Speculative Decoding

Speculative decoding uses a smaller draft model to propose multiple tokens and a larger target model to verify them, preserving the target distribution while reducing effective decoding latency when drafts are accepted. DSpark adds semi-autoregressive draft generation and confidence-scheduled verification to improve draft quality and serving efficiency [src-179].

Related

  • KV Cache
  • LLM Inference Economics
  • Memory Wall for Long Context

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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From 500 indexed pages and articles.

  1. Wiki concept KV Cache Stored key/value representations of prior tokens used during autoregressive transformer decoding so each new token can attend to the previous context without recomputing every Related by decoding
  2. Wiki concept Memory Wall for Long Context Infrastructure constraint where extending LLM context length is limited more by KV-cache memory bandwidth and capacity than by raw compute. Related by dspark
  3. Wiki concept LLM Inference Economics The cost and latency structure of serving large language models, driven by compute throughput, memory bandwidth, batch size, context length, KV-cache storage, and Related by cache