YaYi 2.0 TPS calculator

Open weights Yayi (Wenge) 30B parameters December 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

132 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX A4500

20 GB · IQ4_XS · 22.2 tok/s

Fastest card

B200

113 tok/s · 180 GB

Which GPUs can run YaYi 2.0?

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

132 cards match

Calculating

					Needs	Quantisation	Fit
113 tok/s 68–181 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	32.8 GB	Q8_0	Comfortable
113 tok/s 68–181 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	32.8 GB	Q8_0	Comfortable
90.2 tok/s 54–144 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	32.8 GB	Q8_0	Comfortable
90.2 tok/s 54–144 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	32.8 GB	Q8_0	Comfortable
72.1 tok/s 43–115 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	32.8 GB	Q8_0	Comfortable
69.0 tok/s 41–110 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	32.8 GB	Q8_0	Comfortable
69.0 tok/s 41–110 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	32.8 GB	Q8_0	Comfortable
66.1 tok/s 40–106 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	32.8 GB	Q8_0	Comfortable
58.6 tok/s 35–94 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	32.8 GB	Q8_0	Comfortable
58.6 tok/s 35–94 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	32.8 GB	Q8_0	Comfortable
58.6 tok/s 35–94 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	32.8 GB	Q8_0	Comfortable
55.6 tok/s 33–89 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
43.7 tok/s 26–70 · low confidence	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	18.8 GB	Q4_K_M	Tight
39.8 tok/s 24–64 · low confidence	A30X NVIDIA	24 GB	1,220 GB/s	Apr 2021	18.8 GB	Q4_K_M	Tight
38.4 tok/s 23–61 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	25.8 GB	Q6_K	Tight
38.4 tok/s 23–61 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	25.8 GB	Q6_K	Tight
36.7 tok/s 22–59 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	25.8 GB	Q6_K	Tight
36.7 tok/s 22–59 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	25.8 GB	Q6_K	Tight
36.1 tok/s 22–58 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	32.8 GB	Q8_0	Comfortable
36.1 tok/s 22–58 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	32.8 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: Yayi (Wenge)
Organisation type: Industry
Country: China
Published: 22 December 2023
Authors: Yin Luo, Qingchao Kong, Nan Xu, Jia Cao, Bao Hao, Baoyu Qu, Bo Chen, Chao Zhu Chenyang Zhao, Donglei Zhang, Fan Feng, Feifei Zhao, Hailong Sun, Hanxuan Yang, Haojun Pan, Hongyu Liu, Jianbin Guo, Jiangtao Du, Jingyi Wang, Junfeng Li, Lei Sun, Liduo Liu, Lifeng Dong, Lili Liu, Lin Wang, Liwen Zhang, Minzheng Wang, Pin Wang, Ping Yu, Qingxiao Li, Rui Yan, Rui Zou, Ruiqun Li, Taiwen Huang, Xiaodong Wa…

What it does

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

Domain: Language
Task: Language modeling/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: 30B
Training data: tokens
Epochs: 1

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: 4.8 × 10²³ 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 A800 PCIe 40 GB
Chips used: 1,000
Power draw: 495.5 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 (restricted use)
Training code: Open source
Hugging Face: wenge-research

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: Likely
Citations: 8

Sources

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

Reference: YAYI 2: Multilingual Open-Source Large Language Models
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run YaYi 2.0

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 YaYi 2.0

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

RTX A4500

Memory needed

17.1 GB

Fastest

113 tok/s

With 30B parameters, YaYi 2.0 lands in the range a serious desktop card can handle once the weights are compressed. 132 of the cards we track can run it.

The entry point is the RTX A4500: 20 GB of memory, IQ4_XS compression, roughly 22.2 tokens per second.

The quickest result comes from a B200 at around 113 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Where it came from

YaYi 2.0 was published by Yayi (Wenge), in China, in December 2023. The organisation is categorised as industry.

It works in Language, and is recorded as doing language modeling/generation.

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 wenge-research organisation on Hugging Face.

Understanding the speeds

Half the cards that hold it manage more than 22.0 tokens per second, and 104 exceed reading speed outright.

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.

What went into building it

Producing it required around 4.8 × 10²³ FLOP of arithmetic, on NVIDIA A800 PCIe 40 GB, which is a statement about the training budget rather than about inference.

Step by step

How to choose a GPU for YaYi 2.0

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

01

Start from the memory column

The table lists every card that can hold YaYi 2.0 — around 17.1 GB at IQ4_XS. That figure, not the card's headline performance, is what decides whether it runs.
02

Decide how long your conversations run

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason YaYi 2.0 stops fitting a card that seemed fine.
03

Set a quality floor

Compression is what makes YaYi 2.0 fit smaller cards, at some cost in accuracy — IQ4_XS on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Sort by speed

The speed ordering for YaYi 2.0 is effectively an ordering by memory bandwidth, which is why the B200 tops it at 113 tok/s.
05

Look at the headroom, not just the fit

Tight means YaYi 2.0 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

See what else that card runs

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond YaYi 2.0.

Answers

YaYi 2.0 — common questions

Who created YaYi 2.0?

YaYi 2.0 was published by Yayi (Wenge), based in China, categorised as industry.

When was YaYi 2.0 released?

YaYi 2.0 was published in December 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 YaYi 2.0 used for?

YaYi 2.0 works in Language, and is recorded as handling language modeling/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 YaYi 2.0?

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

How much compute was used to train YaYi 2.0?

Around 4.8 × 10²³ FLOP, on NVIDIA A800 PCIe 40 GB. 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 YaYi 2.0 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 4.4 GB. Our figures for YaYi 2.0 assume it is fully resident.

Would two GPUs run YaYi 2.0 faster?

Two cards buy memory rather than speed. That matters for YaYi 2.0 only if one card cannot hold it — 132 can, so a second adds little.

Why does the quantisation differ between cards for YaYi 2.0?

Because capacity varies, so does how hard YaYi 2.0 has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these YaYi 2.0 speed estimates?

These are estimates with real error bars. The fastest result here, 68–181 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 YaYi 2.0?

The smallest card in our catalogue that holds YaYi 2.0 is the RTX A4500, with 20 GB of memory. It runs the model at IQ4_XS using about 17.1 GB, and produces roughly 22.2 tokens per second. 132 cards in total can run it.

How fast is YaYi 2.0 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 113 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 104 of the cards that can run YaYi 2.0 clear that.

How much VRAM does YaYi 2.0 need?

About 17.1 GB at IQ4_XS 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 YaYi 2.0 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q4_K_M, using about 18.8 GB and generating roughly 43.7 tokens per second — a tight fit.

Is YaYi 2.0 open source?

Its weights are published, so YaYi 2.0 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 YaYi 2.0 have?

YaYi 2.0 has 30B 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.

Source

Original publication

Record last updated 25 May 2026

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.

All AI models