Chinchilla Models by Google DeepMind
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The Chinchilla family of large language models, developed by Google DeepMind, was introduced in March 2022. These models are notable for their exploration of the scaling laws in LLM training. Uniquely, they highlighted that for optimal model performance, the size of the model and the number of training tokens should be proportionately scaled. For instance, the Chinchilla model with 70 billion parameters used the same computational resources as a 280 billion parameter Gopher model but was trained on quadruple the data, leading to enhanced performance across numerous benchmarks. This approach challenged the previous assumption that increasing model size inherently improves performance, emphasizing the critical role of ample data in achieving state-of-the-art results 1)23.
Current Variants
Use-when guidance is based on each model's tracked capabilities, context window, release date, and replacement status.
| Model | Use when | Released | Signals | Status |
|---|---|---|---|---|
| Gopher 280B | Use when the workload needs 280B parameters. | 2022-03 | 280B parameters | Current |
| Chinchilla 70B | Use when the workload needs 70B parameters. | 2022-03 | 70B parameters | Current |
Release Timeline
1 release groupSpecifications(2 models)
| Model | Released | Parameters |
|---|---|---|
| Gopher 280B | 2022-03 | 280B |
| Chinchilla 70B | 2022-03 | 70B |
Frequently Asked Questions
- What is Chinchilla used for?
- Chinchilla is used for coding. The family description and listed model capabilities point to those workloads as the best fit.
- How does Chinchilla compare to T5Gemma?
- Chinchilla by Google DeepMind is strongest where you need coding, while T5Gemma by Google DeepMind is the closest related family to check for agent workflows and tool use. Chinchilla has 2 listed variants, so compare the specs and pricing tables before choosing a production model.
- Which Chinchilla model should I use?
- If price is the main constraint, use the pricing table first because Chinchilla does not have complete provider pricing in the local data. For the most capable/latest local choice, evaluate Gopher 280B.
