MarginGate: Batch-Invariant Large Model Inference via Sparse Boundary-Triggered Validation

In the production deployment of large language models, batch sensitivity causes the same request to produce different results when decoded individually versus in batches, affecting scenarios requiring deterministic outputs such as mathematical reasoning and code generation. MarginGate monitors the logit boundary during token generation and triggers validation only at low-boundary steps. It achieves 100% sequence-level deterministic decoding with a validation trigger rate of 18-49%, reducing latency overhead by more than 2x compared to full validation, providing an efficient solution for deterministic inference.

The root cause of batch sensitivity lies in the non-associativity of floating-point operations under BF16 precision. Changes in computation order during batch processing lead to numerical differences, which can alter token selection at critical steps and cascade. Existing solutions fall into two categories: 1) Batch-invariant operators (complex to implement and performance-sacrificing); 2) Token-wise validation (highly general but doubles latency). The core question is whether validation is needed for every token.

Core Insight: Token flips caused by batch processing are extremely sparse (0.3-1.3%), and a small logit layer boundary (difference between top1 and top2) before flipping is an early warning signal. Strategy: For high-boundary steps, directly use batch decoding results; for low-boundary steps, trigger single-sample validation, and replace KV cache columns if results mismatch. The threshold is optimized via a calibration set and has cross-dataset transferability.

Experiments confirm that MarginGate achieves 100% sequence-level determinism; the validation trigger rate is 18.56% for Llama-3.1-8B and 15.05% for Qwen2.5-14B; latency is reduced by 2.23x (Llama) and 1.99x (Qwen) compared to full validation; even for the challenging model DSR1-Distill-Qwen-7B with a trigger rate of 49.5%, it still maintains 100% determinism.

It consists of three lightweight components: 1. Boundary monitoring module (calculates logit differences and compares with thresholds, negligible overhead); 2. Conditional validation engine (triggers single-sample validation at low boundaries and decides whether to replace KV cache); 3. Threshold calibration tool (automatically optimizes thresholds based on a calibration set).

Applicable to scenarios requiring deterministic outputs: mathematical reasoning (ensures consistent answers for easy cache verification), code generation (eliminates batch differences to improve reproducibility), automated testing (avoids execution environment fluctuations), and distributed inference (consistent outputs across different nodes).

MarginGate successfully reveals a system design principle: accurately identify edge cases rather than adopting conservative strategies. Insight: LLM inference optimization can use the philosophy of "optimistic execution + conservative validation", accepting minor uncertainties and correcting them via lightweight monitoring—this approach has been proven effective in the distributed systems domain.