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AWS Demonstrates Two-Node LLM Inference on Amazon EKS Using Ray LLM Containers

2 reports 2 reporting sources 5 days agoNew

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AWS published a walkthrough for deploying Qwen3.5-9B across two GPU nodes on Amazon EKS using Ray LLM Deep Learning Containers, with separate prefill and decode deployments serving requests through a single OpenAI-compatible endpoint. The example configures the application in YAML and uses NVIDIA Inference Xfer Library (NIXL) over Elastic Fabric Adapter (EFA) to transfer the model’s KV cache between nodes. AWS covers provisioning the cluster, invoking the model, verifying the transport between nodes, and cleaning up the resources.

Generated by AI from reports · Updated 5 days ago

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10/17
  1. Six projects from the last few weeks that caught our attention: A wildfire analysis prototype that maps the fire’s spread over time and tracks hot spots, made with Opus 5.5.

    Six projects from the last few weeks that caught our attention: A wildfire analysis prototype that maps the fire’s spread over time and tracks hot spots, made with Opus 5.5.

10/12
  1. Run multi-node inference workloads using Ray LLM Deep Learning Containers

    AWS shows how to deploy Qwen3.5-9B across two GPU nodes on Amazon EKS using Ray LLM Deep Learning Containers, with separate prefill and decode deployments behind one OpenAI-compatible endpoint. The walkthrough configures the application in YAML and uses NIXL over EFA to transfer the KV cache between nodes. It covers cluster provisioning, model invocation, transport verification, and resource cleanup.

Event heat trend

可比范围当前
3
Peak within comparable coverage
1010/13 01:00 (UTC+8)
近 24 小时变化
-49%

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