Yi 6B TPS calculator

Open weights 01.AI 6B parameters November 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

589 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla K20c

5 GB · Q4_K_M · 28.8 tok/s

Fastest card

B200

565 tok/s · 180 GB

Which GPUs can run Yi 6B?

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

589 cards match

Calculating

					Needs	Quantisation	Fit
565 tok/s 339–904 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	7.1 GB	Q8_0	Comfortable
565 tok/s 339–904 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	7.1 GB	Q8_0	Comfortable
451 tok/s 271–721 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	7.1 GB	Q8_0	Comfortable
451 tok/s 271–721 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	7.1 GB	Q8_0	Comfortable
361 tok/s 216–577 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	7.1 GB	Q8_0	Comfortable
345 tok/s 207–552 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	7.1 GB	Q8_0	Comfortable
345 tok/s 207–552 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	7.1 GB	Q8_0	Comfortable
330 tok/s 198–529 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	7.1 GB	Q8_0	Comfortable
293 tok/s 176–469 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	7.1 GB	Q8_0	Comfortable
293 tok/s 176–469 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	7.1 GB	Q8_0	Comfortable
293 tok/s 176–469 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	7.1 GB	Q8_0	Comfortable
278 tok/s 167–445 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	7.1 GB	Q8_0	Comfortable
237 tok/s 142–379 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	7.1 GB	Q8_0	Comfortable
237 tok/s 142–379 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	7.1 GB	Q8_0	Comfortable
237 tok/s 142–379 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	7.1 GB	Q8_0	Comfortable
237 tok/s 142–379 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	7.1 GB	Q8_0	Comfortable
237 tok/s 142–379 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	7.1 GB	Q8_0	Comfortable
181 tok/s 108–289 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	7.1 GB	Q8_0	Comfortable
181 tok/s 108–289 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	7.1 GB	Q8_0	Comfortable
150 tok/s 90–241 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	7.1 GB	Q8_0	Comfortable
147 tok/s 88–236 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	7.1 GB	Q8_0	Comfortable
144 tok/s 86–230 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	7.1 GB	Q8_0	Comfortable
144 tok/s 86–230 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	7.1 GB	Q8_0	Comfortable
144 tok/s 86–230 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	7.1 GB	Q8_0	Comfortable
144 tok/s 86–230 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	7.1 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: 01.AI
Organisation type: Industry
Country: China
Published: 2 November 2023
Authors: Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang, Shiming Yang, Tao Yu, Wen Xie, Wenhao Huang, Xiaohui Hu, Xiaoyi Ren, Xinyao Niu, Pengcheng Nie, Yuchi Xu, Yudong Liu, Yue Wang, Yuxuan Cai, Zhenyu Gu, Zhiyuan Liu, Zonghong Dai

What it does

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

Domain: Language
Task: Chat, Language modeling/generation, Translation, Code generation

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: 6B
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.

Training compute: 1.3 × 10²³ FLOP
How it was established: Operation counting

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 (restricted use)
Training code: Unreleased

How it is classified

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

Likely above 10²³ FLOP: Yes
Record confidence: Confident

Sources

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

Reference: Yi: Open Foundation Models by 01.AI
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Yi 6B

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 Yi 6B

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

The hardware side

Minimum card

Tesla K20c

Memory needed

4.3 GB

Fastest

565 tok/s

Yi 6B is small enough at 6B parameters that hardware is rarely the obstacle — 589 of the cards we track can run it, including cards several years old.

At the low end, a Tesla K20c handles it — 5 GB, at Q4_K_M, for about 28.8 tokens per second.

At the other end, a B200 generates roughly 565 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

What this model is

Yi 6B was published by 01.AI, in China, in November 2023. The organisation is categorised as industry.

It works in Language, and is recorded as doing chat, Language modeling/generation, Translation, Code generation.

Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.

What decides the speed

Half the cards that hold it manage more than 25.4 tokens per second, and 539 exceed reading speed outright.

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.

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.

Training and provenance

The training run consumed about 1.3 × 10²³ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

Step by step

How to choose a GPU for Yi 6B

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

Look at what Yi 6B actually needs — around 4.3 GB at Q4_K_M. 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 Yi 6B stops fitting a card that seemed fine.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy of Yi 6B — Q4_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Sort by speed

The speed ordering for Yi 6B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 565 tok/s.
05

Read the fit column last

A tight fit runs Yi 6B but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Check the card from the other side

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Yi 6B.

Answers

Yi 6B — common questions

How fast is Yi 6B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 565 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 539 of the cards that can run Yi 6B clear that.

How much VRAM does Yi 6B need?

About 4.3 GB at Q4_K_M 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 Yi 6B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 7.1 GB and generating roughly 105 tokens per second — a tight fit.

Can I run Yi 6B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 7.1 GB and generating roughly 64.4 tokens per second — a comfortable fit.

Can I run Yi 6B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 7.1 GB and generating roughly 79.8 tokens per second — a comfortable fit.

Can I run Yi 6B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 7.1 GB and generating roughly 94.6 tokens per second — a comfortable fit.

Is Yi 6B open source?

Its weights are published, so Yi 6B 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 Yi 6B have?

Yi 6B has 6B parameters. 6B. 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 Yi 6B?

Yi 6B was published by 01.AI, based in China, categorised as industry.

When was Yi 6B released?

Yi 6B was published in November 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 Yi 6B used for?

Yi 6B works in Language, and is recorded as handling chat, Language modeling/generation, Translation, Code generation. 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 Yi 6B?

The weights for Yi 6B are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

How much compute was used to train Yi 6B?

Around 1.3 × 10²³ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.

Can I run Yi 6B 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 — the nearest miss we calculate is short by 0.7 GB. Our figures for Yi 6B assume it is fully resident.

Would two GPUs run Yi 6B faster?

A second card roughly doubles the memory available but not the generation rate. With 589 cards already able to run Yi 6B alone, the case for pairing is weak.

Why does the quantisation differ between cards for Yi 6B?

A larger card holds a more accurate copy. Across the cards that run Yi 6B, 3 compression levels are used; the floor control above pins it to one.

How accurate are these Yi 6B 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 339–904 tok/s on the B200 rather than a single number.

What GPU do I need to run Yi 6B?

The smallest card in our catalogue that holds Yi 6B is the Tesla K20c, with 5 GB of memory. It runs the model at Q4_K_M using about 4.3 GB, and produces roughly 28.8 tokens per second. 589 cards in total can run it.

Source

Original publication

Record last updated 28 November 2025

The other direction

Looking at it from the other side?

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