Core Insights - The article discusses the achievement of SGLang and slime teams in creating a fully reproducible and stable reinforcement learning (RL) training framework based on the Qwen3-8B model, addressing the issue of non-deterministic outputs in large language model (LLM) inference [1][2][6]. Group 1: Deterministic Inference - SGLang and slime teams have developed a deterministic inference solution that integrates batch invariant operators, CUDA Graph, radix cache, and chunked prefill, ensuring high performance while maintaining compatibility with key features [5][8]. - The implementation of batch invariant operators addresses the core issue of output uncertainty in LLM inference, which arises from varying batch sizes during dynamic batching [7][8]. - Testing has shown that the average performance drop for SGLang's solution is 34.35%, significantly better than the 61.5% decline reported by Thinking Machines Lab [5][12]. Group 2: Performance Metrics - The article presents performance metrics for different inference modes, showing that deterministic modes yield consistent outputs across various batch sizes, with unique output counts significantly reduced [10][11]. - In terms of end-to-end latency, deterministic inference shows a performance drop of 25% to 45%, with specific backend performance metrics indicating improvements in certain configurations [12][13]. Group 3: Future Developments - Future efforts will focus on optimizing batch invariant operators to enhance performance, particularly for RL inference, and expanding support to mixture of experts (MoE) models [16][18]. - The team aims to improve radix cache functionality and explore tensor parallelism to further enhance the capabilities of deterministic inference [18].