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New Kernels Ensure Deterministic LLM Inference Across Tensor Parallel Sizes

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Tree-Based Invariant Kernels (TBIK) to ensure deterministic inference in large language models, regardless of tensor parallel (TP) size. This addresses a critical issue where identical inputs can produce different outputs due to variations in TP size and floating-point arithmetic. TBIK guarantees bit-wise reproducibility by aligning reduction orders through a hierarchical binary tree structure, which is crucial for applications like LLM-as-a-judge and reinforcement learning. AI

IMPACT Ensures consistent LLM outputs for critical applications like RL and evaluation, removing a key barrier to reliable deployment.

RANK_REASON The cluster contains an academic paper detailing a new technical method for improving LLM inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Kernels Ensure Deterministic LLM Inference Across Tensor Parallel Sizes

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The cluster contains an academic paper detailing a new technical method for improving LLM inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Ziyang Zhang, Xinheng Ding, Jiayi Yuan, Rixin Liu, Huizi Mao, Jiarong Xing, Zirui Liu ·

    Deterministic Inference across Tensor Parallel Sizes That Eliminates Training-Inference Mismatch

    arXiv:2511.17826v2 Announce Type: replace-cross Abstract: Deterministic inference is increasingly critical for large language model (LLM) applications such as LLM-as-a-judge evaluation, multi-agent systems, and Reinforcement Learning (RL). However, existing LLM serving frameworks…

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