NLLB TPS calculator

Open weights Meta AI 54.5B parameters July 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

92 cards that can run it

818 cards we hold specifications for

Smallest card that fits

FirePro S9170

32 GB · Q3_K_M · 5.2 tok/s

Fastest card

B200

62.2 tok/s · 180 GB

Which GPUs can run NLLB?

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

92 cards match

Calculating

					Needs	Quantisation	Fit
62.2 tok/s 37–99 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	59.0 GB	Q8_0	Comfortable
62.2 tok/s 37–99 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	59.0 GB	Q8_0	Comfortable
49.6 tok/s 30–79 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	59.0 GB	Q8_0	Comfortable
49.6 tok/s 30–79 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	59.0 GB	Q8_0	Comfortable
39.7 tok/s 24–64 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	59.0 GB	Q8_0	Comfortable
39.2 tok/s 24–63 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	27.3 GB	Q3_K_M	Tight
39.2 tok/s 24–63 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	27.3 GB	Q3_K_M	Tight
38.0 tok/s 23–61 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	59.0 GB	Q8_0	Comfortable
38.0 tok/s 23–61 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	59.0 GB	Q8_0	Comfortable
37.5 tok/s 23–60 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	27.3 GB	Q3_K_M	Tight
37.5 tok/s 23–60 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	27.3 GB	Q3_K_M	Tight
36.4 tok/s 22–58 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	59.0 GB	Q8_0	Comfortable
32.3 tok/s 19–52 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	59.0 GB	Q8_0	Comfortable
32.3 tok/s 19–52 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	59.0 GB	Q8_0	Comfortable
32.3 tok/s 19–52 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	59.0 GB	Q8_0	Comfortable
30.6 tok/s 18–49 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	59.0 GB	Q8_0	Comfortable
28.0 tok/s 17–45 · low confidence	A100 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Jun 2020	33.7 GB	Q4_K_M	Tight
28.0 tok/s 17–45 · low confidence	A100 SXM4 40 GB NVIDIA	40 GB	1,560 GB/s	May 2020	33.7 GB	Q4_K_M	Tight
28.0 tok/s 17–45 · low confidence	A800 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Nov 2022	33.7 GB	Q4_K_M	Tight
26.1 tok/s 16–42 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	59.0 GB	Q8_0	Comfortable
26.1 tok/s 16–42 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	59.0 GB	Q8_0	Comfortable
26.1 tok/s 16–42 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	59.0 GB	Q8_0	Comfortable
26.1 tok/s 16–42 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	59.0 GB	Q8_0	Comfortable
26.1 tok/s 16–42 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	59.0 GB	Q8_0	Comfortable
26.0 tok/s 16–42 · low confidence	GRID A100B NVIDIA	48 GB	1,870 GB/s	May 2020	40.0 GB	Q5_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: Meta AI
Organisation type: Industry
Country: United States of America
Published: 6 July 2022
Authors: Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazi…

What it does

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

Domain: Language
Task: Translation
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: 54.5B
Training data: 300,000,000,000 tokens
Batch size: 1,000,000

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: 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 A100 SXM4 80 GB
Chip-hours: 59,168
Compute cost: $50,667

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.

Why it is tracked: SOTA improvement
Record confidence: Confident
Citations: 1,569

Sources

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

Reference: No Language Left Behind: Scaling Human-Centered Machine Translation
Last updated: 1 January 2026

The extremes

The ten fastest GPUs that run NLLB

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 NLLB

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

FirePro S9170

Memory needed

27.3 GB

Fastest

62.2 tok/s

With 54.5B parameters, NLLB lands in the range a serious desktop card can handle once the weights are compressed. 92 of the cards we track can run it.

The entry point is the FirePro S9170: 32 GB of memory, Q3_K_M compression, roughly 5.2 tokens per second.

The quickest result comes from a B200 at around 62.2 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

NLLB was published by Meta AI, in United States of America, in July 2022. industry is the category the publisher falls under.

It works in Language, and is recorded as doing translation.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.

How fast it runs, and why

The median result is around 15.9 tokens per second; 72 cards produce text faster than most people read it.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

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.

How it was trained

The training run consumed about 1.8 × 10²² FLOP, on NVIDIA A100 SXM4 80 GB. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

It was trained on about 300,000,000,000 tokens of text.

Its inclusion criterion is sOTA improvement.

Step by step

How to choose a GPU for NLLB

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

Every card here has been checked against NLLB — around 27.3 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
02

Match the context to your actual use

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

Choose how far you will compress it

Compression is what makes NLLB fit smaller cards, at some cost in accuracy — Q3_K_M on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for NLLB follows memory bandwidth, not core counts, which is why the B200 tops it at 62.2 tok/s.
05

Check the fit verdict before buying

Tight means NLLB 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 NLLB is settled.

Answers

NLLB — common questions

How many parameters does NLLB have?

NLLB has 54.5B parameters. Section 8.2.4: "The model has a total of 54.5B parameters and FLOPs similar to that of a 3.3B dense model". 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 NLLB?

NLLB was published by Meta AI, based in United States of America, categorised as industry.

When was NLLB released?

NLLB was published in July 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 NLLB used for?

NLLB works in Language, and is recorded as handling translation. 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 NLLB?

The weights for NLLB 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 NLLB?

Around 1.8 × 10²² FLOP, on NVIDIA A100 SXM4 80 GB. 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 NLLB 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 8.5 GB. Our figures for NLLB assume it is fully resident.

Would two GPUs run NLLB faster?

Two cards buy memory rather than speed. That matters for NLLB only if one card cannot hold it — 92 can, so a second adds little.

Why does the quantisation differ between cards for NLLB?

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

How accurate are these NLLB 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 37–99 tok/s on the B200 rather than a single number.

What GPU do I need to run NLLB?

The smallest card in our catalogue that holds NLLB is the FirePro S9170, with 32 GB of memory. It runs the model at Q3_K_M using about 27.3 GB, and produces roughly 5.2 tokens per second. 92 cards in total can run it.

How fast is NLLB on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 62.2 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 72 of the cards that can run NLLB clear that.

How much VRAM does NLLB need?

About 27.3 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.

Is NLLB open source?

Its weights are published, so NLLB 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 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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