Hardware
Run multi-node inference workloads using Ray LLM Deep Learning Containers
Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Containers, with separate prefill and decode nodes behind one OpenAI-compatib
Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Containers, with separate prefill and decode nodes behind one OpenAI-compatible endpoint. The pre-built container ships the full inference stack, so you configure the deployment instead of assembling it.
Related
- Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers
- Systematic Exploration of Multi-core Architectures for Efficient LLM Serving using WaferAI-SIM
- Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads
Source: AWS ML Blog | 2026-10-12