LTX-Video-0.9.1. 2B TPS calculator

Open weights Lightricks 1.9B parameters December 2024

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 · 19.4 tok/s

Fastest card

B200

1,783 tok/s · 180 GB

Which GPUs can run LTX-Video-0.9.1. 2B?

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
1,783 tok/s 1,070–2,853 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	2.7 GB	Q8_0	Comfortable
1,783 tok/s 1,070–2,853 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	2.7 GB	Q8_0	Comfortable
1,424 tok/s 854–2,278 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	2.7 GB	Q8_0	Comfortable
1,424 tok/s 854–2,278 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	2.7 GB	Q8_0	Comfortable
1,139 tok/s 683–1,822 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	2.7 GB	Q8_0	Comfortable
1,090 tok/s 654–1,744 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.7 GB	Q8_0	Comfortable
1,090 tok/s 654–1,744 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.7 GB	Q8_0	Comfortable
1,043 tok/s 626–1,669 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	2.7 GB	Q8_0	Comfortable
926 tok/s 556–1,481 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	2.7 GB	Q8_0	Comfortable
926 tok/s 556–1,481 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	2.7 GB	Q8_0	Comfortable
926 tok/s 556–1,481 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	2.7 GB	Q8_0	Comfortable
878 tok/s 527–1,405 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	2.7 GB	Q8_0	Comfortable
749 tok/s 449–1,198 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.7 GB	Q8_0	Comfortable
749 tok/s 449–1,198 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	2.7 GB	Q8_0	Comfortable
749 tok/s 449–1,198 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	2.7 GB	Q8_0	Comfortable
749 tok/s 449–1,198 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.7 GB	Q8_0	Comfortable
749 tok/s 449–1,198 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	2.7 GB	Q8_0	Comfortable
570 tok/s 342–912 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	2.7 GB	Q8_0	Comfortable
570 tok/s 342–912 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	2.7 GB	Q8_0	Comfortable
475 tok/s 285–760 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	2.7 GB	Q8_0	Comfortable
465 tok/s 279–744 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	2.7 GB	Q8_0	Comfortable
455 tok/s 273–728 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	2.7 GB	Q8_0	Comfortable
455 tok/s 273–728 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	2.7 GB	Q8_0	Comfortable
455 tok/s 273–728 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	2.7 GB	Q8_0	Comfortable
455 tok/s 273–728 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	2.7 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: Lightricks
Organisation type: Industry
Country: Israel
Published: 30 December 2024
Authors: Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, Poriya Panet, Sapir Weissbuch, Victor Kulikov, Yaki Bitterman, Zeev Melumian, Ofir Bibi

What it does

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

Domain: Video
Task: Video generation, Text-to-video, Image-to-video

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: 1.9B
Training data: tokens

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 (non-commercial)
Training code: Unreleased
Hugging Face: Lightricks

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Record confidence: Confident

Sources

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

Reference: LTX-Video: Realtime Video Latent Diffusion
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run LTX-Video-0.9.1. 2B

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 LTX-Video-0.9.1. 2B

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

2.7 GB

Fastest

1,783 tok/s

LTX-Video-0.9.1. 2B is small enough at 1.9B 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 19.4 tokens per second.

At the other end, a B200 generates roughly 1,783 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

Background

LTX-Video-0.9.1. 2B was published by Lightricks, in Israel, in December 2024. industry is the category the publisher falls under.

It works in Video, and is recorded as doing video generation, Text-to-video, Image-to-video.

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. It is published under the Lightricks organisation on Hugging Face.

Reading the throughput figures

Half the cards that hold it manage more than 50.1 tokens per second, and 789 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.

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.

Step by step

How to choose a GPU for LTX-Video-0.9.1. 2B

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

Look at what LTX-Video-0.9.1. 2B actually needs — around 2.7 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
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: at long context LTX-Video-0.9.1. 2B can slip off a card that handles short questions easily.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy of LTX-Video-0.9.1. 2B — 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 LTX-Video-0.9.1. 2B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 1,783 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs LTX-Video-0.9.1. 2B 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

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for LTX-Video-0.9.1. 2B alone — a card is usually bought for more than one model.

Answers

LTX-Video-0.9.1. 2B — common questions

Can I run LTX-Video-0.9.1. 2B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 2.7 GB and generating roughly 203 tokens per second — a comfortable fit.

Can I run LTX-Video-0.9.1. 2B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 2.7 GB and generating roughly 252 tokens per second — a comfortable fit.

Can I run LTX-Video-0.9.1. 2B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 2.7 GB and generating roughly 299 tokens per second — a comfortable fit.

Is LTX-Video-0.9.1. 2B open source?

Its weights are published, so LTX-Video-0.9.1. 2B 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 LTX-Video-0.9.1. 2B have?

LTX-Video-0.9.1. 2B has 1.9B parameters. 1.9B (Table 1). 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 LTX-Video-0.9.1. 2B?

LTX-Video-0.9.1. 2B was published by Lightricks, based in Israel, categorised as industry.

When was LTX-Video-0.9.1. 2B released?

LTX-Video-0.9.1. 2B was published in December 2024. 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 LTX-Video-0.9.1. 2B used for?

LTX-Video-0.9.1. 2B works in Video, and is recorded as handling video generation, Text-to-video, Image-to-video. 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.

Where can I download LTX-Video-0.9.1. 2B?

Its weights are published under the Lightricks organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.

Can I run LTX-Video-0.9.1. 2B if it does not fit in my GPU?

It can be split between the card and system memory, but LTX-Video-0.9.1. 2B generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run LTX-Video-0.9.1. 2B faster?

Two cards buy memory rather than speed. That matters for LTX-Video-0.9.1. 2B only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for LTX-Video-0.9.1. 2B?

Each card is shown running the least-compressed copy it can hold, and LTX-Video-0.9.1. 2B appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these LTX-Video-0.9.1. 2B speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 1,070–2,853 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 LTX-Video-0.9.1. 2B?

The smallest card in our catalogue that holds LTX-Video-0.9.1. 2B is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 2.7 GB, and produces roughly 19.4 tokens per second. 818 cards in total can run it.

How fast is LTX-Video-0.9.1. 2B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,783 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 789 of the cards that can run LTX-Video-0.9.1. 2B clear that.

How much VRAM does LTX-Video-0.9.1. 2B need?

About 2.7 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 LTX-Video-0.9.1. 2B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 2.7 GB and generating roughly 332 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 28 November 2025

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