Capstone 14 — Speculative-Decoding Inference Server

Speculative decoding — a cheap draft proposes tokens, the target model verifies them in one pass — is now a production-ready optimization, not a research trick. EAGLE-3 in vLLM 0.7 ships 2.5-3x throughput on real traffic. P-EAGLE (AWS 2026) pushed parallel speculation even further. SGLang's SpecForge trained draft heads at scale. Red Hat's Speculators hub published aligned drafts for common open models. TensorRT-LLM made speculative decoding first-class on NVIDIA. The 2026 production serving stack is vLLM or SGLang with EAGLE-family drafts, FP8 or INT4 quantization, and HPA on queue-wait. This capstone is to serve two open models at 2.5x+ baseline throughput with a full tail-latency report.