Best Inference Servers for Running Open-Source Models in Production
Meta Description: Compare vLLM, SGLang, TensorRT-LLM, LMDeploy, and llama.cpp to choose the right inference server for production LLMs and AI agents.
Choosing the right inference server for running open-source models in production is crucial for performance. OpenAI-compatible API, structured output, prefix caching, metrics, and multi-LoRA capabilities are important features to consider. Hardware compatibility, such as NVIDIA, AMD, Intel, CPUs, and TPU, should also be taken into account.
Prominent open-source inference servers include vLLM, SGLang, TensorRT-LLM, LMDeploy, and llama.cpp server. Each has its strengths and weaknesses, depending on specific use cases and hardware resources. For instance, vLLM is suitable for one large generative model at high concurrency on nearly any hardware, while SGLang excels in agent loops and Retrieval-Augmented Generation (RAG) with repeated context.
TensorRT-LLM is ideal for teams fully on NVIDIA, while LMDeploy is best for serving quantized models on fewer or smaller GPUs. llama.cpp server is suitable for edge devices, on-premises CPU servers, and air-gapped machines. For small models surrounding the agent, TEI and SIE are two options to consider.
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