Best list · Updated August 2026

Best Inference Servers for Open Models

Compare vLLM, SGLang, TGI, LocalAI, LiteLLM, and BentoML for serving open models and routing inference.

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedAugust 2026SourcesOfficial docs, GitHub repositories, vendor documentation, model cards, and source links listed on this page.

AI tools, model releases, pricing, licenses, and platform terms can change quickly. Verify the official source before production or commercial use.

Who this page is for

This page is for teams moving from an interactive model demo to a repeatable API. An inference server should be selected for a specific model, accelerator, traffic pattern, and client contract. Compare latency and throughput under the same prompts, then inspect model support, batching behavior, authentication boundaries, observability, and failure recovery before treating a benchmark result as a deployment decision.

Selection criteria

  • Verified support for the exact model architecture, precision, and quantization you intend to serve.
  • Latency, throughput, and memory behavior measured at realistic context lengths and concurrency.
  • An API surface compatible with clients without assuming every optional endpoint behaves identically.
  • Operational controls for authentication, health checks, logs, metrics, limits, and graceful shutdown.
  • A deployment path your team can reproduce across drivers, containers, accelerators, and upgrades.

Top picks

  1. vLLM
  2. SGLang
  3. LiteLLM
  4. LocalAI
  5. BentoML

Grouped recommendations

Best throughput baseline

vLLM

Best to test for modern models

SGLang

Legacy Hugging Face server to audit

TGI (maintenance mode)

Best gateway layer

LiteLLM

How to choose

Benchmark serving stacks on your exact model, context length, quantization, and traffic pattern. For a new deployment, prioritize an actively developed engine: the TGI repository is archived and read-only, and its README places the project in maintenance mode while recommending vLLM or SGLang going forward.

Related links

/tools/vllm//tools/sglang//tools/tgi//compare/vllm-vs-sglang//compare/vllm-vs-tgi/

Related best-of pages

Best AI Infrastructure ToolsInfrastructure tools for GPU compute, public web data workflows, model serving, and AI app backends.Best Open-Source AI Tools in 2026A grouped shortlist of open-source AI tools for local models, RAG, coding, serving, and observability.

FAQ

How is an inference server different from a desktop local-model app?

A desktop app centers interactive loading and chat. An inference server centers a stable API, concurrent requests, scheduling, and operations. Some tools can cover both roles, but production evaluation should still test the server path and its security boundaries directly.

Which benchmark should I use to compare inference servers?

Use your own model, hardware, prompt lengths, output lengths, and concurrency. Record time to first token, generation throughput, request latency, memory use, and failures. A published throughput number from a different workload does not establish performance for yours.

Is an OpenAI-compatible API enough to make servers interchangeable?

No. Compatibility is usually endpoint- and feature-specific. Check the requests your application sends, including streaming, tool calls, structured output, embeddings, authentication, and nonstandard parameters, then run contract tests before switching servers.

Related resources

Continue comparing tools, models, stacks, and guides related to this category.

/tools/vllm//tools/sglang//tools/tgi//compare/vllm-vs-sglang//compare/vllm-vs-tgi/

Sources

vLLMSGLangText Generation Inference

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