Project
AWS SageMaker service providing managed, resilient compute clusters for large-scale machine learning training and inference deployment.
Reflection AI's Beam coding model computes 23B parameters per token but keeps all 501B in memory, according to AWS and Hugging Face. Self-hosting it starts from a roughly 1 TB BF16 checkpoint and multi-GPU sharding.
Reality
AWS now lets SageMaker Studio users launch JupyterLab and Code Editor Spaces on HyperPod EKS clusters from the browser, with no kubectl step. On a shared GPU cluster, who may launch them comes down to three managed IAM policies and a per-user identity setting that older Studio domains must switch on.
Reality
AWS has generalised the orchestration for multi-turn reinforcement fine-tuning. The part that decides what the model learns is still code somebody on your team has to write and audit.
Reality
Reading a 600 GB checkpoint from local NVMe at 7 GB/s takes about 86 seconds, so the network stops gating scale-out on warm nodes. The first fill still costs the full download. The slowest node sets it.
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AWS counted 13 SageMaker inference launches so far in 2026. The one that changes production behaviour most is a prioritized list of up to five instance types, each allowed its own model optimization settings.
Reality
Amazon's new EKS managed addon reads Prometheus metrics from every model pod and sends each request to the one with room in its KV cache. The advertised 82% cut in first-token latency rests on a single 4.4-second baseline.
Reality
Alibaba's largest open-weight release fits on a single eight-GPU node only because a community four-bit build takes it down to 1.2 TB. AWS publishes the vLLM config for it and skips the price.
Reality
The launch takes four manual steps off teams running Ray on EKS and puts four components on the install list. What travels if you leave is the part AWS does not price.
Reality