vllm and inference
These are competitors—both provide unified inference serving engines for running multiple types of LLMs, but vLLM focuses on high-throughput optimization for a single inference backend, while Xinference abstracts across multiple heterogeneous model types and deployment environments.
About vllm
vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
Implements PagedAttention for efficient KV cache management and continuous request batching to maximize GPU utilization. Supports multiple quantization schemes (GPTQ, AWQ, INT4/8, FP8), speculative decoding, and tensor/pipeline parallelism across NVIDIA, AMD, Intel, and TPU hardware. Provides OpenAI-compatible API endpoints and integrates directly with Hugging Face models, including multi-modal and mixture-of-expert architectures.
About inference
xorbitsai/inference
Swap GPT for any LLM by changing a single line of code. Xinference lets you run open-source, speech, and multimodal models on cloud, on-prem, or your laptop — all through one unified, production-ready inference API.
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