Diffusion Renderer TPS calculator

Open weights NVIDIA,University of Toronto,Vector Institute,University of Illinois Urbana-Champaign (UIUC) 1.1B 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 · 33.5 tok/s

Fastest card

B200

3,080 tok/s · 180 GB

Which GPUs can run Diffusion Renderer?

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
3,080 tok/s 1,848–4,928 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.9 GB	Q8_0	Comfortable
3,080 tok/s 1,848–4,928 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.9 GB	Q8_0	Comfortable
2,460 tok/s 1,476–3,935 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.9 GB	Q8_0	Comfortable
2,460 tok/s 1,476–3,935 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.9 GB	Q8_0	Comfortable
1,967 tok/s 1,180–3,147 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.9 GB	Q8_0	Comfortable
1,883 tok/s 1,130–3,012 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.9 GB	Q8_0	Comfortable
1,883 tok/s 1,130–3,012 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.9 GB	Q8_0	Comfortable
1,802 tok/s 1,081–2,883 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.9 GB	Q8_0	Comfortable
1,599 tok/s 960–2,559 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.9 GB	Q8_0	Comfortable
1,599 tok/s 960–2,559 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.9 GB	Q8_0	Comfortable
1,599 tok/s 960–2,559 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.9 GB	Q8_0	Comfortable
1,517 tok/s 910–2,427 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
985 tok/s 591–1,576 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.9 GB	Q8_0	Comfortable
985 tok/s 591–1,576 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.9 GB	Q8_0	Comfortable
821 tok/s 493–1,313 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.9 GB	Q8_0	Comfortable
803 tok/s 482–1,285 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.9 GB	Q8_0	Comfortable
785 tok/s 471–1,257 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.9 GB	Q8_0	Comfortable
785 tok/s 471–1,257 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.9 GB	Q8_0	Comfortable
785 tok/s 471–1,257 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.9 GB	Q8_0	Comfortable
785 tok/s 471–1,257 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.9 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: NVIDIA,University of Toronto,Vector Institute,University of Illinois Urbana-Champaign (UIUC)
Organisation type: Industry,Academia,Academia,Academia
Country: United States of America, Canada
Published: 22 March 2025
Authors: Ruofan Liang, Zan Gojcic, Huan Ling, Jacob Munkberg, Jon Hasselgren, Zhi-Hao Lin, Jun Gao, Alexander Keller, Nandita Vijaykumar, Sanja Fidler, Zian Wang

What it does

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

Domain: Video
Task: Video editing, Video-to-video, 3D segmentation
Base model: Stable Video Diffusion
Numerical format: FP16

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: 1.1B
Training data: tokens

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.

How it was established: Hardware
Fine-tuning compute: 1.3 × 10²⁰ FLOP

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Training hardware: NVIDIA A100
Chips used: 32
Wall-clock time: 48 hours
Power draw: 25.1 kW

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: nexuslrf

How it is classified

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

Why it is tracked: SOTA improvement
Record confidence: Confident

Sources

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

Reference: DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Diffusion Renderer

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 Diffusion Renderer

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

Hardware requirements in practice

Minimum card

Tesla C1080

Memory needed

1.9 GB

Fastest

3,080 tok/s

Diffusion Renderer is small enough at 1.1B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The entry point is the Tesla C1080: 4 GB of memory, Q8_0 compression, roughly 33.5 tokens per second.

Top of the range is the B200, at roughly 3,080 tokens per second thanks to 8,000 GB/s of bandwidth.

What this model is

Diffusion Renderer was published by NVIDIA,University of Toronto,Vector Institute,University of Illinois Urbana-Champaign (UIUC), in United States of America, in March 2025. The organisation is categorised as industry,Academia,Academia,Academia.

It works in Video, and is recorded as doing video editing, Video-to-video, 3D segmentation.

It is derived from Stable Video Diffusion 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. It is published under the nexuslrf organisation on Hugging Face.

What decides the speed

Half the cards that hold it manage more than 86.5 tokens per second, and 799 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.

How it was trained

The reason it appears in this catalogue at all is sOTA improvement.

Step by step

How to choose a GPU for Diffusion Renderer

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

The table lists every card that can hold Diffusion Renderer — around 1.9 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 Diffusion Renderer stops fitting a card that seemed fine.
03

Choose how far you will compress it

Compression is what makes Diffusion Renderer fit smaller cards, at some cost in accuracy — Q8_0 on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Sort by speed

Sort by speed to see how cards rank for Diffusion Renderer. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 3,080 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs Diffusion Renderer but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
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 Diffusion Renderer is settled.

Answers

Diffusion Renderer — common questions

What is Diffusion Renderer used for?

Diffusion Renderer works in Video, and is recorded as handling video editing, Video-to-video, 3D segmentation. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Diffusion Renderer?

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

Can I run Diffusion Renderer if it does not fit in my GPU?

It can be split between the card and system memory, but Diffusion Renderer generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run Diffusion Renderer faster?

Two cards buy memory rather than speed. That matters for Diffusion Renderer only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for Diffusion Renderer?

A larger card holds a more accurate copy. Across the cards that run Diffusion Renderer, 1 compression levels are used; the floor control above pins it to one.

How accurate are these Diffusion Renderer speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 1,848–4,928 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 Diffusion Renderer?

The smallest card in our catalogue that holds Diffusion Renderer is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.9 GB, and produces roughly 33.5 tokens per second. 818 cards in total can run it.

How fast is Diffusion Renderer on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 3,080 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 799 of the cards that can run Diffusion Renderer clear that.

How much VRAM does Diffusion Renderer need?

About 1.9 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 Diffusion Renderer on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.9 GB and generating roughly 574 tokens per second — a comfortable fit.

Can I run Diffusion Renderer on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.9 GB and generating roughly 351 tokens per second — a comfortable fit.

Can I run Diffusion Renderer on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.9 GB and generating roughly 435 tokens per second — a comfortable fit.

Can I run Diffusion Renderer on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.9 GB and generating roughly 516 tokens per second — a comfortable fit.

Is Diffusion Renderer open source?

Its weights are published, so Diffusion Renderer 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 Diffusion Renderer have?

Diffusion Renderer has 1.1B parameters. "This model has 1.1B model 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 Diffusion Renderer?

Diffusion Renderer was published by NVIDIA,University of Toronto,Vector Institute,University of Illinois Urbana-Champaign (UIUC), based in United States of America, categorised as industry,Academia,Academia,Academia.

When was Diffusion Renderer released?

Diffusion Renderer was published in March 2025.

Source

Original publication

Record last updated 28 November 2025

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