CPM-Ant TPS calculator

Open weights Tsinghua University,Beijing Academy of Artificial Intelligence / BAAI,ModelBest,OpenBMB (Open Lab for Big Model Base) 10B parameters September 2022

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 · 20.3 tok/s

Fastest card

B200

339 tok/s · 180 GB

Which GPUs can run CPM-Ant?

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
339 tok/s 203–542 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	11.4 GB	Q8_0	Comfortable
339 tok/s 203–542 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	11.4 GB	Q8_0	Comfortable
271 tok/s 162–433 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	11.4 GB	Q8_0	Comfortable
271 tok/s 162–433 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	11.4 GB	Q8_0	Comfortable
216 tok/s 130–346 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	11.4 GB	Q8_0	Comfortable
207 tok/s 124–331 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	11.4 GB	Q8_0	Comfortable
207 tok/s 124–331 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	11.4 GB	Q8_0	Comfortable
198 tok/s 119–317 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	11.4 GB	Q8_0	Comfortable
176 tok/s 106–281 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	11.4 GB	Q8_0	Comfortable
176 tok/s 106–281 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	11.4 GB	Q8_0	Comfortable
176 tok/s 106–281 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	11.4 GB	Q8_0	Comfortable
167 tok/s 100–267 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
146 tok/s 87–233 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.7 GB	Q4_K_M	Tight
142 tok/s 85–228 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
142 tok/s 85–228 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	11.4 GB	Q8_0	Comfortable
142 tok/s 85–228 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
142 tok/s 85–228 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
142 tok/s 85–228 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
118 tok/s 71–189 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	7.9 GB	Q5_K_M	Tight
108 tok/s 65–173 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	11.4 GB	Q8_0	Comfortable
108 tok/s 65–173 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	11.4 GB	Q8_0	Comfortable
90.3 tok/s 54–144 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	11.4 GB	Q8_0	Comfortable
88.4 tok/s 53–141 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	11.4 GB	Q8_0	Comfortable
86.4 tok/s 52–138 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	11.4 GB	Q8_0	Comfortable
86.4 tok/s 52–138 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	11.4 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: Tsinghua University,Beijing Academy of Artificial Intelligence / BAAI,ModelBest,OpenBMB (Open Lab for Big Model Base)
Organisation type: Academia,Academia,Industry
Country: China
Published: 22 September 2022

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

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: 10B
Training data: 33,400,000,000 tokens

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.

Training compute: 2 × 10²² FLOP
How it was established: Operation counting

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Wall-clock time: 1,632 hours (68 days)
Compute cost: $66,779

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)
Training code: Unreleased

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: Open-Source 10B-Parameter Chinese pretrained language model
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run CPM-Ant

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 CPM-Ant

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

Xeon Phi 5110P

Memory needed

6.7 GB

Fastest

339 tok/s

CPM-Ant is small enough at 10B parameters that hardware is rarely the obstacle — 509 of the cards we track can run it, including cards several years old.

At the low end, a Xeon Phi 5110P handles it — 8 GB, at Q4_K_M, for about 20.3 tokens per second.

Top of the range is the B200, at roughly 339 tokens per second thanks to 8,000 GB/s of bandwidth.

What this model is

CPM-Ant was published by Tsinghua University,Beijing Academy of Artificial Intelligence / BAAI,ModelBest,OpenBMB (Open Lab for Big Model Base), in China, in September 2022. academia,Academia,Industry is the category the publisher falls under.

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

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.

What decides the speed

Half the cards that hold it manage more than 22.0 tokens per second, and 469 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.

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.

What went into building it

Training it took roughly 2 × 10²² FLOP of computation — a measure of what producing the model cost, not of how fast it answers.

Around 33,400,000,000 tokens went into training it.

Step by step

How to choose a GPU for CPM-Ant

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 CPM-Ant actually needs — around 6.7 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

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context CPM-Ant can slip off a card that handles short questions easily.
03

Choose how far you will compress it

Compression is what makes CPM-Ant 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

Sort by speed to see how cards rank for CPM-Ant. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 339 tok/s.
05

Read the fit column last

The fit column separates cards that just manage CPM-Ant from those with room to spare. Buy for the second if the context might grow.
06

Check the card from the other side

Following a card through to its own page shows every other model it can hold, which is the question that follows once CPM-Ant is settled.

Answers

CPM-Ant — common questions

How fast is CPM-Ant on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 339 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 469 of the cards that can run CPM-Ant clear that.

How much VRAM does CPM-Ant need?

About 6.7 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 CPM-Ant on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q4_K_M, using about 6.7 GB and generating roughly 146 tokens per second — a tight fit.

Can I run CPM-Ant on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q6_K, using about 9.1 GB and generating roughly 56.2 tokens per second — a tight fit.

Can I run CPM-Ant on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 11.4 GB and generating roughly 47.9 tokens per second — a comfortable fit.

Can I run CPM-Ant on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 11.4 GB and generating roughly 56.8 tokens per second — a comfortable fit.

Is CPM-Ant open source?

Its weights are published, so CPM-Ant 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 CPM-Ant have?

CPM-Ant has 10B parameters. "CPM-Ant is an open-source Chinese pre-trained language model (PLM) with 10B 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 CPM-Ant?

CPM-Ant was published by Tsinghua University,Beijing Academy of Artificial Intelligence / BAAI,ModelBest,OpenBMB (Open Lab for Big Model Base), based in China, categorised as academia,Academia,Industry.

When was CPM-Ant released?

CPM-Ant was published in September 2022. 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 CPM-Ant used for?

CPM-Ant works in Language, and is recorded as handling language modeling/generation, Question answering. 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.

Where can I download CPM-Ant?

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

How much compute was used to train CPM-Ant?

Around 2 × 10²² FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.

Can I run CPM-Ant if it does not fit in my GPU?

Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded CPM-Ant is rarely worth using — the nearest miss we calculate is short by 1.3 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run CPM-Ant faster?

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

Why does the quantisation differ between cards for CPM-Ant?

A larger card holds a more accurate copy. Across the cards that run CPM-Ant, 4 compression levels are used; the floor control above pins it to one.

How accurate are these CPM-Ant speed estimates?

These are estimates with real error bars. The fastest result here, 203–542 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 CPM-Ant?

The smallest card in our catalogue that holds CPM-Ant is the Xeon Phi 5110P, with 8 GB of memory. It runs the model at Q4_K_M using about 6.7 GB, and produces roughly 20.3 tokens per second. 509 cards in total can run it.

Source

Original publication

Record last updated 28 November 2025

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