ResNeXt-101 32x48d TPS calculator

Open weights Facebook 829M parameters May 2018

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 · 44.5 tok/s

Fastest card

B200

4,087 tok/s · 180 GB

Which GPUs can run ResNeXt-101 32x48d?

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
4,087 tok/s 2,452–6,539 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.6 GB	Q8_0	Comfortable
4,087 tok/s 2,452–6,539 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.6 GB	Q8_0	Comfortable
3,264 tok/s 1,958–5,222 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.6 GB	Q8_0	Comfortable
3,264 tok/s 1,958–5,222 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.6 GB	Q8_0	Comfortable
2,610 tok/s 1,566–4,176 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.6 GB	Q8_0	Comfortable
2,498 tok/s 1,499–3,997 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.6 GB	Q8_0	Comfortable
2,498 tok/s 1,499–3,997 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.6 GB	Q8_0	Comfortable
2,391 tok/s 1,435–3,826 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.6 GB	Q8_0	Comfortable
2,122 tok/s 1,273–3,395 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.6 GB	Q8_0	Comfortable
2,122 tok/s 1,273–3,395 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.6 GB	Q8_0	Comfortable
2,122 tok/s 1,273–3,395 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.6 GB	Q8_0	Comfortable
2,013 tok/s 1,208–3,221 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.6 GB	Q8_0	Comfortable
1,717 tok/s 1,030–2,747 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.6 GB	Q8_0	Comfortable
1,717 tok/s 1,030–2,747 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.6 GB	Q8_0	Comfortable
1,717 tok/s 1,030–2,747 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.6 GB	Q8_0	Comfortable
1,717 tok/s 1,030–2,747 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.6 GB	Q8_0	Comfortable
1,717 tok/s 1,030–2,747 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.6 GB	Q8_0	Comfortable
1,307 tok/s 784–2,091 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.6 GB	Q8_0	Comfortable
1,307 tok/s 784–2,091 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.6 GB	Q8_0	Comfortable
1,089 tok/s 654–1,743 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.6 GB	Q8_0	Comfortable
1,066 tok/s 640–1,706 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.6 GB	Q8_0	Comfortable
1,042 tok/s 625–1,668 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.6 GB	Q8_0	Comfortable
1,042 tok/s 625–1,668 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.6 GB	Q8_0	Comfortable
1,042 tok/s 625–1,668 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.6 GB	Q8_0	Comfortable
1,042 tok/s 625–1,668 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.6 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: Facebook
Organisation type: Industry
Country: United States of America
Published: 2 May 2018
Authors: Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten

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: 829M
Training data: 940,000,000 tokens

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: 8.7 × 10²¹ FLOP
How it was established: Operation counting

The training run

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

Training hardware: NVIDIA V100
Chips used: 336
Wall-clock time: 496 hours (20.7 days)
Power draw: 209.2 kW
Compute cost: $134,077

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 (non-commercial)
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,SOTA improvement
Record confidence: Confident
Citations: 1,462

Sources

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

Reference: Exploring the Limits of Weakly Supervised Pretraining
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run ResNeXt-101 32x48d

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 ResNeXt-101 32x48d

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 it takes to run this model

Minimum card

Tesla C1080

Memory needed

1.6 GB

Fastest

4,087 tok/s

ResNeXt-101 32x48d reaches a parameter count of 829M. That is small enough that hardware is rarely the obstacle, including on cards several years old. The number of cards we track that can run it: 818.

The smallest card that holds it is Tesla C1080, with a memory capacity of 4 GB, running it at a compression of Q8_0 and producing around 44.5 tokens per second.

At the other end sits B200, generating roughly 4,087 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

Where it came from

ResNeXt-101 32x48d was published by Facebook, in the country recorded as United States of America, during May 2018. The category the publisher falls under is industry.

It works in the domain of Vision, and is recorded as performing the task of image classification.

