High Performance LLM Inference in Production

High-performance LLM inference in production requires tailoring infrastructure to specific workload demands, categorized into online, offline, and semi-online patterns. Online applications, such as chat interfaces, prioritize low latency and interactivity, while offline jobs focus on throughput for large-scale batch processing. Semi-online workloads necessitate flexible auto-scaling to handle variable traffic. Key optimization strategies include utilizing speculative decoding to amortize weight-loading efforts and implementing GPU snapshotting to drastically reduce cold-start latency. Monitoring GPU power usage serves as a reliable diagnostic tool for identifying communication bottlenecks and ensuring efficient hardware utilization. The introduction of a free multi-tenant GLM-5 endpoint on Modal provides developers with a platform to test these agentic coding setups and explore frontier intelligence at scale.