Engine-XL(NE) TPS calculator

Open weights Boston University 1.5B parameters December 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 · 24.6 tok/s

Fastest card

B200

2,259 tok/s · 180 GB

Which GPUs can run Engine-XL(NE)?

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
2,259 tok/s 1,355–3,614 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	2.3 GB	Q8_0	Comfortable
2,259 tok/s 1,355–3,614 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	2.3 GB	Q8_0	Comfortable
1,804 tok/s 1,082–2,886 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	2.3 GB	Q8_0	Comfortable
1,804 tok/s 1,082–2,886 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	2.3 GB	Q8_0	Comfortable
1,443 tok/s 866–2,308 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	2.3 GB	Q8_0	Comfortable
1,381 tok/s 828–2,209 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.3 GB	Q8_0	Comfortable
1,381 tok/s 828–2,209 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.3 GB	Q8_0	Comfortable
1,321 tok/s 793–2,114 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	2.3 GB	Q8_0	Comfortable
1,173 tok/s 704–1,876 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	2.3 GB	Q8_0	Comfortable
1,173 tok/s 704–1,876 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	2.3 GB	Q8_0	Comfortable
1,173 tok/s 704–1,876 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	2.3 GB	Q8_0	Comfortable
1,112 tok/s 667–1,780 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
722 tok/s 433–1,156 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	2.3 GB	Q8_0	Comfortable
722 tok/s 433–1,156 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	2.3 GB	Q8_0	Comfortable
602 tok/s 361–963 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	2.3 GB	Q8_0	Comfortable
589 tok/s 353–943 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	2.3 GB	Q8_0	Comfortable
576 tok/s 346–922 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	2.3 GB	Q8_0	Comfortable
576 tok/s 346–922 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	2.3 GB	Q8_0	Comfortable
576 tok/s 346–922 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	2.3 GB	Q8_0	Comfortable
576 tok/s 346–922 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	2.3 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: Boston University
Organisation type: Academia
Country: United States of America
Published: 11 December 2021
Authors: Zhongping Zhang, Yiwen Gu, Bryan A. Plummer

What it does

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

Domain: Language, Vision, Multimodal
Task: Named entity recognition (NER)
Base model: GPT-2 (1.5B)

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: 1.5B
Training data: 602,871,200 tokens
Epochs: 3

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.

Citations: 3
Benchmark data: Engine-XL(NE)

Sources

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

Reference: Show and Write: Entity-aware Article Generation with Image Information
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run Engine-XL(NE)

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 Engine-XL(NE)

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

2.3 GB

Fastest

2,259 tok/s

Engine-XL(NE) is small enough at 1.5B 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 24.6 tokens per second.

The quickest result comes from a B200 at around 2,259 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

What this model is

Engine-XL(NE) was published by Boston University, in United States of America, in December 2021. academia is the category the publisher falls under.

It works in Language, Vision, Multimodal, and is recorded as doing named entity recognition (NER).

It is derived from GPT-2 (1.5B) rather than trained from scratch, which is the usual way a specialised model is produced.

The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.

What decides the speed

Across every card that can run it, the middle of the range is about 63.4 tokens per second, and 796 of them clear the ten tokens per second that roughly matches reading speed.

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

Around 602,871,200 tokens went into training it.

Step by step

How to choose a GPU for Engine-XL(NE)

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

The table lists every card that can hold Engine-XL(NE) — around 2.3 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
02

Match the context to your actual use

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason Engine-XL(NE) stops fitting a card that seemed fine.
03

Decide how much compression you will accept

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

Sort by speed

Sort by speed to see how cards rank for Engine-XL(NE). It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 2,259 tok/s.
05

Read the fit column last

A tight fit runs Engine-XL(NE) but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Check the card from the other side

Following a card through to its own page shows every other model it can hold, which is the question that follows once Engine-XL(NE) is settled.

Answers

Engine-XL(NE) — common questions

What GPU do I need to run Engine-XL(NE)?

The smallest card in our catalogue that holds Engine-XL(NE) is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 2.3 GB, and produces roughly 24.6 tokens per second. 818 cards in total can run it.

How fast is Engine-XL(NE) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 2,259 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 796 of the cards that can run Engine-XL(NE) clear that.

How much VRAM does Engine-XL(NE) need?

About 2.3 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 Engine-XL(NE) on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 2.3 GB and generating roughly 421 tokens per second — a comfortable fit.

Can I run Engine-XL(NE) on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 2.3 GB and generating roughly 258 tokens per second — a comfortable fit.

Can I run Engine-XL(NE) on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 2.3 GB and generating roughly 319 tokens per second — a comfortable fit.

Can I run Engine-XL(NE) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 2.3 GB and generating roughly 378 tokens per second — a comfortable fit.

Is Engine-XL(NE) open source?

Its weights are published, so Engine-XL(NE) 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 Engine-XL(NE) have?

Engine-XL(NE) has 1.5B parameters. "ENGINE-XL has 48 layers and 1.5 billion parameters, on par with GPT2-1.5B and GROVER-Mega". 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 Engine-XL(NE)?

Engine-XL(NE) was published by Boston University, based in United States of America, categorised as academia.

When was Engine-XL(NE) released?

Engine-XL(NE) was published in December 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 Engine-XL(NE) used for?

Engine-XL(NE) works in Language, Vision, Multimodal, and is recorded as handling named entity recognition (NER). These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Engine-XL(NE)?

The weights for Engine-XL(NE) 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 Engine-XL(NE) 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 Engine-XL(NE) assume it is fully resident.

Would two GPUs run Engine-XL(NE) faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run Engine-XL(NE) alone, the case for pairing is weak.

Why does the quantisation differ between cards for Engine-XL(NE)?

Each card is shown running the least-compressed copy it can hold, and Engine-XL(NE) appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Engine-XL(NE) 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 1,355–3,614 tok/s on the B200 rather than a single number.

Source

Original publication

Record last updated 25 May 2026

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