In the news
Announcing Long-Context Training on TPUs with MaxText Context Parallelism- Google Developers Blog
Google Developers · Published · 3 min read
In 30 seconds
- What happened
- Google released MaxText v0.2.4 with four context parallelism strategies for training long-context models on TPUs, achieving 70% MFU on sequences up to 4 million tokens.
- Why it matters
- ML engineers training large language models on Google Cloud TPUs who need to handle sequences longer than fit in a single chip's memory.
- Watch out
- Context parallelism strategies have different tradeoffs; choice depends on model architecture, KV head count, and whether gathered keys-values fit in HBM. Performance varies significantly by strategy and sequence length.
- long-context
The patterns behind this
- Agentic Context Engineering (Evolving Playbook)
- Contextual Structured Memory
- Contextual Unstructured Memory
Each one covers how the technique works, when it earns its cost, and where it breaks.
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