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speculative-decoding

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.

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# 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
- **Reduce latency** for real-time applications (chatbots, code generation)
- **Optimize throughput** for high-volume serving
- **Deploy efficiently** on limited hardware
- **Generate faster** without changing model architecture

**Key Techniques**: Draft model speculative decoding, Medusa (multiple heads), Lookahead Decoding (Jacobi iteration)

**Papers**: Medusa (arXiv 2401.10774), Lookahead Decoding (ICML 2024), Speculative Decoding Survey (ACL 2024)