Cohere Command A TPS calculator

Open weights Cohere 111B parameters March 2025

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

43 of 818 cards that can run it

Smallest card that fits

Radeon Instinct MI200

64 GB · Q3_K_M · 13.2 tok/s

Fastest card

B200

30.5 tok/s · 180 GB

Which GPUs can run Cohere Command A?

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

43 cards match

Calculating

					Needs	Quantisation	Fit
30.5 tok/s 18–49 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	119.5 GB	Q8_0	Comfortable
30.5 tok/s 18–49 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	119.5 GB	Q8_0	Comfortable
29.6 tok/s 18–47 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	67.9 GB	Q4_K_M	Tight
29.6 tok/s 18–47 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	67.9 GB	Q4_K_M	Tight
28.3 tok/s 17–45 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	93.7 GB	Q6_K	Comfortable
26.9 tok/s 16–43 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	80.8 GB	Q5_K_M	Tight
24.4 tok/s 15–39 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	119.5 GB	Q8_0	Comfortable
24.4 tok/s 15–39 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	119.5 GB	Q8_0	Comfortable
23.0 tok/s 14–37 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	93.7 GB	Q6_K	Comfortable
22.9 tok/s 14–37 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	80.8 GB	Q5_K_M	Tight
22.9 tok/s 14–37 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	80.8 GB	Q5_K_M	Tight
22.9 tok/s 14–37 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	80.8 GB	Q5_K_M	Tight
20.8 tok/s 12–33 · low confidence	H100 SXM5 64 GB NVIDIA	64 GB	2,020 GB/s	Mar 2023	54.9 GB	Q3_K_M	Tight
18.7 tok/s 11–30 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	119.5 GB	Q8_0	Tight
18.7 tok/s 11–30 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	119.5 GB	Q8_0	Tight
18.0 tok/s 11–29 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	67.9 GB	Q4_K_M	Tight
18.0 tok/s 11–29 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	67.9 GB	Q4_K_M	Tight
18.0 tok/s 11–29 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	67.9 GB	Q4_K_M	Tight
18.0 tok/s 11–29 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	67.9 GB	Q4_K_M	Tight
18.0 tok/s 11–29 · low confidence	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	67.9 GB	Q4_K_M	Tight
18.0 tok/s 11–29 · low confidence	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	67.9 GB	Q4_K_M	Tight
17.9 tok/s 11–29 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	119.5 GB	Q8_0	Comfortable
17.1 tok/s 10–27 · low confidence	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	67.9 GB	Q4_K_M	Tight
17.1 tok/s 10–27 · low confidence	A800 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Nov 2022	67.9 GB	Q4_K_M	Tight
15.9 tok/s 10–25 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	119.5 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: Cohere
Organisation type: Industry
Country: Canada
Published: 13 March 2025

What it does

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

Domain: Language
Task: Language modeling/generation, Chat, Code 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: 111B
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)
Training code: Unreleased
Hugging Face: CohereLabs

How it is classified

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

Likely above 10²³ FLOP: Yes
Record confidence: Confident

Sources

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

Reference: Introducing Command A: Max performance, minimal compute
Last updated: 28 November 2025

The extremes

The ten fastest GPUs for Cohere Command A

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 Cohere Command A

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

Radeon Instinct MI200

Memory needed

54.9 GB

Fastest

30.5 tok/s

Cohere Command A sits at 111B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 43 of the cards we track can hold it.

The smallest card that holds it is the Radeon Instinct MI200 with 64 GB, running it at Q3_K_M and producing around 13.2 tokens per second.

Top of the range is the B200, at roughly 30.5 tokens per second thanks to 8,000 GB/s of bandwidth.

About this model

Cohere Command A was published by Cohere, in Canada, in March 2025. industry is the category the publisher falls under.

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

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 CohereLabs organisation on Hugging Face.

How fast it runs, and why

Half the cards that hold it manage more than 17.9 tokens per second, and 39 exceed reading speed outright.

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 Cohere Command A

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 Cohere Command A — around 54.9 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

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Cohere Command A.
03

Decide how much compression you will accept

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 Cohere Command A by squeezing it further than you would want.
04

Sort by speed

Ranking by tokens per second for Cohere Command A follows memory bandwidth, not core counts, which is why the B200 tops it at 30.5 tok/s.
05

Read the fit column last

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

Check the card from the other side

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Cohere Command A.

Answers

Cohere Command A — common questions

How much VRAM does Cohere Command A need?

About 54.9 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 Cohere Command A open source?

Its weights are published, so Cohere Command A 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 Cohere Command A have?

Cohere Command A has 111B parameters. a 111 billion parameter model optimized for demanding enterprises that require fast, secure, and high-quality AI. 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 Cohere Command A?

Cohere Command A was published by Cohere, based in Canada, categorised as industry.

When was Cohere Command A released?

Cohere Command A was published in March 2025.

What is Cohere Command A used for?

Cohere Command A works in Language, and is recorded as handling language modeling/generation, Chat, Code generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Cohere Command A?

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

Can I run Cohere Command A 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 24.7 GB. Our figures for Cohere Command A assume it is fully resident.

Would two GPUs run Cohere Command A faster?

Two cards buy memory rather than speed. That matters for Cohere Command A only if one card cannot hold it — 43 can, so a second adds little.

Why does the quantisation differ between cards for Cohere Command A?

Because capacity varies, so does how hard Cohere Command A has to be squeezed — 6 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Cohere Command A speed estimates?

These are estimates with real error bars. The fastest result here, 18–49 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 Cohere Command A?

The smallest card in our catalogue that holds Cohere Command A is the Radeon Instinct MI200, with 64 GB of memory. It runs the model at Q3_K_M using about 54.9 GB, and produces roughly 13.2 tokens per second. 43 cards in total can run it.

How fast is Cohere Command A on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 30.5 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 39 of the cards that can run Cohere Command A clear that.

Source

Original publication

Record last updated 28 November 2025

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.

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