Command R+ TPS calculator

Open weights Cohere,Cohere for AI 104B 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

43 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Radeon Instinct MI200

64 GB · IQ4_XS · 12.8 tok/s

Fastest card

B200

32.6 tok/s · 180 GB

Which GPUs can run Command R+?

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

43 cards match

Calculating

					Needs	Quantisation	Fit
32.6 tok/s 20–52 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	112.0 GB	Q8_0	Comfortable
32.6 tok/s 20–52 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	112.0 GB	Q8_0	Comfortable
31.6 tok/s 19–51 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	63.6 GB	Q4_K_M	Tight
31.6 tok/s 19–51 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	63.6 GB	Q4_K_M	Tight
28.7 tok/s 17–46 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	75.7 GB	Q5_K_M	Tight
26.0 tok/s 16–42 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	112.0 GB	Q8_0	Comfortable
26.0 tok/s 16–42 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	112.0 GB	Q8_0	Comfortable
24.4 tok/s 15–39 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	75.7 GB	Q5_K_M	Tight
24.4 tok/s 15–39 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	75.7 GB	Q5_K_M	Tight
24.4 tok/s 15–39 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	75.7 GB	Q5_K_M	Tight
20.8 tok/s 12–33 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	112.0 GB	Q8_0	Tight
20.2 tok/s 12–32 · low confidence	H100 SXM5 64 GB NVIDIA	64 GB	2,020 GB/s	Mar 2023	57.6 GB	IQ4_XS	Tight
19.9 tok/s 12–32 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	112.0 GB	Q8_0	Tight
19.9 tok/s 12–32 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	112.0 GB	Q8_0	Tight
19.2 tok/s 12–31 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	63.6 GB	Q4_K_M	Tight
19.2 tok/s 12–31 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	63.6 GB	Q4_K_M	Tight
19.2 tok/s 12–31 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	63.6 GB	Q4_K_M	Tight
19.2 tok/s 12–31 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	63.6 GB	Q4_K_M	Tight
19.2 tok/s 12–31 · low confidence	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	63.6 GB	Q4_K_M	Tight
19.2 tok/s 12–31 · low confidence	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	63.6 GB	Q4_K_M	Tight
19.1 tok/s 11–30 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	112.0 GB	Q8_0	Comfortable
18.2 tok/s 11–29 · low confidence	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	63.6 GB	Q4_K_M	Tight
18.2 tok/s 11–29 · low confidence	A800 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Nov 2022	63.6 GB	Q4_K_M	Tight
16.9 tok/s 10–27 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	112.0 GB	Q8_0	Tight
16.9 tok/s 10–27 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	112.0 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,Cohere for AI
Organisation type: Industry,Industry
Country: Canada
Published: 4 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, Language generation, Translation, Code autocompletion

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: 104B
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: CohereForAI

How it is classified

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

Foundation model: Yes
Likely above 10²³ FLOP: Yes
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 that run Command R+

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 Command R+

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

57.6 GB

Fastest

32.6 tok/s

Command R+ reaches a parameter count of 104B. That puts it above consumer hardware, into the range where a card is bought for this purpose rather than repurposed for it. The number of cards we track that can hold it: 43.

The smallest card that holds it is Radeon Instinct MI200, with a memory capacity of 64 GB, running it at a compression of IQ4_XS and producing around 12.8 tokens per second.

At the other end sits B200, generating roughly 32.6 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

About this model

Command R+ was published by Cohere,Cohere for AI, in the country recorded as Canada, during April 2024. It comes out of an organisation categorised as industry,Industry.

It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Language generation, Translation, Code autocompletion.

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. On Hugging Face it is published under the organisation CohereForAI.

How fast it runs, and why

Across every card that can run it, the middle of the range sits at 18.2 tokens per second. Exceeding reading speed outright: 37 of them.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

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 Command R+

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

Start from what it actually needs, which is the requirement of Command R+, needing around 57.6 GB at a compression of IQ4_XS. Capacity is the gate — a card either holds it or it does not.
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 Command R+.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy, reaching a compression of IQ4_XS on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
04

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for Command R+. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 32.6 tok/s.
05

Check the fit verdict before buying

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of Command R+. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
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 you have settled on Command R+.

Answers

Command R+ — common questions

Command R+— why does the quantisation differ between cards?

Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 4. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Command R+— how accurate are these speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: 20–52 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Command R+— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Radeon Instinct MI200, with a memory capacity of 64 GB. It runs the model at a compression of IQ4_XS using about 57.6 GB, and produces roughly 12.8 tokens per second. The number of cards able to run it in total: 43.

Command R+— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 32.6 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and the number of cards clearing that: 37.

Command R+— how much VRAM does it need?

It needs about 57.6 GB at a compression of IQ4_XS, 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.

Command R+— is it open source?

Its weights are published, so it 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.

Command R+— how many parameters does it have?

It has a parameter count of 104B. 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.

Command R+— who created it?

It was published by Cohere,Cohere for AI, based in Canada, an organisation categorised as industry,Industry.

Command R+— when was it released?

It 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.

Command R+— what is it used for?

It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Language generation, Translation, Code autocompletion. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Command R+— where can I download it?

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

Command R+— can I run it if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part held in system memory drags the whole thing down. The nearest miss we calculate falls short by 20.4 GB. Every figure here assumes the whole model is resident on the card.

Command R+— would two GPUs run it faster?

A second card roughly doubles the memory available but not the generation rate. The number already able to run it alone: 43. So a second card is rarely the answer here.

Source

Original publication

Record last updated 28 November 2025

The other direction

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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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