Chinchilla
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
- DeepMind
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 29 March 2022
- Authors
- Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, Laurent Sifre
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Approach
- Self-supervised learning
- Numerical format
- BF16
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
- 70B
- Training data
- 1,400,000,000,000 tokens
- Epochs
- 1
- Batch size
- 3,000,000
"We test this hypothesis by training a predicted compute-optimal model, \chinchilla, that uses the same compute budget as \gopher but with 70B parameters and 4× more more data. \chinchilla uniformly and significantly outperforms \Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks."
Table 1 shows Chinchilla was training on 1.4 trillion tokens 1 token ~ 0.75 words
Table 1. "1.5M → 3M"
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
- 5.8 × 10²³ FLOP
- How it was established
- Reported
"Both Chinchilla and Gopher have been trained for the same number of FLOPs but differ in the size of the model and the number of training tokens." We see the number of flops in table 3
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,Google TPU v3
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
- Unreleased
- Training code
- Unreleased
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
- Why it is tracked
- SOTA improvement,Historical significance
- Record confidence
- Confident
- Citations
- 3,188
- Benchmark data
- Chinchilla
Proposes new scaling law, with good empirical results
Sources
Where this record came from and when it was last checked.
- Reference
- Training Compute-Optimal Large Language Models
- Last updated
- 25 May 2026
What the numbers mean
About this model
Chinchilla was published by DeepMind, in United Kingdom of Great Britain and Northern Ireland, in March 2022. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required around 5.8 × 10²³ FLOP of arithmetic, on Google TPU v4,Google TPU v3, which is a statement about the training budget rather than about inference.
The training set ran to roughly 1,400,000,000,000 tokens.
Its inclusion criterion is sOTA improvement,Historical significance.
Answers
Chinchilla — common questions
How much compute was used to train Chinchilla?
Around 5.8 × 10²³ FLOP, on Google TPU v4,Google TPU v3. 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 Chinchilla?
None. Chinchilla 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.
Is Chinchilla open source?
No. Chinchilla has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Chinchilla have?
Chinchilla has 70B parameters. "We test this hypothesis by training a predicted compute-optimal model, \chinchilla, that uses the same compute budget as \gopher but with 70B parameters and 4× more more data. \chinchilla uniformly and significantly outperforms \Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks.". 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 Chinchilla?
Chinchilla was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry.
When was Chinchilla released?
Chinchilla was published in March 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 Chinchilla used for?
Chinchilla works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.