MinerU2.5 TPS calculator

Open weights Shanghai AI Lab,Peking University,Shanghai Jiao Tong University 1.2B parameters September 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 of 818 cards that can run it

Smallest card that fits

Tesla C1080

4 GB · Q8_0 · 30.7 tok/s

Fastest card

B200

2,824 tok/s · 180 GB

Which GPUs can run MinerU2.5?

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
2,824 tok/s 1,694–4,518 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	2.0 GB	Q8_0	Comfortable
2,824 tok/s 1,694–4,518 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	2.0 GB	Q8_0	Comfortable
2,255 tok/s 1,353–3,607 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	2.0 GB	Q8_0	Comfortable
2,255 tok/s 1,353–3,607 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	2.0 GB	Q8_0	Comfortable
1,803 tok/s 1,082–2,885 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	2.0 GB	Q8_0	Comfortable
1,726 tok/s 1,036–2,761 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.0 GB	Q8_0	Comfortable
1,726 tok/s 1,036–2,761 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.0 GB	Q8_0	Comfortable
1,652 tok/s 991–2,643 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	2.0 GB	Q8_0	Comfortable
1,466 tok/s 880–2,346 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	2.0 GB	Q8_0	Comfortable
1,466 tok/s 880–2,346 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	2.0 GB	Q8_0	Comfortable
1,466 tok/s 880–2,346 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	2.0 GB	Q8_0	Comfortable
1,391 tok/s 834–2,225 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	2.0 GB	Q8_0	Comfortable
1,186 tok/s 712–1,897 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.0 GB	Q8_0	Comfortable
1,186 tok/s 712–1,897 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	2.0 GB	Q8_0	Comfortable
1,186 tok/s 712–1,897 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	2.0 GB	Q8_0	Comfortable
1,186 tok/s 712–1,897 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.0 GB	Q8_0	Comfortable
1,186 tok/s 712–1,897 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	2.0 GB	Q8_0	Comfortable
903 tok/s 542–1,445 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	2.0 GB	Q8_0	Comfortable
903 tok/s 542–1,445 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	2.0 GB	Q8_0	Comfortable
752 tok/s 451–1,204 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	2.0 GB	Q8_0	Comfortable
736 tok/s 442–1,178 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	2.0 GB	Q8_0	Comfortable
720 tok/s 432–1,152 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	2.0 GB	Q8_0	Comfortable
720 tok/s 432–1,152 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	2.0 GB	Q8_0	Comfortable
720 tok/s 432–1,152 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	2.0 GB	Q8_0	Comfortable
720 tok/s 432–1,152 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	2.0 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: Shanghai AI Lab,Peking University,Shanghai Jiao Tong University
Organisation type: Academia,Academia,Academia
Country: China
Published: 29 September 2025
Authors: Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Yuanhong Zheng, Dongsheng Ma, Zirui Tang, Boyu Niu, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Jingzhou Chen, Fangdong Wang, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, Ruiliang Xu…

What it does

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

Domain: Language, Vision, Multimodal
Task: Visual question answering, Character recognition (OCR)
Base model: Qwen2-VL-2B,Qwen2-0.5B

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: 1.2B
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 (unrestricted)
Training code: Unreleased
Hugging Face: opendatalab

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: MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing
Last updated: 28 November 2025

The extremes

The ten fastest GPUs for MinerU2.5

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 MinerU2.5

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

2.0 GB

Fastest

2,824 tok/s

MinerU2.5 is small enough at 1.2B 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 30.7 tokens per second.

A B200 is the fastest we calculate for it: about 2,824 tokens per second, from 8,000 GB/s of memory bandwidth.

Where it came from

MinerU2.5 was published by Shanghai AI Lab,Peking University,Shanghai Jiao Tong University, in China, in September 2025. academia,Academia,Academia is the category the publisher falls under.

It works in Language, Vision, Multimodal, and is recorded as doing visual question answering, Character recognition (OCR).

It builds on Qwen2-VL-2B,Qwen2-0.5B, which is why it shares that model's general shape and size.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached. It is published under the opendatalab organisation on Hugging Face.

Understanding the speeds

Across every card that can run it, the middle of the range is about 79.3 tokens per second, and 799 of them clear the ten tokens per second that roughly matches reading speed.

Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.

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 MinerU2.5

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

Every card here has been checked against MinerU2.5 — around 2.0 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
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 MinerU2.5 can slip off a card that handles short questions easily.
03

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy of MinerU2.5 — 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 MinerU2.5. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 2,824 tok/s.
05

Check the fit verdict before buying

A tight fit runs MinerU2.5 but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

See what else that card runs

Following a card through to its own page shows every other model it can hold, which is the question that follows once MinerU2.5 is settled.

Answers

MinerU2.5 — common questions

How accurate are these MinerU2.5 speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 1,694–4,518 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 MinerU2.5?

The smallest card in our catalogue that holds MinerU2.5 is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 2.0 GB, and produces roughly 30.7 tokens per second. 818 cards in total can run it.

How fast is MinerU2.5 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 2,824 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 MinerU2.5 clear that.

How much VRAM does MinerU2.5 need?

About 2.0 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 MinerU2.5 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 2.0 GB and generating roughly 526 tokens per second — a comfortable fit.

Can I run MinerU2.5 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 2.0 GB and generating roughly 322 tokens per second — a comfortable fit.

Can I run MinerU2.5 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 2.0 GB and generating roughly 399 tokens per second — a comfortable fit.

Can I run MinerU2.5 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 2.0 GB and generating roughly 473 tokens per second — a comfortable fit.

Is MinerU2.5 open source?

Its weights are published, so MinerU2.5 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 MinerU2.5 have?

MinerU2.5 has 1.2B 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 MinerU2.5?

MinerU2.5 was published by Shanghai AI Lab,Peking University,Shanghai Jiao Tong University, based in China, categorised as academia,Academia,Academia.

When was MinerU2.5 released?

MinerU2.5 was published in September 2025.

What is MinerU2.5 used for?

MinerU2.5 works in Language, Vision, Multimodal, and is recorded as handling visual question answering, Character recognition (OCR). These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download MinerU2.5?

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

Can I run MinerU2.5 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 MinerU2.5 assume it is fully resident.

Would two GPUs run MinerU2.5 faster?

Two cards buy memory rather than speed. That matters for MinerU2.5 only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for MinerU2.5?

A larger card holds a more accurate copy. Across the cards that run MinerU2.5, 1 compression levels are used; the floor control above pins it to one.

Source

Original publication

Record last updated 28 November 2025

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