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 reaches a parameter count of 1.1B. That is small enough that hardware is rarely the obstacle, including on cards several years old. The number of cards we track that can run it: 818.

The entry point is Tesla C1080, with a memory capacity of 4 GB, running it at a compression of Q8_0 and producing around 33.5 tokens per second.

Top of the range is B200, generating roughly 3,080 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

What this model is

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

It works in the domain of Video, and is recorded as performing the task of video editing, Video-to-video, 3D segmentation.

Rather than being trained from scratch, it is derived from Stable Video Diffusion. That is why it shares the base model's general shape and size.

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. On Hugging Face it is published under the organisation nexuslrf.

What decides the speed

Half the cards that hold it manage more than 86.5 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 799 of them.

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: 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 able to hold Diffusion Renderer, needing around 1.9 GB at a compression of Q8_0. No amount of processing power compensates for a card that cannot hold it.
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 a card that seemed fine stops fitting Diffusion Renderer.
03

Choose how far you will compress it

Compression is what makes a model fit smaller cards, at some cost in accuracy, reaching a compression of Q8_0 on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
04

Sort by speed

Sort by speed to see how cards rank for Diffusion Renderer. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 3,080 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of Diffusion Renderer. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
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 you have settled on Diffusion Renderer.

Answers

Diffusion Renderer — common questions

Diffusion Renderer— what is it used for?

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

Diffusion Renderer— where can I download it?

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

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

It can be split between the card and system memory, but it generates painfully slowly that way. Every figure here assumes the whole model is resident on the card.

Diffusion Renderer— would two GPUs run it faster?

Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 818. So a second card is rarely the answer here.

Diffusion Renderer— why does the quantisation differ between cards?

A larger card holds a more accurate copy. The number of compression levels used across the cards that run it: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Diffusion Renderer— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 1,848–4,928 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Diffusion Renderer— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Tesla C1080, with a memory capacity of 4 GB. It runs the model at a compression of Q8_0 using about 1.9 GB, and produces roughly 33.5 tokens per second. The number of cards able to run it in total: 818.

Diffusion Renderer— how fast is it on a GPU?

It depends on the card. The quickest we calculate is 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 the number of cards clearing that: 799.

Diffusion Renderer— how much VRAM does it need?

It needs about 1.9 GB at a compression of Q8_0, 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.

Diffusion Renderer— can I run it on a GPU holding 8 GB?

Yes. The card CMP 170HX 8 GB, holding 8 GB, runs it at a compression of Q8_0, using about 1.9 GB and generating roughly 574 tokens per second. The fit is comfortable.

Diffusion Renderer— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of Q8_0, using about 1.9 GB and generating roughly 351 tokens per second. The fit is comfortable.

Diffusion Renderer— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q8_0, using about 1.9 GB and generating roughly 435 tokens per second. The fit is comfortable.

Diffusion Renderer— can I run it on a GPU holding 24 GB?

Yes. The card GeForce RTX 5090 D V2, holding 24 GB, runs it at a compression of Q8_0, using about 1.9 GB and generating roughly 516 tokens per second. The fit is comfortable.

Diffusion Renderer— is it open source?

Its weights are published, so it 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.

Diffusion Renderer— how many parameters does it have?

It has a parameter count of 1.1B. "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.

Diffusion Renderer— who created it?

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

Diffusion Renderer— when was it released?

It was published in March 2025.

Source

Original publication

Record last updated 28 November 2025

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