EMDR TPS calculator

Open weights Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),McGill University,DeepMind 440M parameters June 2021

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 · 83.8 tok/s

Fastest card

B200

7,701 tok/s · 180 GB

Which GPUs can run EMDR?

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
7,701 tok/s 4,620–12,321 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.2 GB	Q8_0	Comfortable
7,701 tok/s 4,620–12,321 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.2 GB	Q8_0	Comfortable
6,149 tok/s 3,689–9,839 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.2 GB	Q8_0	Comfortable
6,149 tok/s 3,689–9,839 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.2 GB	Q8_0	Comfortable
4,918 tok/s 2,951–7,868 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.2 GB	Q8_0	Comfortable
4,707 tok/s 2,824–7,531 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.2 GB	Q8_0	Comfortable
4,707 tok/s 2,824–7,531 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.2 GB	Q8_0	Comfortable
4,505 tok/s 2,703–7,208 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.2 GB	Q8_0	Comfortable
3,998 tok/s 2,399–6,397 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.2 GB	Q8_0	Comfortable
3,998 tok/s 2,399–6,397 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.2 GB	Q8_0	Comfortable
3,998 tok/s 2,399–6,397 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.2 GB	Q8_0	Comfortable
3,793 tok/s 2,276–6,068 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.2 GB	Q8_0	Comfortable
3,234 tok/s 1,941–5,175 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.2 GB	Q8_0	Comfortable
3,234 tok/s 1,941–5,175 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.2 GB	Q8_0	Comfortable
3,234 tok/s 1,941–5,175 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.2 GB	Q8_0	Comfortable
3,234 tok/s 1,941–5,175 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.2 GB	Q8_0	Comfortable
3,234 tok/s 1,941–5,175 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.2 GB	Q8_0	Comfortable
2,463 tok/s 1,478–3,940 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.2 GB	Q8_0	Comfortable
2,463 tok/s 1,478–3,940 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.2 GB	Q8_0	Comfortable
2,052 tok/s 1,231–3,284 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.2 GB	Q8_0	Comfortable
2,008 tok/s 1,205–3,213 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.2 GB	Q8_0	Comfortable
1,964 tok/s 1,178–3,142 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.2 GB	Q8_0	Comfortable
1,964 tok/s 1,178–3,142 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.2 GB	Q8_0	Comfortable
1,964 tok/s 1,178–3,142 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.2 GB	Q8_0	Comfortable
1,964 tok/s 1,178–3,142 · 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: Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),McGill University,DeepMind
Organisation type: Academia,Academia,Industry
Country: Canada, United Kingdom of Great Britain and Northern Ireland
Published: 9 June 2021
Authors: Devendra Singh Sachan, Siva Reddy, William Hamilton, Chris Dyer, Dani Yogatama

What it does

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

Domain: Language
Task: Question answering
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: 440M
Training data: 171,600,000,000 tokens
Epochs: 4.05

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: 1.9 × 10²¹ 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 A100
Wall-clock time: 355 hours (14.8 days)
Compute cost: $2,773

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: 191

Sources

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

Reference: End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run EMDR

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 EMDR

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

1.2 GB

Fastest

7,701 tok/s

EMDR is small enough at 440M 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 83.8 tokens per second.

The quickest result comes from a B200 at around 7,701 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Where it came from

EMDR was published by Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),McGill University,DeepMind, in Canada, in June 2021. The organisation is categorised as academia,Academia,Industry.

It works in Language, and is recorded as doing question answering.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.

Understanding the speeds

The median result is around 216.2 tokens per second; 817 cards produce text faster than most people read it.

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

Producing it required around 1.9 × 10²¹ FLOP of arithmetic, on NVIDIA A100, which is a statement about the training budget rather than about inference.

The training set ran to roughly 171,600,000,000 tokens.

The reason it appears in this catalogue at all is sOTA improvement.

Step by step

How to choose a GPU for EMDR

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

01

Read the memory figure first

Look at what EMDR actually needs — around 1.2 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
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 EMDR.
03

Set a quality floor

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

Sort by speed

The speed ordering for EMDR is effectively an ordering by memory bandwidth, which is why the B200 tops it at 7,701 tok/s.
05

Read the fit column last

The fit column separates cards that just manage EMDR from those with room to spare. Buy for the second if the context might grow.
06

See what else that card runs

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for EMDR alone — a card is usually bought for more than one model.

Answers

EMDR — common questions

How much VRAM does EMDR 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 EMDR 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 1,434 tokens per second — a comfortable fit.

Can I run EMDR 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 878 tokens per second — a comfortable fit.

Can I run EMDR 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 1,088 tokens per second — a comfortable fit.

Can I run EMDR 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 1,290 tokens per second — a comfortable fit.

Is EMDR open source?

Its weights are published, so EMDR 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 EMDR have?

EMDR has 440M 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 EMDR?

EMDR was published by Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),McGill University,DeepMind, based in Canada, categorised as academia,Academia,Industry.

When was EMDR released?

EMDR was published in June 2021. 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 EMDR used for?

EMDR works in Language, and is recorded as handling question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download EMDR?

The weights for EMDR 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 EMDR?

Around 1.9 × 10²¹ FLOP, on NVIDIA A100. 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 EMDR if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down. Our figures for EMDR assume it is fully resident.

Would two GPUs run EMDR faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run EMDR alone, the case for pairing is weak.

Why does the quantisation differ between cards for EMDR?

Because capacity varies, so does how hard EMDR has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these EMDR speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 4,620–12,321 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 EMDR?

The smallest card in our catalogue that holds EMDR is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.2 GB, and produces roughly 83.8 tokens per second. 818 cards in total can run it.

How fast is EMDR on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 7,701 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 EMDR clear that.

Source

Original publication

Record last updated 25 May 2026

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