Transformer (Adaptive Input Embeddings) WT103 TPS calculator

Open weights Facebook AI Research 247M parameters September 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 · 149 tok/s

Fastest card

B200

13,718 tok/s · 180 GB

Which GPUs can run Transformer (Adaptive Input Embeddings) WT103?

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.

Search cards

Card memory

Also show cards that cannot run it

818 cards match

Calculating

					Needs	Quantisation	Fit
13,718 tok/s 8,231–21,948 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.0 GB	Q8_0	Comfortable
13,718 tok/s 8,231–21,948 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.0 GB	Q8_0	Comfortable
10,954 tok/s 6,572–17,526 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.0 GB	Q8_0	Comfortable
10,954 tok/s 6,572–17,526 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.0 GB	Q8_0	Comfortable
8,760 tok/s 5,256–14,017 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.0 GB	Q8_0	Comfortable
8,385 tok/s 5,031–13,416 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.0 GB	Q8_0	Comfortable
8,385 tok/s 5,031–13,416 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.0 GB	Q8_0	Comfortable
8,025 tok/s 4,815–12,840 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.0 GB	Q8_0	Comfortable
7,122 tok/s 4,273–11,395 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.0 GB	Q8_0	Comfortable
7,122 tok/s 4,273–11,395 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.0 GB	Q8_0	Comfortable
7,122 tok/s 4,273–11,395 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.0 GB	Q8_0	Comfortable
6,756 tok/s 4,054–10,809 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
5,761 tok/s 3,457–9,218 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
5,761 tok/s 3,457–9,218 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.0 GB	Q8_0	Comfortable
5,761 tok/s 3,457–9,218 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
5,761 tok/s 3,457–9,218 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
5,761 tok/s 3,457–9,218 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
4,387 tok/s 2,632–7,019 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.0 GB	Q8_0	Comfortable
4,387 tok/s 2,632–7,019 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.0 GB	Q8_0	Comfortable
3,656 tok/s 2,193–5,849 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.0 GB	Q8_0	Comfortable
3,578 tok/s 2,147–5,724 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.0 GB	Q8_0	Comfortable
3,498 tok/s 2,099–5,597 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.0 GB	Q8_0	Comfortable
3,498 tok/s 2,099–5,597 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.0 GB	Q8_0	Comfortable
3,498 tok/s 2,099–5,597 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.0 GB	Q8_0	Comfortable
3,498 tok/s 2,099–5,597 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.0 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
Organisation type: Industry
Country: United States of America, France
Published: 28 September 2018
Authors: Alexei Baevski, Michael Auli

What it does

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

Domain: Language
Task: Language modeling
Numerical format: FP16

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: 247M
Training data: 100,000,000 tokens
Epochs: 180

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: 4.5 × 10¹⁹ FLOP
How it was established: Hardware,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: 8
Chip-hours: 4,288
Wall-clock time: 67 hours
Power draw: 5.0 kW
Compute cost: $2,881

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: 433
Benchmark data: Transformer (Adaptive Input Embeddings)

Sources

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

Reference: Adaptive Input Representations for Neural Language Modeling
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run Transformer (Adaptive Input Embeddings) WT103

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 Transformer (Adaptive Input Embeddings) WT103

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

1.0 GB

Fastest

13,718 tok/s

Transformer (Adaptive Input Embeddings) WT103 is small enough at 247M parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The smallest card that holds it is the Tesla C1080 with 4 GB, running it at Q8_0 and producing around 149 tokens per second.

Top of the range is the B200, at roughly 13,718 tokens per second thanks to 8,000 GB/s of bandwidth.

Background

Transformer (Adaptive Input Embeddings) WT103 was published by Facebook AI Research, in United States of America, in September 2018. industry is the category the publisher falls under.

It works in Language, and is recorded as doing language modeling.

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 385.2 tokens per second, and 818 exceed reading speed outright.

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.

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

Training it took roughly 4.5 × 10¹⁹ FLOP of computation, on NVIDIA V100 — a measure of what producing the model cost, not of how fast it answers.

It was trained on about 100,000,000 tokens of text.

Its inclusion criterion is sOTA improvement.

Step by step

How to choose a GPU for Transformer (Adaptive Input Embeddings) WT103

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

01

Start from the memory column

Every card here has been checked against Transformer (Adaptive Input Embeddings) WT103 — around 1.0 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
02

Match the context to your actual use

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Transformer (Adaptive Input Embeddings) WT103.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy of Transformer (Adaptive Input Embeddings) WT103 — Q8_0 on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for Transformer (Adaptive Input Embeddings) WT103. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 13,718 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage Transformer (Adaptive Input Embeddings) WT103 from those with room to spare. Buy for the second if the context might grow.
06

See what else that card runs

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Transformer (Adaptive Input Embeddings) WT103.

Answers

Transformer (Adaptive Input Embeddings) WT103 — common questions

Can I run Transformer (Adaptive Input Embeddings) WT103 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.0 GB and generating roughly 1,564 tokens per second — a comfortable fit.

Can I run Transformer (Adaptive Input Embeddings) WT103 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.0 GB and generating roughly 1,938 tokens per second — a comfortable fit.

Can I run Transformer (Adaptive Input Embeddings) WT103 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.0 GB and generating roughly 2,298 tokens per second — a comfortable fit.

Is Transformer (Adaptive Input Embeddings) WT103 open source?

Its weights are published, so Transformer (Adaptive Input Embeddings) WT103 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 Transformer (Adaptive Input Embeddings) WT103 have?

Transformer (Adaptive Input Embeddings) WT103 has 247M parameters. Table 2. 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 Transformer (Adaptive Input Embeddings) WT103?

Transformer (Adaptive Input Embeddings) WT103 was published by Facebook AI Research, based in United States of America, categorised as industry.

When was Transformer (Adaptive Input Embeddings) WT103 released?

Transformer (Adaptive Input Embeddings) WT103 was published in September 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.

What is Transformer (Adaptive Input Embeddings) WT103 used for?

Transformer (Adaptive Input Embeddings) WT103 works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Transformer (Adaptive Input Embeddings) WT103?

The weights for Transformer (Adaptive Input Embeddings) WT103 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 Transformer (Adaptive Input Embeddings) WT103?

Around 4.5 × 10¹⁹ FLOP, on 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.

Can I run Transformer (Adaptive Input Embeddings) WT103 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 Transformer (Adaptive Input Embeddings) WT103 is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run Transformer (Adaptive Input Embeddings) WT103 faster?

Two cards buy memory rather than speed. That matters for Transformer (Adaptive Input Embeddings) WT103 only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for Transformer (Adaptive Input Embeddings) WT103?

Because capacity varies, so does how hard Transformer (Adaptive Input Embeddings) WT103 has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Transformer (Adaptive Input Embeddings) WT103 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 8,231–21,948 tok/s on the B200 rather than a single number.

What GPU do I need to run Transformer (Adaptive Input Embeddings) WT103?

The smallest card in our catalogue that holds Transformer (Adaptive Input Embeddings) WT103 is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.0 GB, and produces roughly 149 tokens per second. 818 cards in total can run it.

How fast is Transformer (Adaptive Input Embeddings) WT103 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 13,718 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 Transformer (Adaptive Input Embeddings) WT103 clear that.

How much VRAM does Transformer (Adaptive Input Embeddings) WT103 need?

About 1.0 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 Transformer (Adaptive Input Embeddings) WT103 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.0 GB and generating roughly 2,555 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 25 May 2026

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.

All AI models