AWS shows how to train across eight GPUs on EKS without building the container stack

AWS has published a working example for distributed PyTorch training with Ray Train on Amazon EKS, using a pre-built container that bundles the software and networking stack. For teams scaling training across machines, the useful change is less container wrangling and a clearer path to getting the GPUs talking to one another.

AWS AI Watch analysis

What happened

The example uses AWS’s Ray Train Deep Learning Container with KubeRay on EKS to fine-tune Qwen2.5-1.5B across eight NVIDIA L4 GPUs on two g6.12xlarge instances. AWS says the image includes the compatible Ray, PyTorch, CUDA, NCCL and Elastic Fabric Adapter components, so the sample runs without building a custom image.

The AWS walkthrough gives the cluster setup and training code. It estimates a running setup at about US$9.50 an hour, including the GPU instances, EKS control plane and NAT Gateway. That is a cost estimate for the documented configuration, not a general price for distributed training.

Why it matters

Distributed training can be slowed by the unglamorous work of matching software versions and configuring fast communication between machines. AWS’s example packages that stack and shows how Ray Train assigns workers across the GPUs, while EFA carries traffic between the two instances. That makes the walkthrough useful to teams evaluating a multi-node setup, even if its specific AWS hardware and cluster prerequisites will not suit everyone.

Our read

This is practical infrastructure guidance rather than a new training breakthrough. The best bit is that the example makes the moving parts, prerequisites and running cost visible instead of treating a working cluster as a small matter of copy-paste. Teams should check regional GPU capacity and the budget before trying it; eight GPUs are not famous for being shy about the bill.

What to watch

• Whether AWS updates the container and sample as its supported Ray and PyTorch versions change.
• Whether the example is extended to other cluster sizes, instance types or training workloads.
• How actual performance and cost compare with teams’ existing multi-node setups.

Discussion spark: Does a pre-built, tested training container make managed cloud infrastructure more appealing, or would you rather own the stack and its version choices?

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