arXiv:2605.30218v1 Announce Type: new Abstract: Temperature-zero BF16 LLM inference is often treated as reproducible, yet the same request can emit different tokens when decoded alone or inside a larger batch. Existing fixes use batch-invariant operators or LLM-42's per-token verification, incurring cost even when most steps are stable. We ask whether verification can be applied exclusively to flipped tokens. Across five models, batch-induced token flips are sparse on the flip-rate benchmarks: on MATH500, Llama-3.1-8B flips on $0.48\%$ of synchronous decode steps, and all tested models stay wi
This research addresses a critical, often overlooked, challenge in LLM deployment — ensuring consistent and reproducible inference, which becomes more pressing as LLMs are integrated into sensitive applications.
Improved reliability and reduced computational overhead for LLM inference will accelerate AI adoption and trust in production environments, impacting everything from enterprise software to autonomous systems.
The focus shifts from blanket batch-invariant operators to more efficient, targeted verification for LLM inference, potentially making large-scale AI deployments more cost-effective and dependable.
- · AI developers
- · Cloud providers
- · Enterprises deploying LLMs
- · High-reliability AI applications
- · Providers of inefficient batch-invariant operators
LLM inference becomes more efficient and reliable, reducing operational costs.
Increased trust in LLM outputs leads to broader and more critical applications of AI.
The overall compute efficiency gain contributes to a more sustainable AI infrastructure, delaying the full impact of potential energy bottlenecks.
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Read at arXiv cs.LG