xLAM-8x22B TPS calculator

Open weights Salesforce 141B parameters September 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 xLAM-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: Salesforce
Organisation type: Industry
Country: United States of America
Published: 6 September 2024
Authors: Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Zhiwei Liu, Haolin Chen, Yihao Feng,Tulika Awalgaonkar, Rithesh Murthy, Eric Hu, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong

What it does

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

Domain: Language
Task: Language modeling/generation
Approach: Supervised
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 (restricted use)

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.

Reference: Actions Speak Louder Than Words: Introducing xLAM, Salesforce’s family of Large Action Models
Last updated: 28 November 2025

The extremes

The ten fastest GPUs for xLAM-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 xLAM-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

The hardware side

Minimum card

A100 SXM4 80 GB

Memory needed

69.6 GB

Fastest

29.1 tok/s

xLAM-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.

At the low end, a A100 SXM4 80 GB handles it — 80 GB, at Q3_K_M, for about 16.5 tokens per second.

A H100 NVL 94 GB is the fastest we calculate for it: about 29.1 tokens per second, from 3,940 GB/s of memory bandwidth.

Where it came from

xLAM-8x22B was published by Salesforce, in United States of America, in September 2024. The organisation is categorised as industry.

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

Its starting point was Mixtral 8x22B — most models at this scale are adapted from an existing base rather than built from nothing.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.

Understanding the speeds

Across every card that can run it, the middle of the range is about 16.5 tokens per second, and 35 of them clear the ten tokens per second that roughly matches reading speed.

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.

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

Step by step

How to choose a GPU for xLAM-8x22B

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

01

Check what it needs before anything else

Look at what xLAM-8x22B actually needs — around 69.6 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
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 xLAM-8x22B stops fitting a card that seemed fine.
03

Decide how much compression you will accept

Compression is what makes xLAM-8x22B fit smaller cards, at some cost in accuracy — Q3_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 xLAM-8x22B is effectively an ordering by memory bandwidth, which is why the H100 NVL 94 GB tops it at 29.1 tok/s.
05

Check the fit verdict before buying

The fit column separates cards that just manage xLAM-8x22B from those with room to spare. Buy for the second if the context might grow.
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 xLAM-8x22B.

Answers

xLAM-8x22B — common questions

Where can I download xLAM-8x22B?

The weights for xLAM-8x22B are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Can I run xLAM-8x22B 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 21.2 GB. Our figures for xLAM-8x22B assume it is fully resident.

Would two GPUs run xLAM-8x22B faster?

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

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

A larger card holds a more accurate copy. Across the cards that run xLAM-8x22B, 6 compression levels are used; the floor control above pins it to one.

How accurate are these xLAM-8x22B speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 17–47 tok/s on the H100 NVL 94 GB, 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 xLAM-8x22B?

The smallest card in our catalogue that holds xLAM-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 xLAM-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 xLAM-8x22B clear that.

How much VRAM does xLAM-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 xLAM-8x22B open source?

Its weights are published, so xLAM-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 xLAM-8x22B have?

xLAM-8x22B has 141B 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 xLAM-8x22B?

xLAM-8x22B was published by Salesforce, based in United States of America, categorised as industry.

When was xLAM-8x22B released?

xLAM-8x22B was published in September 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 xLAM-8x22B used for?

xLAM-8x22B 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.

Source

Original publication

Record last updated 28 November 2025

The other direction

Looking at it from the other side?

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