Technology 25B parameters October 2022

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

241 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 7120P

16 GB · IQ4_XS · 9.5 tok/s

Fastest card

B200

136 tok/s · 180 GB

Which GPUs can run GenSLM?

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

241 cards match

Calculating

					Needs	Quantisation	Fit
136 tok/s 81–217 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	27.5 GB	Q8_0	Comfortable
136 tok/s 81–217 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	27.5 GB	Q8_0	Comfortable
108 tok/s 65–173 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	27.5 GB	Q8_0	Comfortable
108 tok/s 65–173 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	27.5 GB	Q8_0	Comfortable
86.6 tok/s 52–138 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	27.5 GB	Q8_0	Comfortable
82.8 tok/s 50–133 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	27.5 GB	Q8_0	Comfortable
82.8 tok/s 50–133 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	27.5 GB	Q8_0	Comfortable
79.3 tok/s 48–127 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	27.5 GB	Q8_0	Comfortable
70.4 tok/s 42–113 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	27.5 GB	Q8_0	Comfortable
70.4 tok/s 42–113 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	27.5 GB	Q8_0	Comfortable
70.4 tok/s 42–113 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	27.5 GB	Q8_0	Comfortable
66.8 tok/s 40–107 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	27.5 GB	Q8_0	Comfortable
56.9 tok/s 34–91 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	27.5 GB	Q8_0	Comfortable
56.9 tok/s 34–91 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	27.5 GB	Q8_0	Comfortable
56.9 tok/s 34–91 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	27.5 GB	Q8_0	Comfortable
56.9 tok/s 34–91 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	27.5 GB	Q8_0	Comfortable
56.9 tok/s 34–91 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	27.5 GB	Q8_0	Comfortable
47.0 tok/s 28–75 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	14.4 GB	IQ4_XS	Tight
43.3 tok/s 26–69 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	27.5 GB	Q8_0	Comfortable
43.3 tok/s 26–69 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	27.5 GB	Q8_0	Comfortable
40.5 tok/s 24–65 · low confidence	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	18.7 GB	Q5_K_M	Tight
39.9 tok/s 24–64 · low confidence	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	14.4 GB	IQ4_XS	Tight
37.3 tok/s 22–60 · low confidence	Tesla V100 DGXS 16 GB NVIDIA	16 GB	897 GB/s	Mar 2018	14.4 GB	IQ4_XS	Tight
37.3 tok/s 22–60 · low confidence	Tesla V100 PCIe 16 GB NVIDIA	16 GB	897 GB/s	Jun 2017	14.4 GB	IQ4_XS	Tight
37.3 tok/s 22–60 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	897 GB/s	Jun 2017	14.4 GB	IQ4_XS	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: University of Chicago,NVIDIA,Harvard University,Cerebras Systems,Technical University of Munich,California Institute of Technology
Organisation type: Academia,Industry,Academia,Industry,Academia,Academia
Country: United States of America, Germany
Published: 11 October 2022
Authors: Maxim Zvyagin, Alexander Brace, Kyle Hippe, Yuntian Deng, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael Irvin, J. Gregory Pauloski, Logan Ward, Valerie Hayot, Murali Emani, Sam Foreman, Zhen Xie, Diangen Lin, Maulik Shukla, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Ian Foster, James J. D…

What it does

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

Domain: Biology
Task: Protein or nucleotide language model (pLM/nLM)
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: 25B
Training data: 225,280,000,000 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: 1.4 × 10²¹ FLOP
How it was established: Reported

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: Open source

How it is classified

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

Foundation model: Yes
Why it is tracked: SOTA improvement
Record confidence: Confident
Citations: 114

Sources

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

Reference: GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics
Last updated: 1 January 2026

The extremes

The ten fastest GPUs that run GenSLM

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 GenSLM

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

Xeon Phi 7120P

Memory needed

14.4 GB

Fastest

136 tok/s

With 25B parameters, GenSLM lands in the range a serious desktop card can handle once the weights are compressed. 241 of the cards we track can run it.

At the low end, a Xeon Phi 7120P handles it — 16 GB, at IQ4_XS, for about 9.5 tokens per second.

A B200 is the fastest we calculate for it: about 136 tokens per second, from 8,000 GB/s of memory bandwidth.

About this model

GenSLM was published by University of Chicago,NVIDIA,Harvard University,Cerebras Systems,Technical University of Munich,California Institute of Technology, in United States of America, in October 2022. The organisation is categorised as academia,Industry,Academia,Industry,Academia,Academia.

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

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.

How fast it runs, and why

Half the cards that hold it manage more than 18.6 tokens per second, and 192 exceed reading speed outright.

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 1.4 × 10²¹ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.

The training set ran to roughly 225,280,000,000 tokens.

It is tracked in the underlying dataset for one reason in particular: sOTA improvement.

Step by step

How to choose a GPU for GenSLM

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

Look at what GenSLM actually needs — around 14.4 GB at IQ4_XS. No amount of processing power compensates for a card that cannot hold it.
02

Match the context to your actual use

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

Choose how far you will compress it

Each card runs the least-compressed copy it can hold — IQ4_XS on the smallest card that fits. Setting a floor drops the cards that only manage GenSLM by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for GenSLM. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 136 tok/s.
05

Look at the headroom, not just the fit

Tight means GenSLM 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

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond GenSLM.

Answers

GenSLM — common questions

Can I run GenSLM 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 — the nearest miss we calculate is short by 5.0 GB. Our figures for GenSLM assume it is fully resident.

Would two GPUs run GenSLM faster?

A second card roughly doubles the memory available but not the generation rate. With 241 cards already able to run GenSLM alone, the case for pairing is weak.

Why does the quantisation differ between cards for GenSLM?

Because capacity varies, so does how hard GenSLM has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these GenSLM speed estimates?

These are estimates with real error bars. The fastest result here, 81–217 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run GenSLM?

The smallest card in our catalogue that holds GenSLM is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at IQ4_XS using about 14.4 GB, and produces roughly 9.5 tokens per second. 241 cards in total can run it.

How fast is GenSLM on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 136 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 192 of the cards that can run GenSLM clear that.

How much VRAM does GenSLM need?

About 14.4 GB at IQ4_XS 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 GenSLM on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at IQ4_XS, using about 14.4 GB and generating roughly 47.0 tokens per second — a tight fit.

Can I run GenSLM on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q5_K_M, using about 18.7 GB and generating roughly 40.5 tokens per second — a tight fit.

Is GenSLM open source?

Its weights are published, so GenSLM 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 GenSLM have?

GenSLM has 25B parameters. See Table 3. 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 GenSLM?

GenSLM was published by University of Chicago,NVIDIA,Harvard University,Cerebras Systems,Technical University of Munich,California Institute of Technology, based in United States of America, categorised as academia,Industry,Academia,Industry,Academia,Academia.

When was GenSLM released?

GenSLM was published in October 2022. 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 GenSLM used for?

GenSLM works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). 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 GenSLM?

The weights for GenSLM 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 GenSLM?

Around 1.4 × 10²¹ 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.

Source

Original publication

Record last updated 1 January 2026

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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