Depth Anything V2 Large TPS calculator

Open weights Tik Tok,Hong Kong University 335.3M parameters October 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 · 110 tok/s

Fastest card

B200

10,105 tok/s · 180 GB

Which GPUs can run Depth Anything V2 Large?

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
10,105 tok/s 6,063–16,168 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.1 GB	Q8_0	Comfortable
10,105 tok/s 6,063–16,168 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.1 GB	Q8_0	Comfortable
8,069 tok/s 4,842–12,911 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.1 GB	Q8_0	Comfortable
8,069 tok/s 4,842–12,911 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.1 GB	Q8_0	Comfortable
6,453 tok/s 3,872–10,325 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
6,177 tok/s 3,706–9,883 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.1 GB	Q8_0	Comfortable
6,177 tok/s 3,706–9,883 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.1 GB	Q8_0	Comfortable
5,911 tok/s 3,547–9,458 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.1 GB	Q8_0	Comfortable
5,246 tok/s 3,148–8,394 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
5,246 tok/s 3,148–8,394 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
5,246 tok/s 3,148–8,394 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
4,977 tok/s 2,986–7,963 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,244 tok/s 2,546–6,791 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,244 tok/s 2,546–6,791 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.1 GB	Q8_0	Comfortable
4,244 tok/s 2,546–6,791 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,244 tok/s 2,546–6,791 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,244 tok/s 2,546–6,791 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
3,232 tok/s 1,939–5,171 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.1 GB	Q8_0	Comfortable
3,232 tok/s 1,939–5,171 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.1 GB	Q8_0	Comfortable
2,693 tok/s 1,616–4,309 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
2,636 tok/s 1,581–4,217 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
2,577 tok/s 1,546–4,123 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.1 GB	Q8_0	Comfortable
2,577 tok/s 1,546–4,123 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.1 GB	Q8_0	Comfortable
2,577 tok/s 1,546–4,123 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.1 GB	Q8_0	Comfortable
2,577 tok/s 1,546–4,123 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.1 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: Tik Tok,Hong Kong University
Organisation type: Industry,Academia
Country: China, Hong Kong
Published: 20 October 2024
Authors: Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao

What it does

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

Domain: 3D modeling
Task: 3D reconstruction

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: 335.3M
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: depth-anything

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: Depth Anything V2
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Depth Anything V2 Large

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 Depth Anything V2 Large

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

1.1 GB

Fastest

10,105 tok/s

Depth Anything V2 Large is small enough at 335.3M 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 110 tokens per second.

Top of the range is the B200, at roughly 10,105 tokens per second thanks to 8,000 GB/s of bandwidth.

About this model

Depth Anything V2 Large was published by Tik Tok,Hong Kong University, in China, in October 2024. industry,Academia is the category the publisher falls under.

It works in 3D modeling, and is recorded as doing 3D reconstruction.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the depth-anything organisation on Hugging Face.

How fast it runs, and why

The median result is around 283.8 tokens per second; 818 cards produce text faster than most people read it.

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 Depth Anything V2 Large

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 Depth Anything V2 Large actually needs — around 1.1 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
02

Match the context to your actual use

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Depth Anything V2 Large.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy of Depth Anything V2 Large — Q8_0 on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for Depth Anything V2 Large. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 10,105 tok/s.
05

Check the fit verdict before buying

Tight means Depth Anything V2 Large 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

Check the card from the other side

Following a card through to its own page shows every other model it can hold, which is the question that follows once Depth Anything V2 Large is settled.

Answers

Depth Anything V2 Large — common questions

What GPU do I need to run Depth Anything V2 Large?

The smallest card in our catalogue that holds Depth Anything V2 Large is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.1 GB, and produces roughly 110 tokens per second. 818 cards in total can run it.

How fast is Depth Anything V2 Large on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 10,105 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 Depth Anything V2 Large clear that.

How much VRAM does Depth Anything V2 Large need?

About 1.1 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 Depth Anything V2 Large on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,882 tokens per second — a comfortable fit.

Can I run Depth Anything V2 Large on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,152 tokens per second — a comfortable fit.

Can I run Depth Anything V2 Large on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,427 tokens per second — a comfortable fit.

Can I run Depth Anything V2 Large on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,693 tokens per second — a comfortable fit.

Is Depth Anything V2 Large open source?

Its weights are published, so Depth Anything V2 Large 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 Depth Anything V2 Large have?

Depth Anything V2 Large has 335.3M parameters. 335.3M. 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 Depth Anything V2 Large?

Depth Anything V2 Large was published by Tik Tok,Hong Kong University, based in China, categorised as industry,Academia.

When was Depth Anything V2 Large released?

Depth Anything V2 Large was published in October 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 Depth Anything V2 Large used for?

Depth Anything V2 Large works in 3D modeling, and is recorded as handling 3D reconstruction. 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 Depth Anything V2 Large?

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

Can I run Depth Anything V2 Large if it does not fit in my GPU?

Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded Depth Anything V2 Large is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run Depth Anything V2 Large faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run Depth Anything V2 Large alone, the case for pairing is weak.

Why does the quantisation differ between cards for Depth Anything V2 Large?

A larger card holds a more accurate copy. Across the cards that run Depth Anything V2 Large, 1 compression levels are used; the floor control above pins it to one.

How accurate are these Depth Anything V2 Large speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as 6,063–16,168 tok/s on the B200 rather than a single number.

Source

Original publication

Record last updated 28 November 2025

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