bge-reranker-large TPS calculator

Open weights Beijing Academy of Artificial Intelligence / BAAI,Hugging Face 560M parameters September 2023

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 · 65.8 tok/s

Fastest card

B200

6,050 tok/s · 180 GB

Which GPUs can run bge-reranker-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
6,050 tok/s 3,630–9,681 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.3 GB	Q8_0	Comfortable
6,050 tok/s 3,630–9,681 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.3 GB	Q8_0	Comfortable
4,831 tok/s 2,899–7,730 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.3 GB	Q8_0	Comfortable
4,831 tok/s 2,899–7,730 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.3 GB	Q8_0	Comfortable
3,864 tok/s 2,318–6,182 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.3 GB	Q8_0	Comfortable
3,698 tok/s 2,219–5,917 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.3 GB	Q8_0	Comfortable
3,698 tok/s 2,219–5,917 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.3 GB	Q8_0	Comfortable
3,540 tok/s 2,124–5,663 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.3 GB	Q8_0	Comfortable
3,141 tok/s 1,885–5,026 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.3 GB	Q8_0	Comfortable
3,141 tok/s 1,885–5,026 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.3 GB	Q8_0	Comfortable
3,141 tok/s 1,885–5,026 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.3 GB	Q8_0	Comfortable
2,980 tok/s 1,788–4,768 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.3 GB	Q8_0	Comfortable
2,541 tok/s 1,525–4,066 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.3 GB	Q8_0	Comfortable
2,541 tok/s 1,525–4,066 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.3 GB	Q8_0	Comfortable
2,541 tok/s 1,525–4,066 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.3 GB	Q8_0	Comfortable
2,541 tok/s 1,525–4,066 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.3 GB	Q8_0	Comfortable
2,541 tok/s 1,525–4,066 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.3 GB	Q8_0	Comfortable
1,935 tok/s 1,161–3,096 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.3 GB	Q8_0	Comfortable
1,935 tok/s 1,161–3,096 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.3 GB	Q8_0	Comfortable
1,612 tok/s 967–2,580 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.3 GB	Q8_0	Comfortable
1,578 tok/s 947–2,525 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.3 GB	Q8_0	Comfortable
1,543 tok/s 926–2,469 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.3 GB	Q8_0	Comfortable
1,543 tok/s 926–2,469 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.3 GB	Q8_0	Comfortable
1,543 tok/s 926–2,469 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.3 GB	Q8_0	Comfortable
1,543 tok/s 926–2,469 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.3 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: Beijing Academy of Artificial Intelligence / BAAI,Hugging Face
Organisation type: Academia,Industry
Country: China, United States of America
Published: 14 September 2023
Authors: Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff

What it does

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

Domain: Language
Task: Semantic embedding
Base model: XLM-RoBERTa

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: 560M
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)

How it is classified

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

Record confidence: Confident
Citations: 567

Sources

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

Reference: C-Pack: Packaged Resources To Advance General Chinese Embedding
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run bge-reranker-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 bge-reranker-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

1.3 GB

Fastest

6,050 tok/s

bge-reranker-large is small enough at 560M 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 65.8 tokens per second.

Top of the range is the B200, at roughly 6,050 tokens per second thanks to 8,000 GB/s of bandwidth.

Background

bge-reranker-large was published by Beijing Academy of Artificial Intelligence / BAAI,Hugging Face, in China, in September 2023. The organisation is categorised as academia,Industry.

It works in Language, and is recorded as doing semantic embedding.

Its starting point was XLM-RoBERTa — most models at this scale are adapted from an existing base rather than built from nothing.

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.

Reading the throughput figures

Across every card that can run it, the middle of the range is about 169.9 tokens per second, and 809 of them clear the ten tokens per second that roughly matches reading speed.

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.

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

Step by step

How to choose a GPU for bge-reranker-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

The table lists every card that can hold bge-reranker-large — around 1.3 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
02

Set the context length you will work at

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context bge-reranker-large can slip off a card that handles short questions easily.
03

Choose how far you will compress it

Compression is what makes bge-reranker-large 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

Compare tokens per second, not specifications

Ranking by tokens per second for bge-reranker-large follows memory bandwidth, not core counts, which is why the B200 tops it at 6,050 tok/s.
05

Read the fit column last

The fit column separates cards that just manage bge-reranker-large from those with room to spare. Buy for the second if the context might grow.
06

Open the card you have settled on

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for bge-reranker-large alone — a card is usually bought for more than one model.

Answers

bge-reranker-large — common questions

Can I run bge-reranker-large on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.3 GB and generating roughly 1,013 tokens per second — a comfortable fit.

Is bge-reranker-large open source?

Its weights are published, so bge-reranker-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 bge-reranker-large have?

bge-reranker-large has 560M parameters. 560M from https://huggingface.co/BAAI/bge-reranker-large "This reranker is initialized from xlm-roberta-base" from https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker. 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 bge-reranker-large?

bge-reranker-large was published by Beijing Academy of Artificial Intelligence / BAAI,Hugging Face, based in China, categorised as academia,Industry.

When was bge-reranker-large released?

bge-reranker-large was published in September 2023. 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 bge-reranker-large used for?

bge-reranker-large works in Language, and is recorded as handling semantic embedding. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download bge-reranker-large?

The weights for bge-reranker-large are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Can I run bge-reranker-large if it does not fit in my GPU?

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

Would two GPUs run bge-reranker-large faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run bge-reranker-large alone, the case for pairing is weak.

Why does the quantisation differ between cards for bge-reranker-large?

Each card is shown running the least-compressed copy it can hold, and bge-reranker-large appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these bge-reranker-large speed estimates?

These are estimates with real error bars. The fastest result here, 3,630–9,681 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run bge-reranker-large?

The smallest card in our catalogue that holds bge-reranker-large is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.3 GB, and produces roughly 65.8 tokens per second. 818 cards in total can run it.

How fast is bge-reranker-large on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 6,050 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 809 of the cards that can run bge-reranker-large clear that.

How much VRAM does bge-reranker-large need?

About 1.3 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 bge-reranker-large on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.3 GB and generating roughly 1,127 tokens per second — a comfortable fit.

Can I run bge-reranker-large on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.3 GB and generating roughly 690 tokens per second — a comfortable fit.

Can I run bge-reranker-large on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.3 GB and generating roughly 855 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 25 May 2026

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