One API to Run LLMs, Speech, and Multimodal Models Anywhere

What it does

Xinference is a model serving layer that exposes language, speech, and multimodal models through a unified, OpenAI-compatible RESTful API. You point existing clients at it and swap models without touching your application code. Under the hood it handles hardware scheduling, engine selection, and distributed workers.

The interesting bit

Most serving stacks pick a lane—text-only, GPU-only, or single-node. Xinference is aggressively pluralistic: it bundles vLLM, GGML, and TensorRT, runs on GPUs, CPUs, and Metal, and scales from a laptop to a multi-node cluster. The README maintains a self-reported comparison table showing competitors like FastChat and OpenLLM missing most of these combinations.

Key highlights

• OpenAI-compatible API including function calling, so existing clients drop in with a URL change.
• Supports heterogeneous hardware (GPU, CPU, Metal) and multiple inference engines (vLLM, GGML, TensorRT).
• Distributed inference across workers, with auto-batching and shared KV cache for throughput.
• Built-in model zoo covering recent releases (DeepSeek V4, GLM-5.1, Qwen3.6, Gemma-4, etc.).
• Integrations with agent frameworks (Xagent) and LLMOps platforms (Dify, FastGPT, RAGFlow, MaxKB).

Verdict

Worth a look if you need OpenAI API semantics but want to run open-source, multimodal, or speech models on your own hardware. Skip it if you’re already satisfied with a single-engine, single-node stack like raw vLLM.