Part 7
Completed
MQA, GQA, and MLA
Implemented and compared Multi-Query Attention (MQA), Grouped-Query Attention (GQA), and Multi-Head Latent Attention (MLA) for memory-efficient inference. Measured memory reduction, speedup, and quality impact of each approach on long-context tasks.
What I Built
Implemented and compared Multi-Query Attention (MQA), Grouped-Query Attention (GQA), and Multi-Head Latent Attention (MLA) for memory-efficient inference. Measured memory reduction, speedup, and quality impact of each approach on long-context tasks.
Key Concepts
Multi-Query AttentionGrouped-Query AttentionMulti-Head Latent AttentionKV Cache CompressionMemory EfficiencyQuality Tradeoffs
Architecture
1
MQA Attention2
GQA Attention3
MLA Attention4
Memory Benchmark5
Quality EvaluatorResults
MQA reduces cache memory by 8x with 2% quality drop. GQA (4 groups) achieves 4x reduction with <1% drop. MLA offers best quality-memory tradeoff.
Key Learnings
- MQA's memory savings are dramatic but quality cost is real
- GQA is the sweet spot for most production systems
- MLA's latent compression is promising but more complex
Challenges
- Balancing memory reduction with model quality
- Implementing efficient grouped attention kernels
- Understanding why quality degrades with shared keys/values