Adaptive Inputs + LayerDrop TPS calculator
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 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
A longer conversation needs more memory, which can push this model off smaller cards.
Hides cards that would only fit the model by compressing it below this point.
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
https://github.com/facebookresearch/fairseq/blob/main/examples/layerdrop/README.md Repo has MIT license WT training: https://github.com/facebookresearch/fairseq/tree/main/examples/language_model
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
"In neural machine translation on newstest2014, our 12 encoder layer Transformer model with LayerDrop further improves the state of the art, reaching 30.2 BLEU"
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.
- 01 B300 288 GB · 8,000 GB/s · Q8_0 8,010 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 8,010 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 6,396 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 6,396 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 5,115 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 4,896 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 4,896 tok/s
- 08 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 4,686 tok/s
- 09 Radeon Instinct MI300A 128 GB · 5,325 GB/s · Q8_0 4,159 tok/s
- 10 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q8_0 4,159 tok/s
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.
- 01 GeForce RTX 4010 4 GB · needs 1.2 GB · Q8_0 · comfortable 96.1 tok/s
- 02 RTX A400 4 GB · needs 1.2 GB · Q8_0 · comfortable 96.1 tok/s
- 03 RTX 500 Mobile Ada Generation 4 GB · needs 1.2 GB · Q8_0 · comfortable 128 tok/s
- 04 GeForce RTX 3050 A Mobile 4 GB · needs 1.2 GB · Q8_0 · comfortable 192 tok/s
- 05 Jetson Orin Nano 4 GB 4 GB · needs 1.2 GB · Q8_0 · comfortable 34.2 tok/s
- 06 Radeon RX 6450M 4 GB · needs 1.2 GB · Q8_0 · comfortable 100.0 tok/s
- 07 Radeon RX 6550M 4 GB · needs 1.2 GB · Q8_0 · comfortable 112 tok/s
- 08 Radeon RX 6550S 4 GB · needs 1.2 GB · Q8_0 · comfortable 100.0 tok/s
- 09 Arc A310 4 GB · needs 1.2 GB · Q8_0 · comfortable 80.7 tok/s
- 10 Arc Pro A30M 4 GB · needs 1.2 GB · Q8_0 · comfortable 83.3 tok/s
What the numbers mean
The hardware side
Minimum card
Tesla C1080
Memory needed
1.2 GB
Fastest
8,010 tok/s
Adaptive Inputs + LayerDrop is small enough at 423M parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.
At the low end, a Tesla C1080 handles it — 4 GB, at Q8_0, for about 87.2 tokens per second.
Top of the range is the B200, at roughly 8,010 tokens per second thanks to 8,000 GB/s of bandwidth.
Where it came from
Adaptive Inputs + LayerDrop was published by Facebook AI Research,LORIA, in United States of America, in September 2019. It comes out of industry,Academia.
It works in Language, and is recorded as doing 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 is about 224.9 tokens per second, and 817 of them clear the ten tokens per second that roughly matches reading speed.
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 about 103,000,000 tokens of text.
Its inclusion criterion is 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
Look at what Adaptive Inputs + LayerDrop actually needs — around 1.2 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
-
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 Adaptive Inputs + LayerDrop stops fitting a card that seemed fine.
-
03
Set a quality floor
Compression is what makes Adaptive Inputs + LayerDrop 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
Sort by speed
Sort by speed to see how cards rank for Adaptive Inputs + LayerDrop. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 8,010 tok/s.
-
05
Check the fit verdict before buying
Tight means Adaptive Inputs + LayerDrop 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
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. Worth a look before buying for Adaptive Inputs + LayerDrop alone — a card is usually bought for more than one model.
Answers
Adaptive Inputs + LayerDrop — common questions
Can I run Adaptive Inputs + LayerDrop on a 24 GB GPU?
Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.2 GB and generating roughly 1,342 tokens per second — a comfortable fit.
Is Adaptive Inputs + LayerDrop open source?
Its weights are published, so Adaptive Inputs + LayerDrop 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 Adaptive Inputs + LayerDrop have?
Adaptive Inputs + LayerDrop has 423M parameters. 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 Adaptive Inputs + LayerDrop?
Adaptive Inputs + LayerDrop was published by Facebook AI Research,LORIA, based in United States of America, categorised as industry,Academia.
When was Adaptive Inputs + LayerDrop released?
Adaptive Inputs + LayerDrop 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.
What is Adaptive Inputs + LayerDrop used for?
Adaptive Inputs + LayerDrop works in Language, and is recorded as handling 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.
Where can I download Adaptive Inputs + LayerDrop?
The weights for Adaptive Inputs + LayerDrop 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 Adaptive Inputs + LayerDrop 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 Adaptive Inputs + LayerDrop is rarely worth using. Every figure here assumes the whole model is on the card.
Would two GPUs run Adaptive Inputs + LayerDrop faster?
A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run Adaptive Inputs + LayerDrop alone, the case for pairing is weak.
Why does the quantisation differ between cards for Adaptive Inputs + LayerDrop?
A larger card holds a more accurate copy. Across the cards that run Adaptive Inputs + LayerDrop, 1 compression levels are used; the floor control above pins it to one.
How accurate are these Adaptive Inputs + LayerDrop speed estimates?
They are calculated from specifications rather than measured, and each carries a range — 4,806–12,816 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 Adaptive Inputs + LayerDrop?
The smallest card in our catalogue that holds Adaptive Inputs + LayerDrop is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.2 GB, and produces roughly 87.2 tokens per second. 818 cards in total can run it.
How fast is Adaptive Inputs + LayerDrop on a GPU?
It depends on the card. The quickest we calculate is a 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 817 of the cards that can run Adaptive Inputs + LayerDrop clear that.
How much VRAM does Adaptive Inputs + LayerDrop need?
About 1.2 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 Adaptive Inputs + LayerDrop on a 8 GB GPU?
Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.2 GB and generating roughly 1,492 tokens per second — a comfortable fit.
Can I run Adaptive Inputs + LayerDrop on a 12 GB GPU?
Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.2 GB and generating roughly 914 tokens per second — a comfortable fit.
Can I run Adaptive Inputs + LayerDrop on a 16 GB GPU?
Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.2 GB and generating roughly 1,131 tokens per second — a comfortable fit.
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.