🗂️ AI & Agents · View mindmap

Speculative Inference

Speculative Inference (draft-and-verify) accelerates large language model generation by utilizing a smaller Draft Model to propose multiple candidate tokens, which are validated in parallel by a larger Target Model. This method reduces inference latency and computational cost by amortizing the verification step across $K$ tokens, yielding efficiency gains proportional to the token acceptance rate.

Mechanism

• Speculation: Draft model generates $K$ tokens sequentially with minimal compute overhead.
• Verification: Target model processes the entire sequence in a single forward pass to verify token probabilities.
• Decision: Accepted tokens are appended; rejected tokens trigger backtracking or fallback gen

Implementations & Case Studies

• DeepSeek DSpark: A specialized speed layer for LLMs introduced by DeepSeek that enhances speculative decoding performance.
  • Demonstrated ability to double inference speed for Qwen3 models.
  • Acts as an acceleration layer without requiring full model retraining.
  • See: DeepSeek DSpark: LLM Inference Acceleration via Enhanced Speculative Decoding

References

• DeepSeek DSpark: LLM Inference Acceleration via Enhanced Speculative Decoding