Self-Hosted Serving Selection — Matching Engine to Hardware and Scale
Engine selection is a function of hardware, scale, and ecosystem — not a leaderboard read. Four engines dominate self-hosted inference in 2026: llama.cpp, Ollama, vLLM, SGLang, with TGI trailing in maintenance mode. llama.cpp is fastest on CPU — widest model support, full control over quantization and threading. Ollama is the dev-laptop one-command install, 15-30% slower than llama.cpp (Go + CGo + HTTP serialization), 3x throughput gap under prod-like load. TGI entered maintenance mode December 11, 2025 — only bug fixes, 10% slower raw throughput than vLLM but historically top observability and HF-ecosystem integration. That maintenance status makes it a risky long-term bet — SGLang or vLLM are safer defaults for new projects. vLLM is the general-purpose production default — v0.15.1 (February 2026) adds PyTorch 2.10, RTX Blackwell SM120, H200 optimization. SGLang is the agentic multi-turn / prefix-heavy specialist — 400,000+ GPUs in production (xAI, LinkedIn, Cursor, Oracle, GCP, Azure, AWS). Hardware constraints: CPU-first → llama.cpp. AMD / non-NVIDIA → vLLM is the strongest-supported path (TRT-LLM is NVIDIA-locked). 2026 pipeline pattern: dev = Ollama, staging = llama.cpp, prod = vLLM or SGLang. The engines take different weight formats — GGUF for the llama.cpp family, HF safetensors for the GPU engines — so a format conversion may sit between stages. Pick an engine given hardware (CPU / AMD / NVIDIA Hopper /…
Self-Hosted Serving Selection — Matching Engine to Hardware and Scale: Engine selection is a function of hardware, scale, and ecosystem — not a leaderboard…
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