XVERSE-MoE-A4.2B TPS calculator

Open weights XVERSE Technology,Shenzhen Yuanxiang Technology 4.2B parameters April 2024

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 of 818 cards that can run it

Smallest card that fits

Tesla C1080

4 GB · Q4_K_M · 20.3 tok/s

Fastest card

B200

807 tok/s · 180 GB

Which GPUs can run XVERSE-MoE-A4.2B?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
807 tok/s 484–1,291 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	5.2 GB	Q8_0	Comfortable
807 tok/s 484–1,291 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	5.2 GB	Q8_0	Comfortable
644 tok/s 387–1,031 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	5.2 GB	Q8_0	Comfortable
644 tok/s 387–1,031 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	5.2 GB	Q8_0	Comfortable
515 tok/s 309–824 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	5.2 GB	Q8_0	Comfortable
493 tok/s 296–789 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	5.2 GB	Q8_0	Comfortable
493 tok/s 296–789 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	5.2 GB	Q8_0	Comfortable
472 tok/s 283–755 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	5.2 GB	Q8_0	Comfortable
419 tok/s 251–670 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	5.2 GB	Q8_0	Comfortable
419 tok/s 251–670 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	5.2 GB	Q8_0	Comfortable
419 tok/s 251–670 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	5.2 GB	Q8_0	Comfortable
397 tok/s 238–636 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	5.2 GB	Q8_0	Comfortable
339 tok/s 203–542 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	5.2 GB	Q8_0	Comfortable
339 tok/s 203–542 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	5.2 GB	Q8_0	Comfortable
339 tok/s 203–542 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	5.2 GB	Q8_0	Comfortable
339 tok/s 203–542 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	5.2 GB	Q8_0	Comfortable
339 tok/s 203–542 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	5.2 GB	Q8_0	Comfortable
258 tok/s 155–413 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	5.2 GB	Q8_0	Comfortable
258 tok/s 155–413 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	5.2 GB	Q8_0	Comfortable
215 tok/s 129–344 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	5.2 GB	Q8_0	Comfortable
210 tok/s 126–337 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	5.2 GB	Q8_0	Comfortable
206 tok/s 123–329 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	5.2 GB	Q8_0	Comfortable
206 tok/s 123–329 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	5.2 GB	Q8_0	Comfortable
206 tok/s 123–329 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	5.2 GB	Q8_0	Comfortable
206 tok/s 123–329 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	5.2 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: XVERSE Technology,Shenzhen Yuanxiang Technology
Organisation type: Industry,Industry
Country: China
Published: 2 April 2024

What it does

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

Domain: Language
Task: Chat

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: 4.2B
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 (non-commercial)
Hugging Face: xverse

How it is classified

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

Record confidence: Confident

Sources

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

Last updated: 28 November 2025

The extremes

The ten fastest GPUs for XVERSE-MoE-A4.2B

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 XVERSE-MoE-A4.2B

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 it takes to run this model

Minimum card

Tesla C1080

Memory needed

3.2 GB

Fastest

807 tok/s

XVERSE-MoE-A4.2B is small enough at 4.2B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The smallest card that holds it is the Tesla C1080 with 4 GB, running it at Q4_K_M and producing around 20.3 tokens per second.

A B200 is the fastest we calculate for it: about 807 tokens per second, from 8,000 GB/s of memory bandwidth.

What this model is

XVERSE-MoE-A4.2B was published by XVERSE Technology,Shenzhen Yuanxiang Technology, in China, in April 2024. industry,Industry is the category the publisher falls under.

It works in Language, and is recorded as doing chat.

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. It is published under the xverse organisation on Hugging Face.

What decides the speed

Half the cards that hold it manage more than 29.8 tokens per second, and 778 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.

Its internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.

Step by step

How to choose a GPU for XVERSE-MoE-A4.2B

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 XVERSE-MoE-A4.2B actually needs — around 3.2 GB at Q4_K_M. No amount of processing power compensates for a card that cannot hold it.
02

Decide how long your conversations run

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for XVERSE-MoE-A4.2B.
03

Set a quality floor

Compression is what makes XVERSE-MoE-A4.2B 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

Sort by speed

The speed ordering for XVERSE-MoE-A4.2B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 807 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage XVERSE-MoE-A4.2B from those with room to spare. Buy for the second if the context might grow.
06

See what else that card runs

Following a card through to its own page shows every other model it can hold, which is the question that follows once XVERSE-MoE-A4.2B is settled.

Answers

XVERSE-MoE-A4.2B — common questions

When was XVERSE-MoE-A4.2B released?

XVERSE-MoE-A4.2B was published in April 2024. 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 XVERSE-MoE-A4.2B used for?

XVERSE-MoE-A4.2B works in Language, and is recorded as handling chat. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download XVERSE-MoE-A4.2B?

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

Can I run XVERSE-MoE-A4.2B if it does not fit in my GPU?

It can be split between the card and system memory, but XVERSE-MoE-A4.2B generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run XVERSE-MoE-A4.2B faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold XVERSE-MoE-A4.2B on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for XVERSE-MoE-A4.2B?

A larger card holds a more accurate copy. Across the cards that run XVERSE-MoE-A4.2B, 3 compression levels are used; the floor control above pins it to one.

How accurate are these XVERSE-MoE-A4.2B speed estimates?

These are estimates with real error bars. The fastest result here, 484–1,291 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 XVERSE-MoE-A4.2B?

The smallest card in our catalogue that holds XVERSE-MoE-A4.2B is the Tesla C1080, with 4 GB of memory. It runs the model at Q4_K_M using about 3.2 GB, and produces roughly 20.3 tokens per second. 818 cards in total can run it.

How fast is XVERSE-MoE-A4.2B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 807 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 778 of the cards that can run XVERSE-MoE-A4.2B clear that.

How much VRAM does XVERSE-MoE-A4.2B need?

About 3.2 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 XVERSE-MoE-A4.2B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 5.2 GB and generating roughly 150 tokens per second — a comfortable fit.

Can I run XVERSE-MoE-A4.2B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 5.2 GB and generating roughly 92.0 tokens per second — a comfortable fit.

Can I run XVERSE-MoE-A4.2B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 5.2 GB and generating roughly 114 tokens per second — a comfortable fit.

Can I run XVERSE-MoE-A4.2B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 5.2 GB and generating roughly 135 tokens per second — a comfortable fit.

Is XVERSE-MoE-A4.2B open source?

Its weights are published, so XVERSE-MoE-A4.2B 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 XVERSE-MoE-A4.2B have?

XVERSE-MoE-A4.2B has 4.2B parameters. It uses a mixed expert model (MoE, Mixture-of-experts) architecture. The total parameter scale of the model is 25.8 billion, and the actual number of activated parameters is 4.2 billion. 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 XVERSE-MoE-A4.2B?

XVERSE-MoE-A4.2B was published by XVERSE Technology,Shenzhen Yuanxiang Technology, based in China, categorised as industry,Industry.

Source

Original publication

Record last updated 28 November 2025

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