Cohere Command Light
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
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
- 30 November 2023
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language generation, Chat, Text summarization, Language modeling/generation
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
- 6B
- Training data
- tokens
6B https://aws.amazon.com/bedrock/cohere-command-embed/
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
- 1 × 10²² FLOP
- How it was established
- Hardware
https://docs.cohere.com/docs/environmental-impact 2700kg CO2 equivalent. Cohere used this calculator: https://mlco2.github.io/impact/ This calculator claims that ~40000 TPUv3 hours causes ~3000 kg CO2 emissions in the "us-west1", "us-west2", and "us-west3" regions. Not clear what region the data center Cohere used was in. Google has data centers around the world; *most* regions are similarly carbon intensive as us-west but north-america-northeast is 10x less carbon intensive and south Asia is…
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- Google TPU v4
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
- Closed — provider access only
- Model access
- API access
- 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
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Cohere’s Embed and Command Light Models with Fine-tuning Now Available on Amazon Bedrock
- Last updated
- 28 November 2025
What the numbers mean
About this model
Cohere Command Light was published by Cohere, in Canada, in November 2023. It comes out of industry.
It works in Language, and is recorded as doing language generation, Chat, Text summarization, Language modeling/generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training it took roughly 1 × 10²² FLOP of computation, on Google TPU v4 — a measure of what producing the model cost, not of how fast it answers.
Answers
Cohere Command Light — common questions
Is Cohere Command Light open source?
No. Cohere Command Light has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Cohere Command Light have?
Cohere Command Light has 6B parameters. 6B https://aws.amazon.com/bedrock/cohere-command-embed/. 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 Light?
Cohere Command Light was published by Cohere, based in Canada, categorised as industry.
When was Cohere Command Light released?
Cohere Command Light was published in November 2023. 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 Cohere Command Light used for?
Cohere Command Light works in Language, and is recorded as handling language generation, Chat, Text summarization, 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.
How much compute was used to train Cohere Command Light?
Around 1 × 10²² FLOP, on Google TPU v4. 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.
What GPU do I need to run Cohere Command Light?
None. Cohere Command Light is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
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.