Speculative Decoding

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. ...

Tech Radar: SGLang EAGLE-2: Speculative Decoding & Tree-Attention Latency Acceleration Answer-First: SGLang’s native EAGLE-2 implementation establishes the 2026 state-of-the-art for autoregressive latency acceleration, overcoming the memory-bandwidth wall (<1 FLOP/byte) by combining lightweight multi-layer feature extrapolation with dynamic tree-attention verification. On 8x NVIDIA H100 GPU clusters, EAGLE-2 achieves an empirical 2.5x to 3.5x generation speedup and reduces Time-Per-Output-Token from 35.7ms to 11.6ms on Llama-3-70B, with mathematically zero output distribution degradation. Prerequisite: Readers should understand Transformer autoregressive decoding mechanics (arithmetic intensity under single-token generation, KV cache memory footprint), basic speculative decoding principles (draft-and-verify loops), and GPU runtime profiling (CUDA event queries, Tensor Core saturation, and PyTorch/Triton kernel execution). ...