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Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.
speculative-decoding is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 58/100. # Speculative Decoding: Accelerating LLM Inference ## When to Use This Skill Use Speculative Decoding when you need to: - **Speed up inference** by 1.5-3.6× wit. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding speculative-decoding to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
codex skill install chen-yu-hao/codex-webReview the upstream source before running this command.
Scan coverage: Install metadata and the indexed document body.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
# Speculative Decoding: Accelerating LLM Inference ## When to Use This Skill Use Speculative Decoding when you need to: - **Speed up inference** by 1.5-3.6× without quality loss -