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

Understanding the speeds

The median result is around 114.8 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 809 of them.

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.

Training and provenance

Training it took a computation budget of roughly 8.7 × 10²¹ FLOP, on hardware recorded as NVIDIA V100. That figure measures what producing the model cost, and has no bearing on how fast it answers.

The training set ran to roughly 940,000,000 tokens of text.

Its inclusion criterion: highly cited,SOTA improvement.

Step by step

How to choose a GPU for ResNeXt-101 32x48d

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

Start from what it actually needs, which is the requirement of ResNeXt-101 32x48d, needing around 1.6 GB at a compression of Q8_0. Capacity is the gate — a card either holds it or it does not.
02

Match the context to your actual use

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason a card that seemed fine stops fitting ResNeXt-101 32x48d.
03

Choose how far you will compress it

Compression is what makes a model fit smaller cards, at some cost in accuracy, reaching a compression of Q8_0 on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second follows memory bandwidth rather than core counts, for ResNeXt-101 32x48d. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 4,087 tok/s.
05

Read the fit column last

The fit column separates cards that just manage it from those with room to spare, in the case of ResNeXt-101 32x48d. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
06

Check the card from the other side

Every card name links to its own page, which runs the same calculation across the whole model catalogue. A card is usually bought for more than one model, so it is worth a look before buying for ResNeXt-101 32x48d.

Answers

ResNeXt-101 32x48d — common questions

ResNeXt-101 32x48d— where can I download it?

The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

ResNeXt-101 32x48d— how much compute was used to train it?

Training consumed around 8.7 × 10²¹ FLOP, on hardware recorded as NVIDIA V100. 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.

ResNeXt-101 32x48d— can I run it if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part held in system memory drags the whole thing down. Every figure here assumes the whole model is resident on the card.

ResNeXt-101 32x48d— would two GPUs run it faster?

Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 818. So a second card is rarely the answer here.

ResNeXt-101 32x48d— why does the quantisation differ between cards?

A larger card holds a more accurate copy. The number of compression levels used across the cards that run it: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

ResNeXt-101 32x48d— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 2,452–6,539 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

ResNeXt-101 32x48d— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Tesla C1080, with a memory capacity of 4 GB. It runs the model at a compression of Q8_0 using about 1.6 GB, and produces roughly 44.5 tokens per second. The number of cards able to run it in total: 818.

ResNeXt-101 32x48d— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 4,087 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and the number of cards clearing that: 809.

ResNeXt-101 32x48d— how much VRAM does it need?

It needs about 1.6 GB at a compression of Q8_0, 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.

ResNeXt-101 32x48d— can I run it on a GPU holding 8 GB?

Yes. The card CMP 170HX 8 GB, holding 8 GB, runs it at a compression of Q8_0, using about 1.6 GB and generating roughly 761 tokens per second. The fit is comfortable.

ResNeXt-101 32x48d— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of Q8_0, using about 1.6 GB and generating roughly 466 tokens per second. The fit is comfortable.

ResNeXt-101 32x48d— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q8_0, using about 1.6 GB and generating roughly 577 tokens per second. The fit is comfortable.

ResNeXt-101 32x48d— can I run it on a GPU holding 24 GB?

Yes. The card GeForce RTX 5090 D V2, holding 24 GB, runs it at a compression of Q8_0, using about 1.6 GB and generating roughly 685 tokens per second. The fit is comfortable.

ResNeXt-101 32x48d— is it open source?

Its weights are published, so it 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.

ResNeXt-101 32x48d— how many parameters does it have?

It has a parameter count of 829M. Table 6. 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.

ResNeXt-101 32x48d— who created it?

It was published by Facebook, based in United States of America, an organisation categorised as industry.

ResNeXt-101 32x48d— when was it released?

It was published in May 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

ResNeXt-101 32x48d— what is it used for?

It works in the domain of Vision, and is recorded as handling the task of image classification. 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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