{"version":1,"type":"story","url":"https://digestai.news/story/alibaba-releases-openweight-qwen3-82-4ta95b-deploys-on-sagemaker-hyperpod","json":"https://digestai.news/story/alibaba-releases-openweight-qwen3-82-4ta95b-deploys-on-sagemaker-hyperpod.json","markdown":"https://digestai.news/story/alibaba-releases-openweight-qwen3-82-4ta95b-deploys-on-sagemaker-hyperpod.md","slug":"alibaba-releases-openweight-qwen3-82-4ta95b-deploys-on-sagemaker-hyperpod","headline":"Alibaba Releases Open‑Weight Qwen3.8‑2.4T‑A95B; Deploys on SageMaker HyperPod","summary":"Alibaba’s Qwen team unveiled the Qwen3.8‑2.4T‑A95B model on August 12, 2026, marking the first open‑weight release of a Qwen‑Max‑class model. The 2.4 trillion‑parameter network activates 95 billion parameters per token, supports a native context window of 262 k tokens (expandable to 1 M), and is engineered for demanding agentic and reasoning tasks such as multi‑step coding, long‑horizon planning, and autonomous tool use.\n\nThe post details how to run the model on Amazon SageMaker HyperPod using vLLM on an ml.p6‑b300 instance (8× NVIDIA B300 Blackwell Ultra GPUs). It covers cluster provisioning, vLLM configuration (NVFP4 quantization, Multi‑Token Prediction, built‑in reasoning controls), and the HyperPod inference operator that manages model download, scheduling, autoscaling, and health monitoring.\n\nWith open weights, users gain full control over data residency and inference behavior, eliminating per‑token API fees. The deployment guide demonstrates that a single 8‑GPU node can host the compressed 1.2 TB model, making frontier‑scale inference feasible for enterprises that prefer self‑hosted solutions.","keyPoints":["Qwen3.8‑2.4T‑A95B is a 2.4 trillion‑parameter open‑weight LLM with 262 k‑token context","Deployment uses SageMaker HyperPod’s ml.p6‑b300 (8× B300 GPUs) with vLLM and NVFP4 quantization","Model supports built‑in reasoning, tool calling, and Multi‑Token Prediction for efficient agentic workloads"],"whyItMatters":"Open‑weight release of a 2.4 trillion‑parameter LLM gives enterprises control over data and cost, enabling large‑scale, self‑hosted inference for complex agentic applications.","category":{"slug":"models","name":"Generative AI & Models","url":"https://digestai.news/category/models"},"entities":{"companies":["Alibaba","Amazon"],"models":["Qwen3.8-2.4T-A95B"],"people":[]},"firstPublishedAt":"2026-09-09T22:26:29Z","updatedAt":"2026-09-09T22:26:29Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"AWS Machine Learning Blog","title":"Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM","url":"https://aws.amazon.com/blogs/machine-learning/deploying-qwen3-8-2-4t-a95b-on-amazon-sagemaker-hyperpod-with-vllm","publishedAt":"2026-09-09T22:26:29Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":{"title":"AWS Optimizes Enterprise AI Infrastructure","url":"https://digestai.news/thread/amazon-sagemaker-hyperpod-introduces-model-caching-to-cut-cold-starts","storyCount":10},"cite":{"text":"Digest AI, \"Alibaba Releases Open‑Weight Qwen3.8‑2.4T‑A95B; Deploys on SageMaker HyperPod\", 9 September 2026, https://digestai.news/story/alibaba-releases-openweight-qwen3-82-4ta95b-deploys-on-sagemaker-hyperpod","publisher":"Digest AI","title":"Alibaba Releases Open‑Weight Qwen3.8‑2.4T‑A95B; Deploys on SageMaker HyperPod","datePublished":"2026-09-09T22:26:29Z","url":"https://digestai.news/story/alibaba-releases-openweight-qwen3-82-4ta95b-deploys-on-sagemaker-hyperpod"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}