94% GPU reduction claim needs verifiable benchmarks to stand out.
ML engineers running LLM inference at scale
vLLM · LiteLLM · RouterLLM
Semantic caching for LLM APIs exists (Anthropic prompt caching, Langchain, Miniplex, vLLM); gateway routing is table stakes.
Routes LLM requests to GPUs with cached KV prefixes, skipping redundant prefill computation.
Zero-trust networking via zrok beats LiteLLM when your GPUs sit behind NAT.
Context routing cuts 73% of tokens while staying 9/10 accurate on role match.
Read-only GPU waste scanner finds 20-40% cluster spend waste without agents or sidecars.
One-command benchmark suite comparing Ollama and XGBoost performance with a shared Streamlit dashboard.
