BigBiGAN TPS calculator

Open weights Google 86M 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 · 429 tok/s

Fastest card

B200

39,398 tok/s · 180 GB

Which GPUs can run BigBiGAN?

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
39,398 tok/s 23,639–63,037 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	0.8 GB	Q8_0	Comfortable
39,398 tok/s 23,639–63,037 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	0.8 GB	Q8_0	Comfortable
31,460 tok/s 18,876–50,337 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	0.8 GB	Q8_0	Comfortable
31,460 tok/s 18,876–50,337 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	0.8 GB	Q8_0	Comfortable
25,161 tok/s 15,096–40,257 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
24,082 tok/s 14,449–38,531 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.8 GB	Q8_0	Comfortable
24,082 tok/s 14,449–38,531 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.8 GB	Q8_0	Comfortable
23,048 tok/s 13,829–36,877 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	0.8 GB	Q8_0	Comfortable
20,455 tok/s 12,273–32,728 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
20,455 tok/s 12,273–32,728 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
20,455 tok/s 12,273–32,728 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
19,404 tok/s 11,642–31,046 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
16,547 tok/s 9,928–26,476 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
16,547 tok/s 9,928–26,476 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	0.8 GB	Q8_0	Comfortable
16,547 tok/s 9,928–26,476 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
16,547 tok/s 9,928–26,476 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
16,547 tok/s 9,928–26,476 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
12,600 tok/s 7,560–20,159 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	0.8 GB	Q8_0	Comfortable
12,600 tok/s 7,560–20,159 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	0.8 GB	Q8_0	Comfortable
10,500 tok/s 6,300–16,799 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
10,276 tok/s 6,165–16,441 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
10,047 tok/s 6,028–16,074 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	0.8 GB	Q8_0	Comfortable
10,047 tok/s 6,028–16,074 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	0.8 GB	Q8_0	Comfortable
10,047 tok/s 6,028–16,074 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	0.8 GB	Q8_0	Comfortable
10,047 tok/s 6,028–16,074 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	0.8 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: Google
Organisation type: Industry
Country: United States of America
Published: 4 July 2019
Authors: Jeff Donahue, Karen Simonyan

What it does

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

Domain: Vision, Image generation
Task: Image completion

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: 86M
Training data: 2,560,000 tokens

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.

Why it is tracked: SOTA improvement
Citations: 576

Sources

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

Reference: Large Scale Adversarial Representation Learning
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run BigBiGAN

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 BigBiGAN

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

0.8 GB

Fastest

39,398 tok/s

BigBiGAN is small enough at 86M 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 429 tokens per second.

A B200 is the fastest we calculate for it: about 39,398 tokens per second, from 8,000 GB/s of memory bandwidth.

What this model is

BigBiGAN was published by Google, in United States of America, in July 2019. industry is the category the publisher falls under.

It works in Vision, Image generation, and is recorded as doing image completion.

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

Across every card that can run it, the middle of the range is about 1,106.3 tokens per second, and 818 of them clear the ten tokens per second that roughly matches reading speed.

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.

What went into building it

The training set ran to roughly 2,560,000 tokens.

It is tracked in the underlying dataset for one reason in particular: sOTA improvement.

Step by step

How to choose a GPU for BigBiGAN

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 BigBiGAN — around 0.8 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
02

Set the context length you will work at

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for BigBiGAN.
03

Set a quality floor

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

Rank by throughput rather than spec sheet

The speed ordering for BigBiGAN is effectively an ordering by memory bandwidth, which is why the B200 tops it at 39,398 tok/s.
05

Read the fit column last

Tight means BigBiGAN 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

See what else that card runs

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

Answers

BigBiGAN — common questions

When was BigBiGAN released?

BigBiGAN 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 BigBiGAN used for?

BigBiGAN works in Vision, Image generation, and is recorded as handling image completion. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download BigBiGAN?

The weights for BigBiGAN 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 BigBiGAN if it does not fit in my GPU?

Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded BigBiGAN is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run BigBiGAN faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run BigBiGAN alone, the case for pairing is weak.

Why does the quantisation differ between cards for BigBiGAN?

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

How accurate are these BigBiGAN speed estimates?

These are estimates with real error bars. The fastest result here, 23,639–63,037 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 BigBiGAN?

The smallest card in our catalogue that holds BigBiGAN is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 0.8 GB, and produces roughly 429 tokens per second. 818 cards in total can run it.

How fast is BigBiGAN on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 39,398 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 BigBiGAN clear that.

How much VRAM does BigBiGAN need?

About 0.8 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 BigBiGAN on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 0.8 GB and generating roughly 7,338 tokens per second — a comfortable fit.

Can I run BigBiGAN on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 0.8 GB and generating roughly 4,493 tokens per second — a comfortable fit.

Can I run BigBiGAN on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 0.8 GB and generating roughly 5,565 tokens per second — a comfortable fit.

Can I run BigBiGAN on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 0.8 GB and generating roughly 6,599 tokens per second — a comfortable fit.

Is BigBiGAN open source?

Its weights are published, so BigBiGAN 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 BigBiGAN have?

BigBiGAN has 86M parameters. https://openai.com/blog/image-gpt/#rfref53. 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 BigBiGAN?

BigBiGAN was published by Google, based in United States of America, categorised as industry.

Source

Original publication

Record last updated 25 May 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.

All AI models