latency reduction

Bash-first Linux guide to turbocharge AI inference: cut latency, boost throughput, and lower costs with optimized runtimes (ONNX Runtime, OpenVINO), lower precision (INT8/FP16), correct threading and NUMA pinning, smart micro-batching, and graph/kernel fusions. Includes step-by-step setup, Python/Bash snippets, profiling with perf/numa tools, advanced toggles (CPU governor, huge pages), and a practical ResNet50 lab.

A practical guide to glue Bash, curl/jq, and a small LLM to forecast hot assets from logs, pre‑warm your CDN, and auto‑tune cache TTLs—cutting TTFB and origin load without a full ML stack. It includes install steps, scripts for log sampling, OpenAI/Ollama inference, parallel pre‑warming, NGINX TTL map updates, cron automation, plus guardrails for safety, costs, and measurement.

Practical, bash-first guide to harden Redis for AI workloads: install and baseline, benchmark with redis-benchmark, choose persistence (ephemeral cache vs durable AOF/Streams), set maxmemory/eviction/TTLs, enable defrag, tune Linux and network (somaxconn, THP, FDs), use pipelining/ pooling, and model patterns (thundering-herd-safe inference cache, Redis Stack vectors) to cut tail latency and cloud spend.