How We Cut Local LLM Inference Latency by 82% and Reduced GPU Costs by 60% Using Adaptive Batching and Speculative Fallback
Current Situation Analysis Deploying open-weight LLMs locally sounds straightforward until you hit production load. The official documentation for tools like vLLM 0.7.0, Ollama 0.5.8, or llama.cpp (b4343) assumes a single-user, synchronous workflow.
