Xiaomi MiMo

Xiaomi has unveiled a massive leap in inference performance for its MiMo V2.5 model, achieving a throughput of 1000-3000 TPS (Tokens Per Second) by leveraging DFlash architecture and Persistent Kernel technology. An open-source release of the codebase is expected shortly. ▶ Hardware-Aware Co-optimization: DFlash represents a fundamental restructuring aimed at overcoming memory bandwidth bottlenecks, while Persistent Kernels minimize the overhead of frequent operator switching. ▶ Unlocking Real-Time Agentic Workflows: This level of throughput is a game-changer for AI agents, enabling near-instantaneous multi-step reasoning and long-form content generation. Bagua Insight Xiaomi’s breakthrough signals a strategic shift in the GenAI landscape: the focus is migrating from raw parameter counts to "Inference Velocity." Achieving 3000 TPS isn't just a benchmark victory; it is the prerequisite for seamless, human-like interaction in edge and cloud environments. By promising to open-source DFlash, Xiaomi is positioning itself as an infrastructure innovator, potentially disrupting the status quo held by established inference frameworks like vLLM or TensorRT-LLM. This move aims to capture the developer mindshare by providing the "fastest lane" for LLM deployment. Actionable Advice Developers and CTOs should prioritize benchmarking the DFlash repository upon its release. If the performance gains translate across diverse hardware tiers, it could significantly slash the Total Cost of Ownership (TCO) for high-scale AI services. Enterprises running latency-sensitive applications—such as real-time translation or autonomous agents—should evaluate integrating DFlash into their production stacks. Furthermore, infrastructure providers should take note of how persistent kernel optimizations are becoming a mandatory layer for competitive LLM serving.