DEQ-Transformer (Medium, Adaptive Embedding) TPS calculator

Open weights Carnegie Mellon University (CMU),Intel Labs 110M 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 · 335 tok/s

Fastest card

B200

30,802 tok/s · 180 GB

Which GPUs can run DEQ-Transformer (Medium, Adaptive Embedding)?

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
30,802 tok/s 18,481–49,283 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	0.8 GB	Q8_0	Comfortable
30,802 tok/s 18,481–49,283 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	0.8 GB	Q8_0	Comfortable
24,596 tok/s 14,758–39,354 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	0.8 GB	Q8_0	Comfortable
24,596 tok/s 14,758–39,354 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	0.8 GB	Q8_0	Comfortable
19,671 tok/s 11,803–31,474 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
18,828 tok/s 11,297–30,124 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.8 GB	Q8_0	Comfortable
18,828 tok/s 11,297–30,124 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.8 GB	Q8_0	Comfortable
18,019 tok/s 10,812–28,831 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	0.8 GB	Q8_0	Comfortable
15,992 tok/s 9,595–25,587 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
15,992 tok/s 9,595–25,587 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
15,992 tok/s 9,595–25,587 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
15,170 tok/s 9,102–24,272 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
12,937 tok/s 7,762–20,699 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
12,937 tok/s 7,762–20,699 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	0.8 GB	Q8_0	Comfortable
12,937 tok/s 7,762–20,699 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
12,937 tok/s 7,762–20,699 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
12,937 tok/s 7,762–20,699 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
9,851 tok/s 5,910–15,761 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	0.8 GB	Q8_0	Comfortable
9,851 tok/s 5,910–15,761 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	0.8 GB	Q8_0	Comfortable
8,209 tok/s 4,925–13,134 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
8,034 tok/s 4,820–12,854 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
7,855 tok/s 4,713–12,567 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	0.8 GB	Q8_0	Comfortable
7,855 tok/s 4,713–12,567 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	0.8 GB	Q8_0	Comfortable
7,855 tok/s 4,713–12,567 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	0.8 GB	Q8_0	Comfortable
7,855 tok/s 4,713–12,567 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	0.8 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: Carnegie Mellon University (CMU),Intel Labs
Organisation type: Academia,Industry
Country: United States of America
Published: 3 September 2019
Authors: Shaojie Bai, J. Zico Kolter, Vladlen Koltun

What it does

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

Domain: Language
Task: 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: 110M
Training data: 103,000,000 tokens
Epochs: 12

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: 8.2 × 10¹⁷ FLOP
How it was established: Operation counting

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.

Record confidence: Confident
Citations: 873
Benchmark data: DEQ-Transformer (Medium, Adaptive Embedding)

Sources

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

Reference: Deep Equilibrium Models
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run DEQ-Transformer (Medium, Adaptive Embedding)

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 DEQ-Transformer (Medium, Adaptive Embedding)

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

0.8 GB

Fastest

30,802 tok/s

DEQ-Transformer (Medium, Adaptive Embedding) reaches a parameter count of 110M. 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 335 tokens per second.

At the other end sits B200, generating roughly 30,802 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

What this model is

DEQ-Transformer (Medium, Adaptive Embedding) was published by Carnegie Mellon University (CMU),Intel Labs, in the country recorded as United States of America, during September 2019. It comes out of an organisation categorised as academia,Industry.

It works in the domain of Language, and is recorded as performing the task of language modeling.

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

What decides the speed

Half the cards that hold it manage more than 864.9 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 818 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.

Training and provenance

Training it took a computation budget of roughly 8.2 × 10¹⁷ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.

Training consumed a corpus of around 103,000,000 tokens of text.

Step by step

How to choose a GPU for DEQ-Transformer (Medium, Adaptive Embedding)

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

Start from what it actually needs, which is the requirement of DEQ-Transformer (Medium, Adaptive Embedding), needing around 0.8 GB at a compression of Q8_0. That figure, not the headline performance of a card, is what decides whether it runs.
02

Decide how long your conversations run

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect, because at long context a card that handles short questions easily can be dropped by DEQ-Transformer (Medium, Adaptive Embedding).
03

Decide how much compression you will accept

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

Ranking by tokens per second follows memory bandwidth rather than core counts, for DEQ-Transformer (Medium, Adaptive Embedding). It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 30,802 tok/s.
05

Look at the headroom, not just the fit

Tight means it loads and works with no room to raise the context later, in the case of DEQ-Transformer (Medium, Adaptive Embedding). 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

Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond DEQ-Transformer (Medium, Adaptive Embedding).

Answers

DEQ-Transformer (Medium, Adaptive Embedding) — common questions

DEQ-Transformer (Medium, Adaptive Embedding)— 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.

DEQ-Transformer (Medium, Adaptive Embedding)— 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.

DEQ-Transformer (Medium, Adaptive Embedding)— why does the quantisation differ between cards?

Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

DEQ-Transformer (Medium, Adaptive Embedding)— how accurate are these speed estimates?

These are estimates with real error bars, and any of them could reasonably land anywhere in its published range depending on which runtime you use. The fastest result here: 18,481–49,283 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

DEQ-Transformer (Medium, Adaptive Embedding)— 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 0.8 GB, and produces roughly 335 tokens per second. The number of cards able to run it in total: 818.

DEQ-Transformer (Medium, Adaptive Embedding)— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 30,802 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: 818.

DEQ-Transformer (Medium, Adaptive Embedding)— how much VRAM does it need?

It needs about 0.8 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.

DEQ-Transformer (Medium, Adaptive Embedding)— 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 0.8 GB and generating roughly 5,737 tokens per second. The fit is comfortable.

DEQ-Transformer (Medium, Adaptive Embedding)— 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 0.8 GB and generating roughly 3,513 tokens per second. The fit is comfortable.

DEQ-Transformer (Medium, Adaptive Embedding)— 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 0.8 GB and generating roughly 4,351 tokens per second. The fit is comfortable.

DEQ-Transformer (Medium, Adaptive Embedding)— 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 0.8 GB and generating roughly 5,159 tokens per second. The fit is comfortable.

DEQ-Transformer (Medium, Adaptive Embedding)— 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.

DEQ-Transformer (Medium, Adaptive Embedding)— how many parameters does it have?

It has a parameter count of 110M. 110M Table 3. 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.

DEQ-Transformer (Medium, Adaptive Embedding)— who created it?

It was published by Carnegie Mellon University (CMU),Intel Labs, based in United States of America, an organisation categorised as academia,Industry.

DEQ-Transformer (Medium, Adaptive Embedding)— 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.

DEQ-Transformer (Medium, Adaptive Embedding)— what is it used for?

It works in the domain of Language, and is recorded as handling the task of language modeling. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.

DEQ-Transformer (Medium, Adaptive Embedding)— 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.

DEQ-Transformer (Medium, Adaptive Embedding)— how much compute was used to train it?

Training consumed around 8.2 × 10¹⁷ FLOP. 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.

Source

Original publication

Record last updated 25 May 2026

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