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This post shows how to deploy one large language model (LLM) across two GPU nodes on Amazon Elastic Kubernetes Service (Amazon EKS) with the multi-node Ray LLM Deep Learning Containers. It uses separate prefill and decode nodes behind one OpenAI-compatible endpoint. This kind of multi-node setup is common for tensor, pipeline, and data parallelism, as well as prefill/decode disaggregation inference workloads.
TorchServe is no longer actively maintained. The official project notice states there are no planned updates, bug fixes, new features, or security patches, and that vulnerabilities might not be addressed. For teams that run model inference on TorchServe today, this means security patches stop and compatibility updates with newer versions of PyTorch and CUDA stop.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
