# Amazon SageMaker HyperPod

Published articles for Amazon SageMaker HyperPod.

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## Reduce inference cold starts on Amazon SageMaker HyperPod with model caching

DevFeed: [Reduce inference cold starts on Amazon SageMaker HyperPod with model caching](<https://devfeed.tech/articles/reduce-inference-cold-starts-on-amazon-sagemaker-hyperpod-with-model-caching-4739.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/reduce-inference-cold-starts-on-amazon-sagemaker-hyperpod-with-model-caching/>)

Author: Kareem Syed-Mohammed

Published: 2026-09-10T21:37:49Z

Content type: tutorial

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Caching](<https://devfeed.tech/topics/caching.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [autoscaling](<https://devfeed.tech/topics/autoscaling.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>)

Tags: [amazon](<https://devfeed.tech/tags/amazon.md>), [amazon-s3](<https://devfeed.tech/tags/amazon-s3.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-hyperpod](<https://devfeed.tech/tags/amazon-sagemaker-hyperpod.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [autoscaling](<https://devfeed.tech/tags/autoscaling.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [caching](<https://devfeed.tech/tags/caching.md>), [expert-400](<https://devfeed.tech/tags/expert-400.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [llm](<https://devfeed.tech/tags/llm.md>)

### AI overview

Amazon SageMaker HyperPod model caching preloads model weights and container images onto cluster nodes, reducing inference-pod cold starts by serving assets from local NVMe storage.

### Source excerpt

Amazon SageMaker HyperPod now supports model caching for inference, which pre-loads model weights and container images onto cluster nodes so pods read from local NVMe storage instead of downloading over the network. Learn how model caching cuts cold starts from tens of minutes to seconds, how it works, and how to enable it.

## Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM

DevFeed: [Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM](<https://devfeed.tech/articles/deploying-qwen3-8-2-4t-a95b-on-amazon-sagemaker-hyperpod-with-vllm-4731.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/deploying-qwen3-8-2-4t-a95b-on-amazon-sagemaker-hyperpod-with-vllm/>)

Author: Dmitry Soldatkin

Published: 2026-09-09T22:26:29Z

Content type: tutorial

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Deployment](<https://devfeed.tech/topics/deployment.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>)

Tags: [advanced-300](<https://devfeed.tech/tags/advanced-300.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-hyperpod](<https://devfeed.tech/tags/amazon-sagemaker-hyperpod.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [inference](<https://devfeed.tech/tags/inference.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [moe](<https://devfeed.tech/tags/moe.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [technical-how-to](<https://devfeed.tech/tags/technical-how-to.md>), [tool](<https://devfeed.tech/tags/tool.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

A deployment walkthrough for serving the open-weight Qwen3.8-2.4T-A95B language model on Amazon SageMaker HyperPod with vLLM and NVIDIA B300 GPUs. It covers provisioning, NVFP4 quantization, an OpenAI-compatible endpoint, reasoning, tool calling, and MTP speculative decoding.

### Source excerpt

Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding.

## Pathway's brain-inspired architecture development on Amazon SageMaker HyperPod

DevFeed: [Pathway's brain-inspired architecture development on Amazon SageMaker HyperPod](<https://devfeed.tech/articles/pathway-s-brain-inspired-architecture-development-on-amazon-sagemaker-hyperpod-4738.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/pathway-s-brain-inspired-architecture-development-on-amazon-sagemaker-hyperpod/>)

Author: Paulo Aragão

Published: 2026-09-08T19:12:51Z

Content type: article

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [compression and generalization](<https://devfeed.tech/topics/compression-and-generalization.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>)

Tags: [amazon](<https://devfeed.tech/tags/amazon.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-hyperpod](<https://devfeed.tech/tags/amazon-sagemaker-hyperpod.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [intermediate-200](<https://devfeed.tech/tags/intermediate-200.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>)

### AI overview

Pathway describes BDH, a brain-inspired architecture that performs reasoning in latent space rather than producing chain-of-thought token traces. The article covers its recurrent internal memory, its contrast with transformer limitations, and scaling training with Amazon SageMaker HyperPod.

### Source excerpt

Pathway's Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. See how Pathway develops and scales BDH on Amazon SageMaker HyperPod, and how BDH-CQ set a new cost-efficiency mark on the ARC-AGI-1 benchmark.