Flow-Controlled Scheduling for LLM Inference with Provable Stability Guarantees

The project addresses a critical bottleneck in LLM serving: the unpredictable memory growth of the KV cache during the decoding phase. Current industry standards like vLLM (PagedAttention) manage memory fragmentation well but still rely on heuristic-based admission control which can lead to preemption or crashes under extreme load. This paper introduces a more rigorous mathematical approach using flow control. However, as a research-centric repository with 0 stars and 2 forks, it currently lacks any ecosystem moat. The primary value lies in the 'provable stability' algorithm; once published, these mathematical insights are likely to be integrated directly into dominant inference engines like vLLM, SGLang, or TensorRT-LLM. Frontier labs (OpenAI, Google) already employ sophisticated, proprietary versions of these schedulers to maintain their SLAs, making the competitive advantage for a standalone project quite thin. The displacement horizon is short because the core innovation is an algorithmic pattern that can be reimplemented by any senior systems engineer at a major lab or cloud provider within a few months of reading the paper.