LTX-Video-0.9.5. 2B TPS calculator

Open weights Lightricks 1.9B parameters March 2025

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-0.9.5. 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.

Search cards

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: 5 March 2025

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 (restricted use)
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: Release a new checkpoint v0.9.5 with improved quality
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run LTX-Video-0.9.5. 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-0.9.5. 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

The hardware side

Minimum card

Tesla C1080

Memory needed

2.7 GB

Fastest

1,783 tok/s

LTX-Video-0.9.5. 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.

At the low end, a Tesla C1080 handles it — 4 GB, at Q8_0, for about 19.4 tokens per second.

The quickest result comes from a B200 at around 1,783 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

LTX-Video-0.9.5. 2B was published by Lightricks, in Israel, in March 2025. The organisation is categorised as industry.

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.

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.

Step by step

How to choose a GPU for LTX-Video-0.9.5. 2B

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

Every card here has been checked against LTX-Video-0.9.5. 2B — around 2.7 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
02

Decide how long your conversations run

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason LTX-Video-0.9.5. 2B stops fitting a card that seemed fine.
03

Decide how much compression you will accept

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 LTX-Video-0.9.5. 2B by squeezing it further than you would want.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for LTX-Video-0.9.5. 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

The fit column separates cards that just manage LTX-Video-0.9.5. 2B from those with room to spare. Buy for the second if the context might grow.
06

Open the card you have settled on

Following a card through to its own page shows every other model it can hold, which is the question that follows once LTX-Video-0.9.5. 2B is settled.

Answers

LTX-Video-0.9.5. 2B — common questions

What is LTX-Video-0.9.5. 2B used for?

LTX-Video-0.9.5. 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-0.9.5. 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-0.9.5. 2B 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 LTX-Video-0.9.5. 2B assume it is fully resident.

Would two GPUs run LTX-Video-0.9.5. 2B faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run LTX-Video-0.9.5. 2B alone, the case for pairing is weak.

Why does the quantisation differ between cards for LTX-Video-0.9.5. 2B?

Each card is shown running the least-compressed copy it can hold, and LTX-Video-0.9.5. 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-0.9.5. 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-0.9.5. 2B?

The smallest card in our catalogue that holds LTX-Video-0.9.5. 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-0.9.5. 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-0.9.5. 2B clear that.

How much VRAM does LTX-Video-0.9.5. 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-0.9.5. 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.

Can I run LTX-Video-0.9.5. 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-0.9.5. 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-0.9.5. 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-0.9.5. 2B open source?

Its weights are published, so LTX-Video-0.9.5. 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-0.9.5. 2B have?

LTX-Video-0.9.5. 2B has 1.9B parameters. 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-0.9.5. 2B?

LTX-Video-0.9.5. 2B was published by Lightricks, based in Israel, categorised as industry.

When was LTX-Video-0.9.5. 2B released?

LTX-Video-0.9.5. 2B was published in March 2025.

Source

Original publication

Record last updated 28 November 2025

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