GQA: The speed hack that makes LLMs faster

Description

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 speed memory bandwidth bottleneck Llama 3 speed T5-XXL results FlashAttention KV cache quantization Multi-Head Latent Attention MHLA transformer optimization transformer attention LLM architecture LLM inference PyTorch attention GQA PyTorch implementation attention heads query key value heads large language models all about llm grouping queries stability vs speed tradeoff model acceleration GPU memory context length sequence length generation speed benchmarks deep learning engineering AI performance tuning modern LLMs Mistral Gemma Granite models Llama 2 70B quality retention 99.8% 5× faster inference parameter efficiency attention patterns kv-heads vs q-heads gqa tutorial gqa explained llm internals llama architecture changes