Token-level LLM filtering adds minimal CPU overhead, benchmark shows

A recent benchmark on CPU performance indicates that token-level filtering for LLMs adds a minimal overhead of 0.15 to 0.34 milliseconds per token, even with up to 10,000 banned patterns. The primary cost associated with this filtering method is the initial automaton build time, which is a one-time expense rather than a per-request cost. Batching can further reduce per-item costs, and the percentage impact on overall decoding time depends heavily on the specific model and workload. AI

IMPACT This research quantifies the performance cost of token-level filtering, informing developers about potential latency impacts when implementing safety measures in LLM applications.

RANK_REASON Benchmark results detailing performance characteristics of a specific LLM filtering technique. [lever_c_demoted from research: ic=1 ai=1.0]

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