GPT-2 (355M) TPS calculator

Open weights OpenAI 355M parameters February 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 GPT-2 (355M)?

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: OpenAI
Organisation type: Industry
Country: United States of America
Published: 14 February 2019
Authors: Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever

What it does

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

Domain: Language
Task: Language modeling/generation

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: 10,666,666,667 tokens
Epochs: 100

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: 2.3 × 10²¹ FLOP

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Compute cost: $8,649

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: Unreleased

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
Record confidence: Speculative
Citations: 26,463
Benchmark data: GPT-2 (345M)

Sources

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

Reference: Language Models are Unsupervised Multitask Learners
Last updated: 11 February 2026

The extremes

The ten fastest GPUs that run GPT-2 (355M)

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 GPT-2 (355M)

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 you need to run it

Minimum card

Tesla C1080

Memory needed

1.1 GB

Fastest

9,544 tok/s

GPT-2 (355M) 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 least hardware that works is a Tesla C1080. Its 4 GB is enough at Q8_0 compression, giving 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.

What this model is

GPT-2 (355M) was published by OpenAI, in United States of America, in February 2019. It comes out of industry.

It works in Language, and is recorded as doing 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.

What decides the speed

Half the cards that hold it manage more than 268.0 tokens per second, and 817 exceed reading speed outright.

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

Training it took roughly 2.3 × 10²¹ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.

The training set ran to roughly 10,666,666,667 tokens.

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

Step by step

How to choose a GPU for GPT-2 (355M)

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

Look at what GPT-2 (355M) actually needs — around 1.1 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
02

Match the context to your actual use

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for GPT-2 (355M).
03

Choose how far you will compress it

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 GPT-2 (355M) by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

The speed ordering for GPT-2 (355M) is effectively an ordering by memory bandwidth, which is why the B200 tops it at 9,544 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage GPT-2 (355M) from those with room to spare. Buy for the second if the context might grow.
06

Open the card you have settled on

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for GPT-2 (355M) alone — a card is usually bought for more than one model.

Answers

GPT-2 (355M) — common questions

Why does the quantisation differ between cards for GPT-2 (355M)?

Each card is shown running the least-compressed copy it can hold, and GPT-2 (355M) appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these GPT-2 (355M) 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 GPT-2 (355M)?

The smallest card in our catalogue that holds GPT-2 (355M) 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 GPT-2 (355M) 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 GPT-2 (355M) clear that.

How much VRAM does GPT-2 (355M) 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 GPT-2 (355M) 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 GPT-2 (355M) 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 GPT-2 (355M) 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 GPT-2 (355M) 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 GPT-2 (355M) open source?

Its weights are published, so GPT-2 (355M) 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 GPT-2 (355M) have?

GPT-2 (355M) has 355M parameters. Note that the initial paper release stated GPT-2 medium had 345M parameters. The official github repo notes that this was due to an error: https://github.com/openai/gpt-2?tab=readme-ov-file. 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 GPT-2 (355M)?

GPT-2 (355M) was published by OpenAI, based in United States of America, categorised as industry.

When was GPT-2 (355M) released?

GPT-2 (355M) was published in February 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 GPT-2 (355M) used for?

GPT-2 (355M) works in Language, and is recorded as handling language modeling/generation. 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 GPT-2 (355M)?

The weights for GPT-2 (355M) 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 GPT-2 (355M)?

Around 2.3 × 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.

Can I run GPT-2 (355M) if it does not fit in my GPU?

It can be split between the card and system memory, but GPT-2 (355M) generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run GPT-2 (355M) faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold GPT-2 (355M) on their own, a second card is rarely the answer here.

Source

Original publication

Record last updated 11 February 2026

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