OPT-2.7B (finetuned on PTB) TPS calculator

Open weights Meta AI 2.7B parameters June 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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q8_0 · 13.7 tok/s

Fastest card

B200

1,255 tok/s · 180 GB

Which GPUs can run OPT-2.7B (finetuned on PTB)?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
1,255 tok/s 753–2,008 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	3.6 GB	Q8_0	Comfortable
1,255 tok/s 753–2,008 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	3.6 GB	Q8_0	Comfortable
1,002 tok/s 601–1,603 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	3.6 GB	Q8_0	Comfortable
1,002 tok/s 601–1,603 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	3.6 GB	Q8_0	Comfortable
801 tok/s 481–1,282 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	3.6 GB	Q8_0	Comfortable
767 tok/s 460–1,227 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.6 GB	Q8_0	Comfortable
767 tok/s 460–1,227 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.6 GB	Q8_0	Comfortable
734 tok/s 440–1,175 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	3.6 GB	Q8_0	Comfortable
652 tok/s 391–1,042 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	3.6 GB	Q8_0	Comfortable
652 tok/s 391–1,042 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	3.6 GB	Q8_0	Comfortable
652 tok/s 391–1,042 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	3.6 GB	Q8_0	Comfortable
618 tok/s 371–989 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	3.6 GB	Q8_0	Comfortable
527 tok/s 316–843 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.6 GB	Q8_0	Comfortable
527 tok/s 316–843 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	3.6 GB	Q8_0	Comfortable
527 tok/s 316–843 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	3.6 GB	Q8_0	Comfortable
527 tok/s 316–843 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.6 GB	Q8_0	Comfortable
527 tok/s 316–843 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	3.6 GB	Q8_0	Comfortable
401 tok/s 241–642 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	3.6 GB	Q8_0	Comfortable
401 tok/s 241–642 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	3.6 GB	Q8_0	Comfortable
334 tok/s 201–535 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	3.6 GB	Q8_0	Comfortable
327 tok/s 196–524 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	3.6 GB	Q8_0	Comfortable
320 tok/s 192–512 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	3.6 GB	Q8_0	Comfortable
320 tok/s 192–512 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	3.6 GB	Q8_0	Comfortable
320 tok/s 192–512 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	3.6 GB	Q8_0	Comfortable
320 tok/s 192–512 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	3.6 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: Meta AI
Organisation type: Industry
Country: United States of America
Published: 21 June 2022
Authors: Susan Zhang∗ , Stephen Roller∗ , Naman Goyal∗ , Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott† , Sam Shleifer† , Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, Luke Zettlemoyer

What it does

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

Domain: Language
Task: Language modeling, Chat, 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: 2.7B
Training data: tokens
Epochs: 1.67

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.

Record confidence: Confident
Citations: 4,716
Benchmark data: OPT-2.7B (finetuned on PTB)

Sources

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

Reference: OPT: Open Pre-trained Transformer Language Models
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run OPT-2.7B (finetuned on PTB)

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 OPT-2.7B (finetuned on PTB)

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

3.6 GB

Fastest

1,255 tok/s

OPT-2.7B (finetuned on PTB) is small enough at 2.7B 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 13.7 tokens per second.

Top of the range is the B200, at roughly 1,255 tokens per second thanks to 8,000 GB/s of bandwidth.

Where it came from

OPT-2.7B (finetuned on PTB) was published by Meta AI, in United States of America, in June 2022. The organisation is categorised as industry.

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

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

Understanding the speeds

Half the cards that hold it manage more than 35.2 tokens per second, and 775 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.

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.

Step by step

How to choose a GPU for OPT-2.7B (finetuned on PTB)

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

Every card here has been checked against OPT-2.7B (finetuned on PTB) — around 3.6 GB at Q8_0. 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 OPT-2.7B (finetuned on PTB) can slip off a card that handles short questions easily.
03

Choose how far you will compress it

Compression is what makes OPT-2.7B (finetuned on PTB) fit smaller cards, at some cost in accuracy — Q8_0 on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for OPT-2.7B (finetuned on PTB). It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 1,255 tok/s.
05

Look at the headroom, not just the fit

Tight means OPT-2.7B (finetuned on PTB) 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

Open the card you have settled on

Following a card through to its own page shows every other model it can hold, which is the question that follows once OPT-2.7B (finetuned on PTB) is settled.

Answers

OPT-2.7B (finetuned on PTB) — common questions

Where can I download OPT-2.7B (finetuned on PTB)?

The weights for OPT-2.7B (finetuned on PTB) are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Can I run OPT-2.7B (finetuned on PTB) 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 OPT-2.7B (finetuned on PTB) assume it is fully resident.

Would two GPUs run OPT-2.7B (finetuned on PTB) faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run OPT-2.7B (finetuned on PTB) alone, the case for pairing is weak.

Why does the quantisation differ between cards for OPT-2.7B (finetuned on PTB)?

Because capacity varies, so does how hard OPT-2.7B (finetuned on PTB) has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these OPT-2.7B (finetuned on PTB) speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 753–2,008 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 OPT-2.7B (finetuned on PTB)?

The smallest card in our catalogue that holds OPT-2.7B (finetuned on PTB) is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 3.6 GB, and produces roughly 13.7 tokens per second. 818 cards in total can run it.

How fast is OPT-2.7B (finetuned on PTB) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,255 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 775 of the cards that can run OPT-2.7B (finetuned on PTB) clear that.

How much VRAM does OPT-2.7B (finetuned on PTB) need?

About 3.6 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 OPT-2.7B (finetuned on PTB) on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 3.6 GB and generating roughly 234 tokens per second — a comfortable fit.

Can I run OPT-2.7B (finetuned on PTB) on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 3.6 GB and generating roughly 143 tokens per second — a comfortable fit.

Can I run OPT-2.7B (finetuned on PTB) on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 3.6 GB and generating roughly 177 tokens per second — a comfortable fit.

Can I run OPT-2.7B (finetuned on PTB) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 3.6 GB and generating roughly 210 tokens per second — a comfortable fit.

Is OPT-2.7B (finetuned on PTB) open source?

Its weights are published, so OPT-2.7B (finetuned on PTB) 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 OPT-2.7B (finetuned on PTB) have?

OPT-2.7B (finetuned on PTB) has 2.7B parameters. 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 OPT-2.7B (finetuned on PTB)?

OPT-2.7B (finetuned on PTB) was published by Meta AI, based in United States of America, categorised as industry.

When was OPT-2.7B (finetuned on PTB) released?

OPT-2.7B (finetuned on PTB) was published in June 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 OPT-2.7B (finetuned on PTB) used for?

OPT-2.7B (finetuned on PTB) works in Language, and is recorded as handling language modeling, Chat, Language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Source

Original publication

Record last updated 25 May 2026

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