WizardLM-2 8x22B TPS calculator

Open weights Microsoft 141B 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

37 of 818 cards that can run it

Smallest card that fits

A100 SXM4 80 GB

80 GB · Q3_K_M · 16.5 tok/s

Fastest card

H100 NVL 94 GB

29.1 tok/s · 94 GB

Which GPUs can run WizardLM-2 8x22B?

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

37 cards match

Calculating

					Needs	Quantisation	Fit
29.1 tok/s 17–47 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	77.8 GB	IQ4_XS	Tight
27.4 tok/s 16–44 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	102.4 GB	Q5_K_M	Tight
27.2 tok/s 16–44 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	69.6 GB	Q3_K_M	Tight
27.2 tok/s 16–44 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	69.6 GB	Q3_K_M	Tight
24.8 tok/s 15–40 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	77.8 GB	IQ4_XS	Tight
24.0 tok/s 14–38 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	151.7 GB	Q8_0	Tight
24.0 tok/s 14–38 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	151.7 GB	Q8_0	Comfortable
23.3 tok/s 14–37 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	86.0 GB	Q4_K_M	Tight
23.3 tok/s 14–37 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	86.0 GB	Q4_K_M	Tight
22.3 tok/s 13–36 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	102.4 GB	Q5_K_M	Tight
21.3 tok/s 13–34 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	118.8 GB	Q6_K	Tight
21.3 tok/s 13–34 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	118.8 GB	Q6_K	Tight
19.2 tok/s 12–31 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	151.7 GB	Q8_0	Comfortable
19.2 tok/s 12–31 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	151.7 GB	Q8_0	Comfortable
16.5 tok/s 10–26 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	69.6 GB	Q3_K_M	Tight
16.5 tok/s 10–26 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	69.6 GB	Q3_K_M	Tight
16.5 tok/s 10–26 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	69.6 GB	Q3_K_M	Tight
16.5 tok/s 10–26 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	69.6 GB	Q3_K_M	Tight
16.5 tok/s 10–26 · low confidence	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	69.6 GB	Q3_K_M	Tight
16.5 tok/s 10–26 · low confidence	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	69.6 GB	Q3_K_M	Tight
15.7 tok/s 9–25 · low confidence	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	69.6 GB	Q3_K_M	Tight
15.7 tok/s 9–25 · low confidence	A800 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Nov 2022	69.6 GB	Q3_K_M	Tight
14.1 tok/s 8–22 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	151.7 GB	Q8_0	Comfortable
13.7 tok/s 8–22 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	102.4 GB	Q5_K_M	Tight
13.7 tok/s 8–22 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	102.4 GB	Q5_K_M	Tight

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: Microsoft
Organisation type: Industry
Country: United States of America
Published: 15 April 2024

What it does

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

Domain: Language
Task: Language modeling/generation, Question answering
Base model: Mixtral 8x22B

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: 141B
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 (unrestricted)
Training code: Unreleased
Hugging Face: alpindale

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 WizardLM-2 8x22B

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 WizardLM-2 8x22B

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

A100 SXM4 80 GB

Memory needed

69.6 GB

Fastest

29.1 tok/s

WizardLM-2 8x22B sits at 141B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 37 of the cards we track can hold it.

The least hardware that works is a A100 SXM4 80 GB. Its 80 GB is enough at Q3_K_M compression, giving roughly 16.5 tokens per second.

The quickest result comes from a H100 NVL 94 GB at around 29.1 tokens per second — its 3,940 GB/s of bandwidth is what buys that.

Where it came from

WizardLM-2 8x22B was published by Microsoft, in United States of America, in April 2024. The organisation is categorised as industry.

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

It is derived from Mixtral 8x22B rather than trained from scratch, which is the usual way a specialised model is produced.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the alpindale organisation on Hugging Face.

Understanding the speeds

The median result is around 16.5 tokens per second; 35 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.

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.

Step by step

How to choose a GPU for WizardLM-2 8x22B

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

The table lists every card that can hold WizardLM-2 8x22B — around 69.6 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
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 WizardLM-2 8x22B stops fitting a card that seemed fine.
03

Set a quality floor

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 WizardLM-2 8x22B by squeezing it further than you would want.
04

Sort by speed

The speed ordering for WizardLM-2 8x22B is effectively an ordering by memory bandwidth, which is why the H100 NVL 94 GB tops it at 29.1 tok/s.
05

Read the fit column last

A tight fit runs WizardLM-2 8x22B 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

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for WizardLM-2 8x22B alone — a card is usually bought for more than one model.

Answers

WizardLM-2 8x22B — common questions

When was WizardLM-2 8x22B released?

WizardLM-2 8x22B 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 WizardLM-2 8x22B used for?

WizardLM-2 8x22B works in Language, and is recorded as handling language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download WizardLM-2 8x22B?

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

Can I run WizardLM-2 8x22B if it does not fit in my GPU?

It can be split between the card and system memory, but WizardLM-2 8x22B generates painfully slowly that way — the nearest miss we calculate is short by 21.2 GB. Nothing on this page assumes offloading.

Would two GPUs run WizardLM-2 8x22B faster?

Capacity adds across cards; throughput does not. Since 37 of the cards we track already hold WizardLM-2 8x22B on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for WizardLM-2 8x22B?

Each card is shown running the least-compressed copy it can hold, and WizardLM-2 8x22B appears at 6 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these WizardLM-2 8x22B speed estimates?

These are estimates with real error bars. The fastest result here, 17–47 tok/s on the H100 NVL 94 GB, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run WizardLM-2 8x22B?

The smallest card in our catalogue that holds WizardLM-2 8x22B is the A100 SXM4 80 GB, with 80 GB of memory. It runs the model at Q3_K_M using about 69.6 GB, and produces roughly 16.5 tokens per second. 37 cards in total can run it.

How fast is WizardLM-2 8x22B on a GPU?

It depends on the card. The quickest we calculate is a H100 NVL 94 GB at about 29.1 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 35 of the cards that can run WizardLM-2 8x22B clear that.

How much VRAM does WizardLM-2 8x22B need?

About 69.6 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.

Is WizardLM-2 8x22B open source?

Its weights are published, so WizardLM-2 8x22B 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 WizardLM-2 8x22B have?

WizardLM-2 8x22B has 141B parameters. Parameters: 141B. 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 WizardLM-2 8x22B?

WizardLM-2 8x22B was published by Microsoft, based in United States of America, categorised as industry.

Source

Original publication

Record last updated 28 November 2025

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