TABLE OF CONTENTS
NVIDIA H100 SXM On-Demand
Happy New Year and welcome to 2026 🎉
Before we start, we want to thank you for being a valuable part of the Hyperstack community.
Back in December, we made a promise to bring you Object Storage along with meaningful platform enhancements designed to make your workflows smoother and more scalable. We delivered on that promise before the year wrapped up.
And this is just the beginning. More improvements, performance upgrades and new capabilities are already in motion and there’s a lot more coming your way this year.
Here’s a quick look at everything that landed in December.
New on Hyperstack
Check out what's new on the Hyperstack this month:
Object Storage
Hyperstack Object Storage is now live and fully S3-compatible, giving you a smarter way to store and scale unstructured data. If you’ve been relying on SSV, this is the upgrade you’ve been waiting for.
Built for AI/ML datasets, logs, backups and media, our Object Storage is secure, cost-efficient and designed to scale effortlessly. Please note that it is only available in the CANADA-1 region.
Latest GPU Deployment
Check out what's new on our hardware side this month:
NVIDIA RTX Pro 6000 SE
New deployments for the NVIDIA RTX 6000 will be going live SOON.
If you’re running steady production workloads, this is your chance to get an instant performance uplift with zero changes to your setup. And if you haven’t already reserved, now is the moment to act.
Securing access early ensures you’re ready the moment it goes live. Reservations start at just $1.26/hr.
Fixes and Improvements
- Callback URL validation: Callback URLs must now resolve to a public IP address. This prevents the use of internal or non-routable addresses for improved security.
- API key naming rules: API key names no longer allow special characters, reducing the risk of misuse and strengthening security controls.
- Improved network bootstrapping: VM deployments are now more reliable in new environments. The first VM handles initial network setup, preventing conflicts when multiple VMs are launched simultaneously.
New on the Blog
Check out exciting blogs on Hyperstack this month:
CUDA Cores vs Tensor Cores: What’s the Difference?
What’s the Difference?
Ever wondered why some GPUs train AI models faster than others? Tensor and CUDA Cores in NVIDIA GPUs directly impact training speed, inference efficiency and how effectively you can leverage mixed-precision formats like FP16, BF16, FP8 or INT8. In this blog, we break down the difference between CUDA Cores vs Tensor Cores, explain how each works and show how developers can choose the right GPU for their workloads.
Learn more in our latest blog.
What is TensorFlow:
Features, Use Cases and More
TensorFlow shows up everywhere, from the models powering image recognition to the systems behind real-time language understanding. But what actually makes TensorFlow so popularly used in AI and ML today? If you’re building, training or scaling models, you’ve likely come across it or you’re about to. TensorFlow is designed to help you turn data into working intelligence, faster and at scale.
Learn more in our latest blog.
What is Container Deployment:
How Does it Work?
If you’ve ever shipped an application to production and thought, “Why does it work on my machine but break everywhere else?”, congratulations, you’ve met the exact problem containers were built to solve. Today, almost every modern engineering team rely on speed, consistency and portability. Whether you're building AI pipelines, microservices or full cloud-native systems, containers are the technology powering your favourite apps and platforms.
Learn more in our latest blog.
What is PyTorch:
All You Need to Know
If you’ve tried learning deep learning, you’ve likely felt the mix of excitement and frustration, powerful models wrapped in complex frameworks, cryptic errors and code that feels heavier than it should. You start with a simple idea, then end up buried in abstractions, long debugging sessions and tools that don’t feel intuitive. You need a framework that lets you experiment freely, understand what your model is doing and move fast without losing performance or scalability. That’s why many developers choose PyTorch: it makes deep learning feel natural and flexible for Python users and helps you build, debug and deploy models with clarity.
Learn more in our latest blog.
Your Ideas Power Hyperstack
You know your workflow better than anyone. If there’s anything you wish Hyperstack did differently or better, now’s your chance to tell us.
Maybe it’s a feature you’ve been thinking about, a tool that could speed up your workflow, or a simple improvement that would make your project easier. Whatever it is, we’re listening.
For any questions or suggestions, feel free to reach out at support@hyperstack.cloud. Stay tuned for even more updates and exciting tools next month.
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