Why Grouped Query Attention (GQA) Outperforms Multi-head Attention

Tales Of Tensors · Advanced ·🧠 Large Language Models ·4mo ago
What if one architecture tweak made Llama 3 5× faster with 99.8% of the quality? In this deep dive, we break down Grouped Query Attention (GQA)—why it slashes KV-cache memory, speeds up inference, and avoids the instability of Multi-Query Attention. We compare MHA vs MQA vs GQA, show how GQA-8 became the modern default, and share intuition, pitfalls, and next steps (FlashAttention, KV-cache quantization, MHLA). Grouped Query Attention GQA GQA-8 Multi-Head Attention MHA Multi-Query Attention MQA attention mechanisms KV cache KV cache memory KV cache optimization inference latency inference spe…
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Chapters (14)

The Impact of Grouped Query Attention (GQA)
0:24 The Problem: Linear Growth of the KV Cache
0:46 Three Mechanisms: MHA, MQA, and GQA
1:03 Multi-head Attention (MHA) Explained
1:28 The Memory Cost of MHA
1:52 Multi-query Attention (MQA) Explained
2:12 Comparing the Two Extremes (MHA vs MQA)
2:25 Grouped Query Attention (GQA): The Sweet Spot
3:04 Step-by-Step Walkthrough of Grouping
3:40 Why GQA Works: Redundancy in Attention Patterns
4:02 Industry Standard: Llama 2, Mistral, and Gemma
4:24 Performance Benchmarks and Stability
4:47 Three Common Misconceptions About GQA
5:15 Other Attention Optimizations
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Dave Ebbelaar (LLM Eng)