YAYI-7B-Llama2 TPS calculator

Open weights Yayi (Wenge) 7B 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

509 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 5110P

8 GB · Q4_K_M · 29.1 tok/s

Fastest card

B200

484 tok/s · 180 GB

Which GPUs can run YAYI-7B-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

509 cards match

Calculating

					Needs	Quantisation	Fit
484 tok/s 411–581	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	9.8 GB	Q8_0	Comfortable
484 tok/s 411–581	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	9.8 GB	Q8_0	Comfortable
387 tok/s 232–618 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	9.8 GB	Q8_0	Comfortable
387 tok/s 232–618 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	9.8 GB	Q8_0	Comfortable
309 tok/s 185–495 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	9.8 GB	Q8_0	Comfortable
296 tok/s 251–355	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	9.8 GB	Q8_0	Comfortable
296 tok/s 251–355	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	9.8 GB	Q8_0	Comfortable
283 tok/s 170–453 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	9.8 GB	Q8_0	Comfortable
251 tok/s 151–402 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	9.8 GB	Q8_0	Comfortable
251 tok/s 151–402 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	9.8 GB	Q8_0	Comfortable
251 tok/s 151–402 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	9.8 GB	Q8_0	Comfortable
238 tok/s 203–286	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
208 tok/s 177–250	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.5 GB	Q4_K_M	Tight
203 tok/s 173–244	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	9.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
155 tok/s 93–248 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	9.8 GB	Q8_0	Comfortable
155 tok/s 93–248 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	9.8 GB	Q8_0	Comfortable
137 tok/s 117–165	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.1 GB	Q6_K	Tight
129 tok/s 77–206 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	9.8 GB	Q8_0	Comfortable
126 tok/s 76–202 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	9.8 GB	Q8_0	Comfortable
123 tok/s 105–148	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	9.8 GB	Q8_0	Comfortable
123 tok/s 105–148	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	9.8 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 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 2-7B

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: 7B
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: 1.7 × 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-7B-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-7B-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

Xeon Phi 5110P

Memory needed

6.5 GB

Fastest

484 tok/s

YAYI-7B-Llama2 is small enough at 7B parameters that hardware is rarely the obstacle — 509 of the cards we track can run it, including cards several years old.

The least hardware that works is a Xeon Phi 5110P. Its 8 GB is enough at Q4_K_M compression, giving roughly 29.1 tokens per second.

A B200 is the fastest we calculate for it: about 484 tokens per second, from 8,000 GB/s of memory bandwidth.

Where it came from

YAYI-7B-Llama2 was published by Yayi (Wenge), in China, in July 2023. industry is the category the publisher falls under.

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

It builds on Llama 2-7B, 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 wenge-research organisation on Hugging Face.

Understanding the speeds

The median result is around 30.4 tokens per second; 482 cards produce text faster than most people read it.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

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

Step by step

How to choose a GPU for YAYI-7B-Llama2

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

Every card here has been checked against YAYI-7B-Llama2 — around 6.5 GB at Q4_K_M. Capacity is the gate — a card either holds it or it does not.
02

Set the context length you will work at

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason YAYI-7B-Llama2 stops fitting a card that seemed fine.
03

Choose how far you will compress it

Compression is what makes YAYI-7B-Llama2 fit smaller cards, at some cost in accuracy — Q4_K_M on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Compare tokens per second, not specifications

The speed ordering for YAYI-7B-Llama2 is effectively an ordering by memory bandwidth, which is why the B200 tops it at 484 tok/s.
05

Look at the headroom, not just the fit

Tight means YAYI-7B-Llama2 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-7B-Llama2.

Answers

YAYI-7B-Llama2 — common questions

What GPU do I need to run YAYI-7B-Llama2?

The smallest card in our catalogue that holds YAYI-7B-Llama2 is the Xeon Phi 5110P, with 8 GB of memory. It runs the model at Q4_K_M using about 6.5 GB, and produces roughly 29.1 tokens per second. 509 cards in total can run it.

How fast is YAYI-7B-Llama2 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 484 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 482 of the cards that can run YAYI-7B-Llama2 clear that.

How much VRAM does YAYI-7B-Llama2 need?

About 6.5 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 YAYI-7B-Llama2 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q4_K_M, using about 6.5 GB and generating roughly 208 tokens per second — a tight fit.

Can I run YAYI-7B-Llama2 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 9.8 GB and generating roughly 55.2 tokens per second — a tight fit.

Can I run YAYI-7B-Llama2 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 9.8 GB and generating roughly 68.4 tokens per second — a comfortable fit.

Can I run YAYI-7B-Llama2 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 9.8 GB and generating roughly 81.1 tokens per second — a comfortable fit.

Is YAYI-7B-Llama2 open source?

Its weights are published, so YAYI-7B-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-7B-Llama2 have?

YAYI-7B-Llama2 has 7B 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 YAYI-7B-Llama2?

YAYI-7B-Llama2 was published by Yayi (Wenge), based in China, categorised as industry.

When was YAYI-7B-Llama2 released?

YAYI-7B-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-7B-Llama2 used for?

YAYI-7B-Llama2 works in Language, and is recorded as handling language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download YAYI-7B-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-7B-Llama2 if it does not fit in my GPU?

It can be split between the card and system memory, but YAYI-7B-Llama2 generates painfully slowly that way — the nearest miss we calculate is short by 1.1 GB. Nothing on this page assumes offloading.

Would two GPUs run YAYI-7B-Llama2 faster?

Two cards buy memory rather than speed. That matters for YAYI-7B-Llama2 only if one card cannot hold it — 509 can, so a second adds little.

Why does the quantisation differ between cards for YAYI-7B-Llama2?

A larger card holds a more accurate copy. Across the cards that run YAYI-7B-Llama2, 3 compression levels are used; the floor control above pins it to one.

How accurate are these YAYI-7B-Llama2 speed estimates?

These are estimates with real error bars. The fastest result here, 411–581 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

Source

Original publication

Record last updated 28 November 2025

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