Open-source models now match closed-source on coding benchmarks, but self-hosting isn't just picking a model — vLLM handles high-concurrency production serving, SGLang is 29% faster on prefix-heavy workloads, Ollama is the local dev default, and llama.cpp runs on the least hardware. A100 cloud rentals run ~$1.4-2.2/hr; self-hosting breaks even at roughly 100M tokens/month.
The key question in self-hosted inference isn't how fast the engine is — it's your GPU utilization. A fully saturated A100 costs ~$0.70 per million output tokens; at 10% utilization that becomes $7, more than most cloud APIs. This overview maps seven tools across three layers to help you decide which layer you need.
Self-hosted inference servers fall into three layers: execution engine (llama.cpp), serving engine (vLLM, SGLang), and model management platform (Ollama, Xinference, Triton). Picking the right layer matters more than picking the right tool — ask where your bottleneck is before deciding where to add complexity.
Xinference wraps vLLM, SGLang, llama.cpp, Transformers, and MLX under a single management layer, using a Web UI and OpenAI-compatible API to manage LLMs, embedding, rerank, speech, and image models — suited for self-hosted deployments that need multiple model types to coexist. But the management layer's parsing logic also creates a larger attack surface than pure serving engines (CVE-2026-61539 is a case study).
Lecture 10 separates prefill from decode: prefill parallelizes and is often compute-bound, while decode is sequential and commonly bandwidth-bound. GQA/MLA, quantization, speculative decoding, continuous batching, and PagedAttention reshape that cost.
vLLM is the de facto standard for self-hosted LLM inference (89,470 GitHub stars, verified 2026-08-21), built on managing the KV cache the way an OS manages paged memory. But the selection question isn't how fast it is — it's your GPU utilization. Using Red Hat's measured 793 output tokens/second, a fully saturated A100 costs roughly $0.70 per million output tokens; at 10% utilization that becomes $7, more than most cloud APIs.
2026 Q1 saw a full-blown open-source model explosion: on the LLM front, GLM-5, Kimi K2.5, and Qwen3.5 caught up with closed-source models; Embedding and Reranker are dominated by Qwen3 and BGE; speech has Voxtral TTS and Whisper V3; image has FLUX.2; and video has Wan 2.2 rivaling Sora. This is the complete navigation map.
The official provider directory now lists 60 entries. The most common failure when attaching a local model is writing Ollama's base URL with /v1 — that breaks tool calling, and the model starts emitting raw tool-call JSON as plain text.
vLLM uses PagedAttention to eliminate KV cache memory waste, combining continuous batching and prefix caching to become the most widely adopted open-source LLM inference engine today.