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AI Architecture

Grouped Query Attention (GQA): How Modern LLMs Shrink KV Cache

GQA cuts KV cache 4-8x vs. multi-head attention with minimal quality loss. Architecture, memory math, MHA vs MQA vs GQA trade-offs, and which models (LLaMA 3, Mistral, Gemma) use it.

Published May 13, 2026
16 min read
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AI Architecture

How Speculative Decoding Works: Draft Models and 3x Speedup

Speculative decoding proposes token batches with a small draft model and verifies them in one large-model pass — 2-3x speedup with zero quality loss. Here's the algorithm, the acceptance math, and when it fails.

AI Architecture

Inside Model Context Protocol: How MCP Servers Actually Work

MCP connects AI models to tools via JSON-RPC 2.0 across stdio and HTTP transports. This deep-dive covers the host-client-server split, capability negotiation, the tool call state machine, and why the protocol was designed this way.

AI Architecture

How Vector Databases Actually Work: HNSW, ANN, and Retrieval Architecture

Vector databases are not magic. This deep-dive covers HNSW graph structure, ANN tradeoffs, index construction costs, and the retrieval pipeline behind every RAG system.

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