Serving Stacks

vLLM isn't the only way to serve an LLM, and this cluster covers the rest of the field: SGLang's RadixAttention, TensorRT-LLM's engine-build workflow, NVIDIA Dynamo's disaggregated prefill-decode architecture, Triton Inference Server, Ray Serve, and half a dozen others including LMDeploy, Aphrodite Engine, Modular's MAX, and Hugging Face TGI, now in maintenance mode, with a migration guide to prove it.

Most posts follow the same shape: a working deployment on a specific GPU, then a benchmark against at least one competing stack, because the honest answer to "which serving engine is fastest" changes by model size, batch size, and hardware generation. We also cover the orchestration layer above the serving engine itself: KServe versus Seldon Core versus BentoML for teams running Kubernetes-native ML serving.

The pillar post, vLLM vs TensorRT-LLM vs SGLang, is the direct three-way benchmark: same H100, same model, real throughput and latency numbers so you can see the tradeoffs instead of taking a vendor's word for it. Use this cluster when vLLM's defaults aren't fast enough for your workload and you need to know what else is worth trying.