7 Best Self-Hosted Inference Servers for Open-Source Models, Compared (2026)
7 self-hosted inference servers compared: vLLM, SGLang, Ollama, TEI, LocalAI, Dynamo-Triton & SIE. Choose the right one based on your workload, not the brand.
When it comes to self-hosting open-source models for inference, there are many servers to choose from. The decision should not be based on brand, but rather on the shape of your workload. Single-model servers are optimized for one heavy generative model, while multi-model servers can handle a fleet of smaller task models more efficiently.
There are several factors to consider when selecting an inference server. Self-hosting can lead to cost savings compared to hosted APIs, ensures data privacy and residency, and offers flexibility in serving various model types. However, self-hosting also moves costs from token counters to operational time, so the goal is to minimize operational overhead.
When it comes to the actual servers, options include Hugging Face TEI for single-model serving of embeddings and rerankers, LocalAI for multi-model serving of various model types, NVIDIA Dynamo-Triton for serving heterogeneous models at scale on NVIDIA GPUs, Ollama for local LLM development and prototyping, SGLang for serving a single large generative model, and vLLM for serving one large generative model with maximum throughput.
Ultimately, the best choice depends on your specific needs, such as the number of models, hardware available, and whether you prioritize simplicity or maximum performance.
Written by urgent.news from HackerNoon's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
