Comparisons that start from vLLM, the Apache-2.0 high-throughput LLM serving engine, against other GPU inference servers.
When vLLM should serve a model from one GPU, from several GPUs in one node with tensor parallelism, or across nodes with tensor plus pipeline parallelism. Covers the vLLM docs' rule of thumb for choosing, the Ray runtime for multi-node runs, fast interconnect needs, and the unencrypted node-to-node traffic that multi-node serving brings.
The two leading open-source GPU serving engines compared: vLLM and SGLang from LMSYS. Covers caching and parallelism approaches, OpenAI-compatible APIs, hardware support across NVIDIA, AMD, TPU and other accelerators, release pace and what changes for the team operating them.
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