Grouped Query Attention (GQA)

Grouped Query Attention (GQA)Grouped Query Attention (GQA)
GQ

Grouped Query Attention (GQA)

An efficient attention mechanism for transformer models that balances quality and speed.

2023Active
5.0 (1)
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About

Grouped Query Attention (GQA) is an optimization technique for transformer models that balances computational efficiency and model performance. It acts as a middle ground between Multi-Head Attention (MHA) and Multi-Query Attention (MQA), reducing computational needs and memory bandwidth usage while maintaining model quality. GQA achieves this by grouping query heads and allowing them to share key and value heads, which significantly reduces the memory footprint of the KV cache during inference, making it particularly beneficial for large language models with long context windows.
Frequently asked

What does Grouped Query Attention (GQA) do?

Grouped Query Attention (GQA) is an optimization technique for transformer models that balances computational efficiency and model performance. It acts as a middle ground between Multi-Head Attention (MHA) and Multi-Query Attention (MQA), reducing computational needs and memory bandwidth usage while maintaining model quality. GQA achieves this by grouping query heads and allowing them to share key and value heads, which significantly reduces the memory footprint of the KV cache during inference,…

When was Grouped Query Attention (GQA) founded?

Grouped Query Attention (GQA) was founded in 2023.

What industry does Grouped Query Attention (GQA) operate in?

Grouped Query Attention (GQA) operates in Foundation Model, Large Language Model, Transformer Architecture, AI Infrastructure, AI Tools & Apps, Generative AI.

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