GLA Transformer 340M TPS calculator

Open weights MIT-IBM Watson AI Lab,Massachusetts Institute of Technology (MIT) 340M parameters August 2024

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 · 108 tok/s

Fastest card

B200

9,965 tok/s · 180 GB

Which GPUs can run GLA Transformer 340M?

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
9,965 tok/s 5,979–15,945 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.1 GB	Q8_0	Comfortable
9,965 tok/s 5,979–15,945 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.1 GB	Q8_0	Comfortable
7,958 tok/s 4,775–12,732 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.1 GB	Q8_0	Comfortable
7,958 tok/s 4,775–12,732 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.1 GB	Q8_0	Comfortable
6,364 tok/s 3,818–10,183 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
6,091 tok/s 3,655–9,746 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.1 GB	Q8_0	Comfortable
6,091 tok/s 3,655–9,746 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.1 GB	Q8_0	Comfortable
5,830 tok/s 3,498–9,328 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.1 GB	Q8_0	Comfortable
5,174 tok/s 3,104–8,278 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
5,174 tok/s 3,104–8,278 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
5,174 tok/s 3,104–8,278 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
4,908 tok/s 2,945–7,853 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,185 tok/s 2,511–6,697 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,185 tok/s 2,511–6,697 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.1 GB	Q8_0	Comfortable
4,185 tok/s 2,511–6,697 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,185 tok/s 2,511–6,697 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,185 tok/s 2,511–6,697 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
3,187 tok/s 1,912–5,099 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.1 GB	Q8_0	Comfortable
3,187 tok/s 1,912–5,099 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.1 GB	Q8_0	Comfortable
2,656 tok/s 1,593–4,249 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
2,599 tok/s 1,559–4,159 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
2,541 tok/s 1,525–4,066 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.1 GB	Q8_0	Comfortable
2,541 tok/s 1,525–4,066 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.1 GB	Q8_0	Comfortable
2,541 tok/s 1,525–4,066 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.1 GB	Q8_0	Comfortable
2,541 tok/s 1,525–4,066 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.1 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: MIT-IBM Watson AI Lab,Massachusetts Institute of Technology (MIT)
Organisation type: Academia,Industry,Academia
Country: United States of America
Published: 27 August 2024
Authors: Songlin Yang, Bailin Wang, Yikang Shen, Rameswar Panda, Yoon Kim

What it does

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

Domain: Language
Task: Language modeling/generation, Question answering

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: 340M
Training data: 15,000,000,000 tokens
Batch size: 500,000

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: 3.1 × 10¹⁹ 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 (unrestricted)
Training code: Open source
Hugging Face: fla-hub

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: Gated Linear Attention Transformers with Hardware-Efficient Training
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run GLA Transformer 340M

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 GLA Transformer 340M

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 it takes to run this model

Minimum card

Tesla C1080

Memory needed

1.1 GB

Fastest

9,965 tok/s

GLA Transformer 340M is small enough at 340M parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The smallest card that holds it is the Tesla C1080 with 4 GB, running it at Q8_0 and producing around 108 tokens per second.

The quickest result comes from a B200 at around 9,965 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

What this model is

GLA Transformer 340M was published by MIT-IBM Watson AI Lab,Massachusetts Institute of Technology (MIT), in United States of America, in August 2024. It comes out of academia,Industry,Academia.

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

Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all. It is published under the fla-hub organisation on Hugging Face.

What decides the speed

The median result is around 279.8 tokens per second; 818 cards produce text faster than most people read it.

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.

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

How it was trained

The training run consumed about 3.1 × 10¹⁹ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

The training set ran to roughly 15,000,000,000 tokens.

Step by step

How to choose a GPU for GLA Transformer 340M

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

Look at what GLA Transformer 340M actually needs — around 1.1 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
02

Set the context length you will work at

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context GLA Transformer 340M can slip off a card that handles short questions easily.
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 GLA Transformer 340M by squeezing it further than you would want.
04

Sort by speed

Sort by speed to see how cards rank for GLA Transformer 340M. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 9,965 tok/s.
05

Read the fit column last

The fit column separates cards that just manage GLA Transformer 340M from those with room to spare. Buy for the second if the context might grow.
06

Open the card you have settled on

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond GLA Transformer 340M.

Answers

GLA Transformer 340M — common questions

Who created GLA Transformer 340M?

GLA Transformer 340M was published by MIT-IBM Watson AI Lab,Massachusetts Institute of Technology (MIT), based in United States of America, categorised as academia,Industry,Academia.

When was GLA Transformer 340M released?

GLA Transformer 340M was published in August 2024. 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 GLA Transformer 340M used for?

GLA Transformer 340M works in Language, and is recorded as handling language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download GLA Transformer 340M?

Its weights are published under the fla-hub organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.

How much compute was used to train GLA Transformer 340M?

Around 3.1 × 10¹⁹ 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 GLA Transformer 340M if it does not fit in my GPU?

It can be split between the card and system memory, but GLA Transformer 340M generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run GLA Transformer 340M faster?

Two cards buy memory rather than speed. That matters for GLA Transformer 340M only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for GLA Transformer 340M?

Because capacity varies, so does how hard GLA Transformer 340M has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these GLA Transformer 340M speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 5,979–15,945 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 GLA Transformer 340M?

The smallest card in our catalogue that holds GLA Transformer 340M is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.1 GB, and produces roughly 108 tokens per second. 818 cards in total can run it.

How fast is GLA Transformer 340M on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 9,965 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 818 of the cards that can run GLA Transformer 340M clear that.

How much VRAM does GLA Transformer 340M need?

About 1.1 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 GLA Transformer 340M on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,856 tokens per second — a comfortable fit.

Can I run GLA Transformer 340M on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,137 tokens per second — a comfortable fit.

Can I run GLA Transformer 340M on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,408 tokens per second — a comfortable fit.

Can I run GLA Transformer 340M on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,669 tokens per second — a comfortable fit.

Is GLA Transformer 340M open source?

Its weights are published, so GLA Transformer 340M 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 GLA Transformer 340M have?

GLA Transformer 340M has 340M parameters. 340m. 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.

Source

Original publication

Record last updated 28 November 2025

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