Adaptive Inputs + LayerDrop TPS calculator

Open weights Facebook AI Research,LORIA 423M parameters September 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 · 87.2 tok/s

Fastest card

B200

8,010 tok/s · 180 GB

Which GPUs can run Adaptive Inputs + LayerDrop?

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
8,010 tok/s 4,806–12,816 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.2 GB	Q8_0	Comfortable
8,010 tok/s 4,806–12,816 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.2 GB	Q8_0	Comfortable
6,396 tok/s 3,838–10,234 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.2 GB	Q8_0	Comfortable
6,396 tok/s 3,838–10,234 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.2 GB	Q8_0	Comfortable
5,115 tok/s 3,069–8,185 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.2 GB	Q8_0	Comfortable
4,896 tok/s 2,938–7,834 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.2 GB	Q8_0	Comfortable
4,896 tok/s 2,938–7,834 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.2 GB	Q8_0	Comfortable
4,686 tok/s 2,812–7,497 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.2 GB	Q8_0	Comfortable
4,159 tok/s 2,495–6,654 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.2 GB	Q8_0	Comfortable
4,159 tok/s 2,495–6,654 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.2 GB	Q8_0	Comfortable
4,159 tok/s 2,495–6,654 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.2 GB	Q8_0	Comfortable
3,945 tok/s 2,367–6,312 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.2 GB	Q8_0	Comfortable
3,364 tok/s 2,019–5,383 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.2 GB	Q8_0	Comfortable
3,364 tok/s 2,019–5,383 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.2 GB	Q8_0	Comfortable
3,364 tok/s 2,019–5,383 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.2 GB	Q8_0	Comfortable
3,364 tok/s 2,019–5,383 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.2 GB	Q8_0	Comfortable
3,364 tok/s 2,019–5,383 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.2 GB	Q8_0	Comfortable
2,562 tok/s 1,537–4,099 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.2 GB	Q8_0	Comfortable
2,562 tok/s 1,537–4,099 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.2 GB	Q8_0	Comfortable
2,135 tok/s 1,281–3,415 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.2 GB	Q8_0	Comfortable
2,089 tok/s 1,253–3,343 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.2 GB	Q8_0	Comfortable
2,043 tok/s 1,226–3,268 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.2 GB	Q8_0	Comfortable
2,043 tok/s 1,226–3,268 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.2 GB	Q8_0	Comfortable
2,043 tok/s 1,226–3,268 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.2 GB	Q8_0	Comfortable
2,043 tok/s 1,226–3,268 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.2 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 AI Research,LORIA
Organisation type: Industry,Academia
Country: United States of America, France
Published: 25 September 2019
Authors: Angela Fan, Edouard Grave, Armand Joulin

What it does

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

Domain: Language
Task: Language modeling/generation, Translation, Question answering, Text summarization, Language modeling

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: 423M
Training data: 103,000,000 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: Open source

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
Record confidence: Confident
Citations: 702
Benchmark data: Adaptive Inputs + LayerDrop

Sources

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

Reference: Reducing Transformer Depth on Demand with Structured Dropout
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run Adaptive Inputs + LayerDrop

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 Adaptive Inputs + LayerDrop

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

The hardware side

Minimum card

Tesla C1080

Memory needed

1.2 GB

Fastest

8,010 tok/s

Adaptive Inputs + LayerDrop reaches a parameter count of 423M. 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.

At the low end it is handled by Tesla C1080, with a memory capacity of 4 GB, running it at a compression of Q8_0 and producing around 87.2 tokens per second.

Top of the range is B200, generating roughly 8,010 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

Where it came from

Adaptive Inputs + LayerDrop was published by Facebook AI Research,LORIA, in the country recorded as United States of America, during September 2019. It comes out of an organisation categorised as industry,Academia.

It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Translation, Question answering, Text summarization, Language modeling.

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

Understanding the speeds

Across every card that can run it, the middle of the range sits at 224.9 tokens per second. Exceeding reading speed outright: 817 of them.

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

It was trained on a corpus of about 103,000,000 tokens of text.

Its inclusion criterion: sOTA improvement.

Step by step

How to choose a GPU for Adaptive Inputs + LayerDrop

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 Adaptive Inputs + LayerDrop, needing around 1.2 GB at a compression of Q8_0. Capacity is the gate — a card either holds it or it does not.
02

Set the context length you will work at

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 Adaptive Inputs + LayerDrop.
03

Set a quality floor

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

Sort by speed

Sort by speed to see how cards rank for Adaptive Inputs + LayerDrop. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 8,010 tok/s.
05

Check the fit verdict before buying

Tight means it loads and works with no room to raise the context later, in the case of Adaptive Inputs + LayerDrop. 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

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. A card is usually bought for more than one model, so it is worth a look before buying for Adaptive Inputs + LayerDrop.

Answers

Adaptive Inputs + LayerDrop — common questions

Adaptive Inputs + LayerDrop— 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 1.2 GB and generating roughly 1,342 tokens per second. The fit is comfortable.

Adaptive Inputs + LayerDrop— 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.

Adaptive Inputs + LayerDrop— how many parameters does it have?

It has a parameter count of 423M. 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.

Adaptive Inputs + LayerDrop— who created it?

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

Adaptive Inputs + LayerDrop— when was it released?

It was published in September 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.

Adaptive Inputs + LayerDrop— what is it used for?

It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Translation, Question answering, Text summarization, Language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Adaptive Inputs + LayerDrop— 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.

Adaptive Inputs + LayerDrop— can I run it 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 model is rarely worth using. Every figure here assumes the whole model is resident on the card.

Adaptive Inputs + LayerDrop— would two GPUs run it faster?

A second card roughly doubles the memory available but not the generation rate. The number already able to run it alone: 818. So a second card is rarely the answer here.

Adaptive Inputs + LayerDrop— 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.

Adaptive Inputs + LayerDrop— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 4,806–12,816 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Adaptive Inputs + LayerDrop— 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 1.2 GB, and produces roughly 87.2 tokens per second. The number of cards able to run it in total: 818.

Adaptive Inputs + LayerDrop— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 8,010 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: 817.

Adaptive Inputs + LayerDrop— how much VRAM does it need?

It needs about 1.2 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.

Adaptive Inputs + LayerDrop— 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 1.2 GB and generating roughly 1,492 tokens per second. The fit is comfortable.

Adaptive Inputs + LayerDrop— 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 1.2 GB and generating roughly 914 tokens per second. The fit is comfortable.

Adaptive Inputs + LayerDrop— 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 1.2 GB and generating roughly 1,131 tokens per second. The fit is comfortable.

Source

Original publication

Record last updated 25 May 2026

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