Engine-Base (NE) TPS calculator

Open weights Boston University 124M 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 · 297 tok/s

Fastest card

B200

27,324 tok/s · 180 GB

Which GPUs can run Engine-Base (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.

Search cards

Card memory

Also show cards that cannot run it

818 cards match

Calculating

					Needs	Quantisation	Fit
27,324 tok/s 16,395–43,719 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	0.8 GB	Q8_0	Comfortable
27,324 tok/s 16,395–43,719 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	0.8 GB	Q8_0	Comfortable
21,819 tok/s 13,092–34,911 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	0.8 GB	Q8_0	Comfortable
21,819 tok/s 13,092–34,911 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	0.8 GB	Q8_0	Comfortable
17,450 tok/s 10,470–27,920 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
16,702 tok/s 10,021–26,723 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.8 GB	Q8_0	Comfortable
16,702 tok/s 10,021–26,723 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.8 GB	Q8_0	Comfortable
15,985 tok/s 9,591–25,576 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	0.8 GB	Q8_0	Comfortable
14,187 tok/s 8,512–22,698 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
14,187 tok/s 8,512–22,698 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
14,187 tok/s 8,512–22,698 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
13,457 tok/s 8,074–21,532 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
11,476 tok/s 6,886–18,362 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
11,476 tok/s 6,886–18,362 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	0.8 GB	Q8_0	Comfortable
11,476 tok/s 6,886–18,362 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
11,476 tok/s 6,886–18,362 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
11,476 tok/s 6,886–18,362 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
8,738 tok/s 5,243–13,981 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	0.8 GB	Q8_0	Comfortable
8,738 tok/s 5,243–13,981 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	0.8 GB	Q8_0	Comfortable
7,282 tok/s 4,369–11,651 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
7,127 tok/s 4,276–11,403 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
6,968 tok/s 4,181–11,148 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	0.8 GB	Q8_0	Comfortable
6,968 tok/s 4,181–11,148 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	0.8 GB	Q8_0	Comfortable
6,968 tok/s 4,181–11,148 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	0.8 GB	Q8_0	Comfortable
6,968 tok/s 4,181–11,148 · 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: 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)

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: 124M
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.

Record confidence: Confident
Citations: 3
Benchmark data: Engine-Base (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-Base (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-Base (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

The hardware side

Minimum card

Tesla C1080

Memory needed

0.8 GB

Fastest

27,324 tok/s

Engine-Base (NE) is small enough at 124M 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 297 tokens per second.

The quickest result comes from a B200 at around 27,324 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

Engine-Base (NE) was published by Boston University, in United States of America, in December 2021. It comes out of academia.

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

Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.

How fast it runs, and why

Across every card that can run it, the middle of the range is about 767.3 tokens per second, and 818 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.

Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.

Training and provenance

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

Step by step

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

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

Every card here has been checked against Engine-Base (NE) — around 0.8 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

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Engine-Base (NE) can slip off a card that handles short questions easily.
03

Choose how far you will compress it

Each card runs the least-compressed copy it can hold — Q8_0 on the smallest card that fits. Setting a floor drops the cards that only manage Engine-Base (NE) by squeezing it further than you would want.
04

Sort by speed

The speed ordering for Engine-Base (NE) is effectively an ordering by memory bandwidth, which is why the B200 tops it at 27,324 tok/s.
05

Read the fit column last

Tight means Engine-Base (NE) 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

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 Engine-Base (NE) alone — a card is usually bought for more than one model.

Answers

Engine-Base (NE) — common questions

Who created Engine-Base (NE)?

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

When was Engine-Base (NE) released?

Engine-Base (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-Base (NE) used for?

Engine-Base (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-Base (NE)?

The weights for Engine-Base (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-Base (NE) if it does not fit in my GPU?

It can be split between the card and system memory, but Engine-Base (NE) generates painfully slowly that way. Nothing on this page assumes offloading.

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

Two cards buy memory rather than speed. That matters for Engine-Base (NE) only if one card cannot hold it — 818 can, so a second adds little.

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

Each card is shown running the least-compressed copy it can hold, and Engine-Base (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-Base (NE) speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 16,395–43,719 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 Engine-Base (NE)?

The smallest card in our catalogue that holds Engine-Base (NE) is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 0.8 GB, and produces roughly 297 tokens per second. 818 cards in total can run it.

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

It depends on the card. The quickest we calculate is a B200 at about 27,324 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 Engine-Base (NE) clear that.

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

About 0.8 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-Base (NE) on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 0.8 GB and generating roughly 5,089 tokens per second — a comfortable fit.

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

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 0.8 GB and generating roughly 3,116 tokens per second — a comfortable fit.

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

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 0.8 GB and generating roughly 3,860 tokens per second — a comfortable fit.

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

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 0.8 GB and generating roughly 4,577 tokens per second — a comfortable fit.

Is Engine-Base (NE) open source?

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

Engine-Base (NE) has 124M parameters. "ENGINE-Base has 12 layers and 124 million parameters, on par with GPT2-124M and ROVER-Base". 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.

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