ResNet-101 (ImageNet) TPS calculator

Open weights Microsoft 44.5M parameters December 2015

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 · 828 tok/s

Fastest card

B200

76,140 tok/s · 180 GB

Which GPUs can run ResNet-101 (ImageNet)?

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
76,140 tok/s 45,684–121,824 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	0.7 GB	Q8_0	Comfortable
76,140 tok/s 45,684–121,824 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	0.7 GB	Q8_0	Comfortable
60,800 tok/s 36,480–97,280 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	0.7 GB	Q8_0	Comfortable
60,800 tok/s 36,480–97,280 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	0.7 GB	Q8_0	Comfortable
48,625 tok/s 29,175–77,800 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
46,541 tok/s 27,924–74,465 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.7 GB	Q8_0	Comfortable
46,541 tok/s 27,924–74,465 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.7 GB	Q8_0	Comfortable
44,542 tok/s 26,725–71,267 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	0.7 GB	Q8_0	Comfortable
39,531 tok/s 23,719–63,250 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
39,531 tok/s 23,719–63,250 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
39,531 tok/s 23,719–63,250 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
37,499 tok/s 22,499–59,998 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
31,979 tok/s 19,187–51,166 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
31,979 tok/s 19,187–51,166 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	0.7 GB	Q8_0	Comfortable
31,979 tok/s 19,187–51,166 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
31,979 tok/s 19,187–51,166 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
31,979 tok/s 19,187–51,166 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
24,350 tok/s 14,610–38,959 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	0.7 GB	Q8_0	Comfortable
24,350 tok/s 14,610–38,959 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	0.7 GB	Q8_0	Comfortable
20,291 tok/s 12,175–32,466 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
19,858 tok/s 11,915–31,773 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
19,416 tok/s 11,649–31,065 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	0.7 GB	Q8_0	Comfortable
19,416 tok/s 11,649–31,065 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	0.7 GB	Q8_0	Comfortable
19,416 tok/s 11,649–31,065 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	0.7 GB	Q8_0	Comfortable
19,416 tok/s 11,649–31,065 · 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: Microsoft
Organisation type: Industry
Country: United States of America
Published: 10 December 2015
Authors: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

What it does

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

Domain: Vision
Task: Image classification
Approach: Supervised

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: 44.5M
Training data: 1,280,000 tokens
Epochs: 120

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: 7 × 10¹⁸ FLOP
How it was established: Operation counting

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)

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: Highly cited
Record confidence: Confident
Citations: 228,517

Sources

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

Reference: Deep Residual Learning for Image Recognition
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run ResNet-101 (ImageNet)

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 ResNet-101 (ImageNet)

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

0.7 GB

Fastest

76,140 tok/s

ResNet-101 (ImageNet) is small enough at 44.5M 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 828 tokens per second.

Top of the range is the B200, at roughly 76,140 tokens per second thanks to 8,000 GB/s of bandwidth.

Background

ResNet-101 (ImageNet) was published by Microsoft, in United States of America, in December 2015. It comes out of industry.

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

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.

Reading the throughput figures

Half the cards that hold it manage more than 2,138.0 tokens per second, and 818 exceed reading speed outright.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

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.

What went into building it

The training run consumed about 7 × 10¹⁸ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

The training set ran to roughly 1,280,000 tokens.

Its inclusion criterion is highly cited.

Step by step

How to choose a GPU for ResNet-101 (ImageNet)

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

Every card here has been checked against ResNet-101 (ImageNet) — around 0.7 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
02

Decide how long your conversations run

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for ResNet-101 (ImageNet).
03

Choose how far you will compress it

Compression is what makes ResNet-101 (ImageNet) 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

Ranking by tokens per second for ResNet-101 (ImageNet) follows memory bandwidth, not core counts, which is why the B200 tops it at 76,140 tok/s.
05

Check the fit verdict before buying

Tight means ResNet-101 (ImageNet) 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

Following a card through to its own page shows every other model it can hold, which is the question that follows once ResNet-101 (ImageNet) is settled.

Answers

ResNet-101 (ImageNet) — common questions

Would two GPUs run ResNet-101 (ImageNet) faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold ResNet-101 (ImageNet) on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for ResNet-101 (ImageNet)?

A larger card holds a more accurate copy. Across the cards that run ResNet-101 (ImageNet), 1 compression levels are used; the floor control above pins it to one.

How accurate are these ResNet-101 (ImageNet) speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 45,684–121,824 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 ResNet-101 (ImageNet)?

The smallest card in our catalogue that holds ResNet-101 (ImageNet) is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 0.7 GB, and produces roughly 828 tokens per second. 818 cards in total can run it.

How fast is ResNet-101 (ImageNet) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 76,140 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 ResNet-101 (ImageNet) clear that.

How much VRAM does ResNet-101 (ImageNet) 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 ResNet-101 (ImageNet) 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 14,181 tokens per second — a comfortable fit.

Can I run ResNet-101 (ImageNet) 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 8,684 tokens per second — a comfortable fit.

Can I run ResNet-101 (ImageNet) 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 10,755 tokens per second — a comfortable fit.

Can I run ResNet-101 (ImageNet) 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 12,753 tokens per second — a comfortable fit.

Is ResNet-101 (ImageNet) open source?

Its weights are published, so ResNet-101 (ImageNet) 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 ResNet-101 (ImageNet) have?

ResNet-101 (ImageNet) has 44.5M parameters. Taken from https://arxiv.org/abs/1605.07146. 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 ResNet-101 (ImageNet)?

ResNet-101 (ImageNet) was published by Microsoft, based in United States of America, categorised as industry.

When was ResNet-101 (ImageNet) released?

ResNet-101 (ImageNet) was published in December 2015. 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 ResNet-101 (ImageNet) used for?

ResNet-101 (ImageNet) works in Vision, and is recorded as handling image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download ResNet-101 (ImageNet)?

The weights for ResNet-101 (ImageNet) 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 ResNet-101 (ImageNet)?

Around 7 × 10¹⁸ 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 ResNet-101 (ImageNet) 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 ResNet-101 (ImageNet) is rarely worth using. Every figure here assumes the whole model is on the card.

Source

Original publication

Record last updated 25 May 2026

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