Hyperstack - Case Studies

Inside Hyperstack: How Every Code Change Gets Scanned for Secrets

daman.preet@nexgencloud.com (Damanpreet Kaur Vohra) — Thu, 08 Oct 2026 15:00:57 GMT

The Key You Pasted In "Just to Test"

You are wiring up a new integration and want to see whether the call works before you build it properly. You paste the API key straight into the file, run the code and it works. A meeting starts, the change gets committed with the rest of your work and the key is now part of your codebase.

Regulated AI Inference for Healthcare on Secure Private Cloud

daman.preet@nexgencloud.com (Damanpreet Kaur Vohra) — Wed, 15 Jul 2026 09:50:42 GMT

The Problem

The deal was interrupted at legal review, not because the model underperformed or the price was wrong but because nobody on the vendor side could answer one question with a straight sentence: where does the patient's imaging data live and who can access it?

A European health institute, built up over the past decade to bring AI-assisted diagnostics and administrative tooling to public hospital networks across the region, builds inference products for three different buyers at once. Hospital radiology departments use its triage models to flag urgent CT and MRI scans before a radiologist opens the file, health system clinical teams use its ambient documentation and EHR summarisation tools during patient visits, and payer organisations run its claims adjudication and prior authorisation models on submitted case files, which means three buyer types and three risk profiles are sitting on one shared infrastructure layer underneath. A fourth workload runs in the background across all of it: continuous model monitoring and drift detection, watching every prediction the other three models make for signs of degradation before a regulator or a client notices first.

That shared layer was public cloud and it was starting to fail in three specific ways.

Running a Multi-Program AI Research on Secure Private Cloud

daman.preet@nexgencloud.com (Damanpreet Kaur Vohra) — Mon, 15 Jun 2026 08:32:54 GMT

Note: All metrics and scenarios in this case study are illustrative. Specific outcomes depend on workload architecture, deployment configuration and operational context.

Running AI-Intensive Health Research on a Secure Private Cloud

daman.preet@nexgencloud.com (Damanpreet Kaur Vohra) — Wed, 27 May 2026 14:15:13 GMT

1. The Problem

A European biomedical research consortium operating shared AI infrastructure across multiple research programmes. Six concurrent AI workload types. One funding and reporting timeline. And an infrastructure environment that had started failing them in specific, measurable ways.

The Inference SLA Kept Breaking

daman.preet@nexgencloud.com (Damanpreet Kaur Vohra) — Wed, 20 May 2026 08:10:15 GMT

Consider two engineering teams at European payments processors. Same model architecture. Same transaction volumes. Same contractual SLA with their card networks: sub-50 millisecond inference response time on every fraud score. Both are running on public cloud GPU infrastructure.

What is Slurm? HPC Workloads Explained | Hyperstack

daman.preet@nexgencloud.com (Damanpreet Kaur Vohra) — Mon, 18 May 2026 08:00:00 GMT

You are configuring a cluster for serious GPU workloads. You have looked at Kubernetes. You have read the docs. And Slurm keeps coming up, recommended by the team that ran the benchmark, cited in the paper that matched your architecture and mentioned in every thread about distributed training at scale.

KV Cache in LLMs for Low-Latency LLM Inference

daman.preet@nexgencloud.com (Damanpreet Kaur Vohra) — Mon, 11 May 2026 07:35:21 GMT

You kick off a long-context generation job. The model is solid, the prompt is well-formed and the first few tokens come back fast. Then, beyond the 30,000-token mark, latency increases. By 80,000 tokens, you are waiting. By 128k, each new token feels like a round-trip to somewhere expensive.

Building Gen AI Doesn’t Have to Be Expensive with AI Studio

daman.preet@nexgencloud.com (Damanpreet Kaur Vohra) — Tue, 04 Nov 2025 15:50:43 GMT

Everyone wants to build with Generative AI, from startups training niche chatbots to enterprises automating customer interactions. But for most, the excitement quickly fades when the costs of experimentation start to pile up.

Why Renting Cloud GPUs Beats On-Premise Hardware

daman.preet@nexgencloud.com (Damanpreet Kaur Vohra) — Mon, 03 Nov 2025 09:38:35 GMT

Training and deploying AI models is no small feat. High-performance GPUs, massive datasets and long compute hours are all part of the equation. To give an idea, earlier this meant investing heavily in on-premises infrastructure, which included racks of GPUs, storage servers, networking, cooling systems and different teams to maintain it all.

3 Types of Storage for AI Workloads You Need to Know

daman.preet@nexgencloud.com (Damanpreet Kaur Vohra) — Tue, 07 Oct 2025 08:17:28 GMT

GPU time is expensive. When storage cannot keep up with your training pipeline, those GPUs stall -- waiting for data instead of computing. For teams running large-scale training or distributed inference, storage is often the bottleneck that does not show up until it costs you hours of wasted compute.