TABLE OF CONTENTS
NVIDIA H100 SXM On-Demand
Welcome to Hyperstack Weekly Rundown
Your weekly digest of the latest updates, tutorials and improvements from Hyperstack. This week features a Kubernetes version update, deployment guides for DeepSeek V4 and Kimi K2.6, and a chance to shape what we build next.
Take a few minutes to catch up on everything.
New on Hyperstack
Check out what's new on Hyperstack this week:
Supported Kubernetes Versions Updated
Kubernetes 1.35.1 is now live and set as the new default for all cluster deployments. Here's what you need to know:
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Kubernetes 1.35.1 is the new default; all new clusters will be created on this version automatically.
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Kubernetes 1.27.8 has reached end-of-life and is no longer supported for new cluster deployments.
Already running 1.27.8? No action needed. Your existing clusters are unaffected and continue to support adding and removing nodes and node groups. Learn how to upgrade your cluster.
New on our Blog
Check out the latest tutorials on new AI models on Hyperstack:
Deploy DeepSeek-V4 on Hyperstack:
A Step-by-Step Guide
DeepSeek-V4 is DeepSeek AI’s latest open-weight LLM family, optimised for efficiency and long-context reasoning. The lineup spans 284B–1.6T parameters with only 13B active per step, a native 1M-token context window, and FP4+FP8 mixed precision for efficient single-node deployment. This tutorial shows how to deploy DeepSeek-V4-Flash on Hyperstack using vLLM and Docker with an OpenAI-compatible API.
Deploy DeepSeek-V4 Pro on Hyperstack:
A Step-by-Step Guide
DeepSeek-V4 Pro is a 1.6 trillion parameter sparse MoE with 49 billion active per token, a 1M token context window, and an efficient hybrid attention stack. It delivers strong coding benchmark performance but exceeds single-node limits, making it ideal for multi-node deployment on 8x H100. This tutorial covers running it on Hyperstack using vLLM and Docker.
Deploy Kimi K2.6 on Hyperstack:
A Step-by-Step Guide
Kimi K2.6 is an open-weight, native multimodal agentic model from Moonshot AI, built for advanced coding and autonomous agent workflows. Using a sparse Mixture-of-Experts architecture with 1T parameters and only 32B active per step, it matches or exceeds leading closed models on coding and agentic benchmarks while remaining practical to self-host. This tutorial covers deploying Kimi K2.6 on Hyperstack using a GPU VM, vLLM and an OpenAI-compatible API.
Help Shape the Future of Hyperstack
Great products are built with the people who use them. If there’s something you would like to see on Hyperstack whether it is a new feature, workflow improvement or integration that would make your work easier, we would love to hear about it.
Your feedback helps us prioritise what matters most and build a platform that works better for the community.
That's it for this week's Hyperstack Rundown! Stay tuned for more updates next week and subscribe to our newsletter below for exclusive AI and GPU insights delivered to your inbox!
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