PagedAttention

← Previous Chapter: Executive Summary | Series Hub | Next Chapter: Part 2: SFT Data Engineering → Prerequisite: Read Executive Summary: The Rise of Specialized Small Language Models for cost break-even formulas and hybrid AI architectural framing. Answer-first: The Hybrid AI Routing architecture evaluates incoming request complexity and token uncertainty in under 3.5ms. 80% of structured queries are served locally by fine-tuned 7B models on vLLM within 35ms TTFT, while low-confidence requests automatically cascade to Claude 3.5 Sonnet through a localized PII sanitization proxy, cutting monthly API bills by 85%. ...

Prerequisite: Familiarity with agent execution loops and memory storage examined in Part 7 — Agentic Memory Systems: Episodic & Working Storage. Review it first if needed. Answer-first: Serving large language models at enterprise scale bottlenecks on GPU VRAM capacity and severe KV cache fragmentation during high-concurrency workloads. Deploying vLLM with PagedAttention virtual memory mapping, prefix-sharing RadixAttention, speculative decoding draft models, and FP4/AWQ quantization doubles serving throughput while slashing P99 token generation latency by 58% on production clusters. ...

High-throughput local LLM architecture guide combining vLLM PagedAttention virtual memory, Prefill-Decode disaggregation over RoCE v2/NVLink, and a custom Go API Gateway with SHA256 prompt prefix context-affinity routing, zero-allocation SSE streaming, and 71% cost savings over SaaS APIs.