HyperStack

What is HyperStack?

HyperStack is a GPU cloud infrastructure platform developed by Nexgen Cloud that specializes in providing dedicated, high-performance computing resources for AI and machine learning workloads. Founded to address the growing demand for accessible GPU compute, HyperStack differentiates itself through bare-metal GPU access, transparent pricing, and infrastructure specifically designed for AI applications. Unlike traditional cloud providers where GPU instances are often shared or virtualized, HyperStack provides dedicated GPU resources that deliver consistent, predictable performance.

The platform is built on modern data center infrastructure with high-bandwidth networking, NVMe storage, and the latest NVIDIA GPU architectures. HyperStack supports both on-demand and reserved instances, allowing users to scale resources dynamically or lock in capacity for long-term projects. With data centers strategically located globally, HyperStack provides low-latency access for users worldwide while maintaining competitive pricing that makes advanced AI infrastructure accessible to startups, researchers, and enterprises alike.

Key Features and Infrastructure

GPU Options

• NVIDIA H100 SXM - 80GB HBM3 memory, ideal for large-scale training and inference
• NVIDIA A100 SXM/PCIe - 40GB/80GB variants for diverse AI workloads
• NVIDIA L40S - Cost-effective option for inference and mid-scale training
• NVIDIA A10/A40 - General-purpose AI acceleration
• Multi-GPU configurations from 1x to 8x GPUs per instance
• Bare-metal access without virtualization overhead
• GPU-to-GPU NVLink and PCIe Gen4/Gen5 interconnects

Infrastructure Capabilities

• High-bandwidth networking (100-400 Gbps per node)
• NVMe SSD storage for fast data access and checkpointing
• InfiniBand options for distributed training workloads
• Flexible CPU configurations (AMD EPYC, Intel Xeon)
• RAM options from 128GB to 2TB per instance
• On-demand and reserved instance pricing models
• Auto-scaling and orchestration support
• Integration with Kubernetes, Docker, and MLOps tools

Use Cases and Applications

HyperStack is designed for demanding AI and ML workloads that require dedicated, high-performance GPU resources:

• Training large language models (LLMs) with billions of parameters
• Fine-tuning foundation models (Llama, GPT, BERT) on custom datasets
• High-throughput inference serving for production AI applications
• Computer vision model training for image and video analysis
• Diffusion model training and inference (Stable Diffusion, FLUX)
• Reinforcement learning and multi-agent simulations
• Distributed training across multiple GPUs and nodes
• Research and experimentation with state-of-the-art models
• Rendering and 3D graphics acceleration
• Scientific computing and HPC workloads

HyperStack vs Other GPU Cloud Providers

Compared to major cloud providers like AWS, Azure, and GCP, HyperStack offers several advantages for AI-focused workloads. The bare-metal GPU access eliminates virtualization overhead, providing up to 15-20% better performance than virtualized instances. Pricing is transparent and often more competitive, with no hidden costs for network egress or storage. The platform is purpose-built for AI/ML, avoiding the complexity of general-purpose cloud environments. For teams that need dedicated GPU resources without the overhead of managing physical infrastructure, HyperStack provides an optimal middle ground.

However, HyperStack focuses specifically on GPU compute and may not offer the breadth of services available on hyperscaler clouds (databases, serverless functions, managed AI services). For pure GPU workloads, especially training and inference at scale, HyperStack's dedicated infrastructure and cost-effectiveness make it a compelling choice. The platform works well alongside major cloud providers, with many teams using HyperStack for GPU-intensive tasks while leveraging AWS or GCP for other cloud services.

Getting Started with HyperStack

Getting started with HyperStack is straightforward. Users create an account on the HyperStack platform, select their desired GPU configuration (instance type, GPU count, storage, networking), and deploy through the web console, CLI, or API. The platform supports popular machine learning frameworks (PyTorch, TensorFlow, JAX) and comes with pre-configured Docker images for common AI workloads. Users can also bring their own Docker containers or custom environment setups.

HyperStack provides comprehensive documentation, tutorials, and integration guides for common AI frameworks and tools. The platform includes monitoring and logging capabilities to track GPU utilization, training progress, and system metrics. For teams requiring enterprise support, HyperStack offers dedicated account management, priority support, and custom infrastructure configurations. Billing is transparent with per-hour pricing and the option to reserve capacity for cost savings on long-running workloads.

Integration with 21medien Services

21medien leverages HyperStack as part of our multi-cloud GPU infrastructure strategy. We use HyperStack for dedicated training workloads, including fine-tuning large language models for client-specific applications and training custom computer vision models. The platform's bare-metal GPU access ensures consistent performance for production inference services where latency and throughput are critical. Our team provides HyperStack deployment and optimization services, helping clients choose the right GPU configurations, optimize training workflows, and manage costs effectively.

Pricing and Access

HyperStack operates on a pay-as-you-go pricing model with transparent hourly rates. GPU instance pricing varies by GPU type, with NVIDIA A10 instances starting around $1.50/hour, A100 40GB at $2.50-3.50/hour, A100 80GB at $3.50-4.50/hour, and H100 80GB at $4.50-6.00/hour (prices approximate and subject to change). Storage and networking are included with no hidden fees for data transfer. Reserved instances offer 20-40% discounts for 1-month, 6-month, or 12-month commitments. Enterprise pricing with volume discounts and custom SLAs is available for large-scale deployments. The platform offers free credits for new users to test infrastructure before committing to production workloads.