HyperPod model caching cuts LLM cold starts from 30 minutes to seconds
AWS ML Blog · rss · 2026-09-11
Amazon SageMaker HyperPod launched model caching: pre-loading model weights and container images onto node-local NVMe (7 GB/s) so pods start serving in seconds instead of waiting 30+ minutes of downloads. Cold starts were dominated by two sequential downloads: multi-GB inference images (5–7 min) plus weights (20+ min for a 145GB model, 30+ min for DeepSeek-R1 at 600+GB), repeated on every scale-out.
Two independently toggleable caches:
- Weights cache: the operator pre-downloads weights from S3/FSx/HF Hub to node NVMe, labels nodes cache-ready before creating the deployment, and the cache persists across pod restarts.
- Image cache: a DaemonSet pre-pulls the image, skipping ECR downloads; multiple deployments share one cache.
Scheduling is preferred, not required: pods favor cached nodes but fall back to normal downloads without failure. Enable via modelCacheConfig (weightsCache/imageCache); model updates roll out a new cache and clean the old one with zero downtime.
More from Infra
- Meta approves nonvoting Class C shares as 2026 capex guidance hits $135B–$145B — thione · 2026-10-26
- Engineer with thousands of fabricated devices pours cold water on photonic crystal modulator yields — jwt0625 · 2026-10-26
- Anthropic S-1 reveals 48 cents of compute cost per dollar of Q2 revenue — nmasc_ · 2026-10-26
- Persistent AI joins UR network to route private AI inference with encrypted, IP-hidden traffic — 0xSammy · 2026-10-26
- Anthropic's disclosed compute commitments: $114.5B Google, $161.2B Broadcom top the list — BenBajarin · 2026-10-26
- SpaceX strikes $40B deal with Apollo Global to lease AI chips and data center hardware — Polymarket · 2026-10-26