YAYI-13B-Llama2 TPS calculator

Open weights Yayi (Wenge) 13B parameters July 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

295 cards that can run it

818 cards we hold specifications for

Smallest card that fits

GeForce GTX 1080 Ti

11 GB · Q3_K_M · 36.2 tok/s

Fastest card

B200

261 tok/s · 180 GB

Which GPUs can run YAYI-13B-Llama2?

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

295 cards match

Calculating

					Needs	Quantisation	Fit
261 tok/s 222–313	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	16.9 GB	Q8_0	Comfortable
261 tok/s 222–313	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	16.9 GB	Q8_0	Comfortable
208 tok/s 125–333 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	16.9 GB	Q8_0	Comfortable
208 tok/s 125–333 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	16.9 GB	Q8_0	Comfortable
166 tok/s 100–266 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	16.9 GB	Q8_0	Comfortable
159 tok/s 135–191	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	16.9 GB	Q8_0	Comfortable
159 tok/s 135–191	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	16.9 GB	Q8_0	Comfortable
152 tok/s 91–244 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	16.9 GB	Q8_0	Comfortable
135 tok/s 81–217 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	16.9 GB	Q8_0	Comfortable
135 tok/s 81–217 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	16.9 GB	Q8_0	Comfortable
135 tok/s 81–217 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	16.9 GB	Q8_0	Comfortable
128 tok/s 109–154	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
109 tok/s 93–131	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
109 tok/s 93–131	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	16.9 GB	Q8_0	Comfortable
109 tok/s 93–131	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
109 tok/s 93–131	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
109 tok/s 93–131	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
83.4 tok/s 50–133 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	16.9 GB	Q8_0	Comfortable
83.4 tok/s 50–133 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	16.9 GB	Q8_0	Comfortable
73.0 tok/s 62–88	GeForce RTX 3080 12 GB NVIDIA	12 GB	912 GB/s	Jan 2022	10.1 GB	IQ4_XS	Tight
73.0 tok/s 62–88	GeForce RTX 3080 Ti NVIDIA	12 GB	912 GB/s	May 2021	10.1 GB	IQ4_XS	Tight
69.5 tok/s 42–111 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	16.9 GB	Q8_0	Comfortable
68.0 tok/s 41–109 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	16.9 GB	Q8_0	Comfortable
66.5 tok/s 56–80	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	16.9 GB	Q8_0	Comfortable
66.5 tok/s 56–80	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	16.9 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 July 2023

What it does

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

Domain: Language
Task: Language modeling/generation
Base model: Llama 2-13B

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: 13B
Training data: tokens
Epochs: 2

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.

How it was established: Operation counting
Fine-tuning compute: 3.1 × 10¹⁸ FLOP

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)
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.

Record confidence: Likely

Sources

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

Reference: wenge-research
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run YAYI-13B-Llama2

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-13B-Llama2

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

What you need to run it

Minimum card

GeForce GTX 1080 Ti

Memory needed

9.4 GB

Fastest

261 tok/s

YAYI-13B-Llama2 is small enough at 13B parameters that hardware is rarely the obstacle — 295 of the cards we track can run it, including cards several years old.

The least hardware that works is a GeForce GTX 1080 Ti. Its 11 GB is enough at Q3_K_M compression, giving roughly 36.2 tokens per second.

The quickest result comes from a B200 at around 261 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

YAYI-13B-Llama2 was published by Yayi (Wenge), in China, in July 2023. It comes out of industry.

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

It is derived from Llama 2-13B rather than trained from scratch, which is the usual way a specialised model is produced.

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.

Reading the throughput figures

Across every card that can run it, the middle of the range is about 24.2 tokens per second, and 258 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.

Because the architecture is recorded, the memory column is derived rather than estimated.

Step by step

How to choose a GPU for YAYI-13B-Llama2

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-13B-Llama2 — around 9.4 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
02

Set the context length you will work at

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for YAYI-13B-Llama2.
03

Choose how far you will compress it

Each card runs the least-compressed copy it can hold — Q3_K_M on the smallest card that fits. Setting a floor drops the cards that only manage YAYI-13B-Llama2 by squeezing it further than you would want.
04

Sort by speed

The speed ordering for YAYI-13B-Llama2 is effectively an ordering by memory bandwidth, which is why the B200 tops it at 261 tok/s.
05

Read the fit column last

A tight fit runs YAYI-13B-Llama2 but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Open the card you have settled on

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond YAYI-13B-Llama2.

Answers

YAYI-13B-Llama2 — common questions

Who created YAYI-13B-Llama2?

YAYI-13B-Llama2 was published by Yayi (Wenge), based in China, categorised as industry.

When was YAYI-13B-Llama2 released?

YAYI-13B-Llama2 was published in July 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-13B-Llama2 used for?

YAYI-13B-Llama2 works in Language, and is recorded as handling language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download YAYI-13B-Llama2?

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.

Can I run YAYI-13B-Llama2 if it does not fit in my GPU?

It can be split between the card and system memory, but YAYI-13B-Llama2 generates painfully slowly that way — the nearest miss we calculate is short by 1.9 GB. Nothing on this page assumes offloading.

Would two GPUs run YAYI-13B-Llama2 faster?

Capacity adds across cards; throughput does not. Since 295 of the cards we track already hold YAYI-13B-Llama2 on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for YAYI-13B-Llama2?

Because capacity varies, so does how hard YAYI-13B-Llama2 has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these YAYI-13B-Llama2 speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 222–313 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 YAYI-13B-Llama2?

The smallest card in our catalogue that holds YAYI-13B-Llama2 is the GeForce GTX 1080 Ti, with 11 GB of memory. It runs the model at Q3_K_M using about 9.4 GB, and produces roughly 36.2 tokens per second. 295 cards in total can run it.

How fast is YAYI-13B-Llama2 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 261 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 258 of the cards that can run YAYI-13B-Llama2 clear that.

How much VRAM does YAYI-13B-Llama2 need?

About 9.4 GB at Q3_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 YAYI-13B-Llama2 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at IQ4_XS, using about 10.1 GB and generating roughly 73.0 tokens per second — a tight fit.

Can I run YAYI-13B-Llama2 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q6_K, using about 13.9 GB and generating roughly 53.5 tokens per second — a tight fit.

Can I run YAYI-13B-Llama2 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 16.9 GB and generating roughly 43.7 tokens per second — a comfortable fit.

Is YAYI-13B-Llama2 open source?

Its weights are published, so YAYI-13B-Llama2 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-13B-Llama2 have?

YAYI-13B-Llama2 has 13B 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 28 November 2025

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