Two Leaps to 1000 Tokens/s on a 1T-Parameter Model
5 hours ago
- #Hardware-Software Co-Design
- #Ultra-Low Latency
- #LLM Inference Optimization
- The optimization of LLM inference systems has historically focused on kernels, operators, and scheduling to maximize hardware utilization.
- The shift to ultra-low latency applications has exposed previously hidden execution overheads, making microsecond-level optimization critical.
- TileRT introduces a Persistent Engine that consolidates the computational pipeline to eliminate execution gaps caused by operator boundaries.
- Persistent execution enables continuous prefetching and tile-level pipelining, improving data movement overlap within the hardware.
- Warp Specialization and Heterogeneous Workers transform the GPU into a coordinated, heterogeneous execution system.
- Breaking the 1000 TPS barrier requires hardware-software co-design to address microsecond-scale bottlenecks like auxiliary operations.
- Collaboration with Xiaomi's MiMo team led to optimizations such as mixed-precision quantization and DFlash integration to reduce latency.
- Speed is emerging as a new scaling law, where inference speed directly influences model capabilities and application viability.
- Achieving 1000+ TPS represents a paradigm shift towards integrated evolution of models and systems in ultra-low latency regimes.