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.
TensorRT-LLM is NVIDIA's open-source LLM inference library (Apache 2.0). It offline-compiles model weights and compute graphs into optimized TensorRT engines, then serves them with custom CUDA kernels, in-flight batching, and multi-dimensional parallelism. The cost: NVIDIA GPUs only, compilation takes tens of minutes, and switching models or quantization means rebuilding.
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.
OpenRouter exposes many models and inference endpoints through an OpenAI-compatible API, with provider ordering, failover, BYOK, and zero-data-retention controls in one routing policy.
Sail Research lets each inference request declare a completion window, scheduling patient background agents on cheaper capacity, while Sailboxes provide persistent long-running execution environments.
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.
llama.cpp is the most widely used local LLM inference engine, implemented in pure C/C++. It supports CPU, Metal, CUDA, Vulkan, and other backends, and uses the GGUF quantization format to run multi-billion-parameter models on consumer hardware.
TurboQuant+ is an open-source implementation of a Google Research ICLR 2026 paper that uses PolarQuant + QJL two-stage quantization to compress the KV cache by 3.8-6.4x, enabling consumer hardware to run larger models with longer contexts.
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.