This article explains LLM inference optimization techniques for faster, cheaper production deployments. It covers the two-phase inference process (prefill and decode), memory management strategies like KV caching and PagedAttention, and methods such as model compression and speculative decoding to reduce cost and improve throughput.
Von is an open-source, non-autoregressive system for fast decision-making that evaluates discrete criteria in sub-25ms without token-by-token text generation. It achieves 91.23% accuracy on multi-hop reasoning benchmarks and 71.5% macro accuracy on expanded task suites, with calibrated probabilistic outputs and native support for NVIDIA CUDA, AMD ROCm, and Apple Silicon.
A robotics startup's vision-guided robot lost track of moving targets due to Python's GIL preventing true parallelism and causing unpredictable timing in the control loop. The system was rewritten in Rust, which provides true parallelism, memory safety without garbage collection, and compiled performance, resulting in the robot successfully tracking targets and cutting end-to-end latency roughly in half.