@makora_ai 's sequential Monte Carlo speculative decoding keeps multiple draft tokens alive in parallel instead of rewin…

SemiAnalysis Twitter · SemiAnalysis (@SemiAnalysis_) · 2026-06-06

Makora AI's sequential Monte Carlo speculative decoding method improves LLM inference efficiency by keeping multiple draft tokens alive in parallel rather than rewinding on failed matches.

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Ultra-Low Latency LLM Inference: Benchmarks and Emerging Enterprise Pricing Tier

Extraction

Topics: speculative-decodingllm-inferenceinference-optimization

Claims

Sequential Monte Carlo speculative decoding maintains multiple candidate draft tokens in parallel rather than discarding them on a mismatch.
This approach eliminates the rewind cost incurred when draft tokens fail verification in standard speculative decoding.

Key quotes

@makora_ai 's sequential Monte Carlo speculative decoding keeps multiple draft tokens alive in parallel instead of rewinding failed matches