Run LLMs on your own infrastructure — vLLM, TGI, Ollama, and hardware planning for inference workloads.
Five passes over the same idea, each from a different angle. Do them in order, or jump to whichever you need.
Self-hosting gives you full control over data, latency, and cost. Inference servers (vLLM, TGI, llama.cpp) handle batching, KV-cache management, and quantized model loading. Key decisions: GPU selection (A100/H100/L40S), quantization level (FP16/INT8/INT4), and whether to use container orchestration (K8s + GPU operators).
