RoBERTa Large TPS calculator

Open weights Facebook,University of Washington 355M parameters July 2019

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 · 104 tok/s

Fastest card

B200

9,544 tok/s · 180 GB

Which GPUs can run RoBERTa Large?

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
9,544 tok/s 5,727–15,271 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.1 GB	Q8_0	Comfortable
9,544 tok/s 5,727–15,271 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.1 GB	Q8_0	Comfortable
7,621 tok/s 4,573–12,194 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.1 GB	Q8_0	Comfortable
7,621 tok/s 4,573–12,194 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.1 GB	Q8_0	Comfortable
6,095 tok/s 3,657–9,752 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
5,834 tok/s 3,500–9,334 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.1 GB	Q8_0	Comfortable
5,834 tok/s 3,500–9,334 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.1 GB	Q8_0	Comfortable
5,583 tok/s 3,350–8,933 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.1 GB	Q8_0	Comfortable
4,955 tok/s 2,973–7,928 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
4,955 tok/s 2,973–7,928 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
4,955 tok/s 2,973–7,928 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
4,701 tok/s 2,820–7,521 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,009 tok/s 2,405–6,414 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,009 tok/s 2,405–6,414 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.1 GB	Q8_0	Comfortable
4,009 tok/s 2,405–6,414 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,009 tok/s 2,405–6,414 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,009 tok/s 2,405–6,414 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
3,052 tok/s 1,831–4,884 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.1 GB	Q8_0	Comfortable
3,052 tok/s 1,831–4,884 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.1 GB	Q8_0	Comfortable
2,544 tok/s 1,526–4,070 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
2,489 tok/s 1,494–3,983 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
2,434 tok/s 1,460–3,894 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.1 GB	Q8_0	Comfortable
2,434 tok/s 1,460–3,894 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.1 GB	Q8_0	Comfortable
2,434 tok/s 1,460–3,894 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.1 GB	Q8_0	Comfortable
2,434 tok/s 1,460–3,894 · 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: Facebook,University of Washington
Organisation type: Industry,Academia
Country: United States of America
Published: 1 July 2019
Authors: Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov

What it does

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

Domain: Language
Task: Question answering, Language modeling/generation
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: 355M
Training data: 42,666,666,666 tokens
Epochs: 48

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: 8.5 × 10²¹ FLOP
How it was established: Hardware,Operation counting,Third-party estimation

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 Tesla V100 DGXS 32 GB
Chips used: 1,024
Chip-hours: 122,880
Wall-clock time: 120 hours
Power draw: 526.2 kW
Compute cost: $85,350

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.

Frontier model: Yes
Why it is tracked: Highly cited,SOTA improvement
Record confidence: Confident
Citations: 29,641

Sources

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

Reference: RoBERTa: A Robustly Optimized BERT Pretraining Approach
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run RoBERTa Large

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 RoBERTa Large

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

1.1 GB

Fastest

9,544 tok/s

RoBERTa Large is small enough at 355M 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 104 tokens per second.

The quickest result comes from a B200 at around 9,544 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

RoBERTa Large was published by Facebook,University of Washington, in United States of America, in July 2019. The organisation is categorised as industry,Academia.

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

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

The median result is around 268.0 tokens per second; 817 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.

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.

How it was trained

The training run consumed about 8.5 × 10²¹ FLOP, on NVIDIA Tesla V100 DGXS 32 GB. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

Around 42,666,666,666 tokens went into training it.

It is tracked in the underlying dataset for one reason in particular: highly cited,SOTA improvement.

Step by step

How to choose a GPU for RoBERTa Large

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

The table lists every card that can hold RoBERTa Large — around 1.1 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
02

Decide how long your conversations run

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

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy of RoBERTa Large — Q8_0 on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Compare tokens per second, not specifications

The speed ordering for RoBERTa Large is effectively an ordering by memory bandwidth, which is why the B200 tops it at 9,544 tok/s.
05

Read the fit column last

A tight fit runs RoBERTa Large but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
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 RoBERTa Large is settled.

Answers

RoBERTa Large — common questions

How many parameters does RoBERTa Large have?

RoBERTa Large has 355M parameters. 355M https://github.com/facebookresearch/fairseq/blob/main/examples/roberta/README.md. 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 RoBERTa Large?

RoBERTa Large was published by Facebook,University of Washington, based in United States of America, categorised as industry,Academia.

When was RoBERTa Large released?

RoBERTa Large was published in July 2019. 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 RoBERTa Large used for?

RoBERTa Large works in Language, and is recorded as handling question answering, Language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download RoBERTa Large?

The weights for RoBERTa Large 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 RoBERTa Large?

Around 8.5 × 10²¹ FLOP, on NVIDIA Tesla V100 DGXS 32 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 RoBERTa Large 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 RoBERTa Large assume it is fully resident.

Would two GPUs run RoBERTa Large faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold RoBERTa Large on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for RoBERTa Large?

Each card is shown running the least-compressed copy it can hold, and RoBERTa Large appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these RoBERTa Large speed estimates?

These are estimates with real error bars. The fastest result here, 5,727–15,271 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 RoBERTa Large?

The smallest card in our catalogue that holds RoBERTa Large is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.1 GB, and produces roughly 104 tokens per second. 818 cards in total can run it.

How fast is RoBERTa Large on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 9,544 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 817 of the cards that can run RoBERTa Large clear that.

How much VRAM does RoBERTa Large 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 RoBERTa Large 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 1,778 tokens per second — a comfortable fit.

Can I run RoBERTa Large 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 1,089 tokens per second — a comfortable fit.

Can I run RoBERTa Large 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 1,348 tokens per second — a comfortable fit.

Can I run RoBERTa Large 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 1,599 tokens per second — a comfortable fit.

Is RoBERTa Large open source?

Its weights are published, so RoBERTa Large 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 25 May 2026

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