EfficientNet-B1 TPS calculator

Open weights Google 7.8M parameters May 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 · 4,726 tok/s

Fastest card

B200

434,389 tok/s · 180 GB

Which GPUs can run EfficientNet-B1?

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
434,389 tok/s 260,633–695,023 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	0.7 GB	Q8_0	Comfortable
434,389 tok/s 260,633–695,023 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	0.7 GB	Q8_0	Comfortable
346,871 tok/s 208,122–554,993 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	0.7 GB	Q8_0	Comfortable
346,871 tok/s 208,122–554,993 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	0.7 GB	Q8_0	Comfortable
277,412 tok/s 166,447–443,859 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
265,520 tok/s 159,312–424,833 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.7 GB	Q8_0	Comfortable
265,520 tok/s 159,312–424,833 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.7 GB	Q8_0	Comfortable
254,118 tok/s 152,471–406,588 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	0.7 GB	Q8_0	Comfortable
225,529 tok/s 135,318–360,847 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
225,529 tok/s 135,318–360,847 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
225,529 tok/s 135,318–360,847 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
213,937 tok/s 128,362–342,299 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
182,443 tok/s 109,466–291,910 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
182,443 tok/s 109,466–291,910 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	0.7 GB	Q8_0	Comfortable
182,443 tok/s 109,466–291,910 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
182,443 tok/s 109,466–291,910 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
182,443 tok/s 109,466–291,910 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
138,918 tok/s 83,351–222,268 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	0.7 GB	Q8_0	Comfortable
138,918 tok/s 83,351–222,268 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	0.7 GB	Q8_0	Comfortable
115,765 tok/s 69,459–185,224 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
113,294 tok/s 67,976–181,271 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
110,769 tok/s 66,462–177,231 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	0.7 GB	Q8_0	Comfortable
110,769 tok/s 66,462–177,231 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	0.7 GB	Q8_0	Comfortable
110,769 tok/s 66,462–177,231 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	0.7 GB	Q8_0	Comfortable
110,769 tok/s 66,462–177,231 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	0.7 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: 28 May 2019
Authors: Mingxing Tan, Quoc V. Le

What it does

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

Domain: Vision
Task: Image classification

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: 7.8M
Training data: 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: 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: Unknown
Citations: 23,945

Sources

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

Reference: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run EfficientNet-B1

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 EfficientNet-B1

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

0.7 GB

Fastest

434,389 tok/s

EfficientNet-B1 is small enough at 7.8M 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 4,726 tokens per second.

A B200 is the fastest we calculate for it: about 434,389 tokens per second, from 8,000 GB/s of memory bandwidth.

What this model is

EfficientNet-B1 was published by Google, in United States of America, in May 2019. It comes out of industry.

It works in Vision, and is recorded as doing image classification.

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

What decides the speed

The median result is around 12,197.7 tokens per second; 818 cards produce text faster than most people read it.

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.

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.

Step by step

How to choose a GPU for EfficientNet-B1

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 EfficientNet-B1 actually needs — around 0.7 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

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context EfficientNet-B1 can slip off a card that handles short questions easily.
03

Set a quality floor

Compression is what makes EfficientNet-B1 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

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for EfficientNet-B1. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 434,389 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage EfficientNet-B1 from those with room to spare. Buy for the second if the context might grow.
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 EfficientNet-B1 alone — a card is usually bought for more than one model.

Answers

EfficientNet-B1 — common questions

Can I run EfficientNet-B1 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 0.7 GB and generating roughly 72,760 tokens per second — a comfortable fit.

Is EfficientNet-B1 open source?

Its weights are published, so EfficientNet-B1 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 EfficientNet-B1 have?

EfficientNet-B1 has 7.8M parameters. 7.8M (Figure 1). 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 EfficientNet-B1?

EfficientNet-B1 was published by Google, based in United States of America, categorised as industry.

When was EfficientNet-B1 released?

EfficientNet-B1 was published in May 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 EfficientNet-B1 used for?

EfficientNet-B1 works in Vision, and is recorded as handling image classification. 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 EfficientNet-B1?

The weights for EfficientNet-B1 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 EfficientNet-B1 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 EfficientNet-B1 assume it is fully resident.

Would two GPUs run EfficientNet-B1 faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run EfficientNet-B1 alone, the case for pairing is weak.

Why does the quantisation differ between cards for EfficientNet-B1?

A larger card holds a more accurate copy. Across the cards that run EfficientNet-B1, 1 compression levels are used; the floor control above pins it to one.

How accurate are these EfficientNet-B1 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 260,633–695,023 tok/s on the B200 rather than a single number.

What GPU do I need to run EfficientNet-B1?

The smallest card in our catalogue that holds EfficientNet-B1 is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 0.7 GB, and produces roughly 4,726 tokens per second. 818 cards in total can run it.

How fast is EfficientNet-B1 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 434,389 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 EfficientNet-B1 clear that.

How much VRAM does EfficientNet-B1 need?

About 0.7 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 EfficientNet-B1 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 0.7 GB and generating roughly 80,905 tokens per second — a comfortable fit.

Can I run EfficientNet-B1 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 0.7 GB and generating roughly 49,542 tokens per second — a comfortable fit.

Can I run EfficientNet-B1 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 0.7 GB and generating roughly 61,357 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 25 May 2026

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