A100 SXM4 80GB on Hyperstack
Visit HyperstackHyperstack delivers the NVIDIA A100 SXM4 80GB GPU, a premier Ampere architecture offering with 80GB HBM2e VRAM, optimized for enterprise AI, machine learning training, data analytics, and HPC workloads. This combination is noteworthy for its sustainability focus—powered by 100% renewable energy—appealing to European enterprises emphasizing GDPR compliance and green computing. Key value propositions include per-minute billing for precise cost control, robust multi-GPU scaling via NVLink, and the AI Studio toolset for streamlined generative AI pipelines. ML engineers evaluating options will appreciate the A100's 19.5 TFLOPS FP64 performance, Tensor Cores for mixed-precision acceleration, and MIG support for workload isolation. Hyperstack's infrastructure ensures low-latency access within Europe, balancing high throughput with environmental responsibility, making it a strategic choice for large-model training without legacy hourly billing overheads.
Why NVIDIA A100 SXM4 80GB on Hyperstack?
Opt for Hyperstack's NVIDIA A100 SXM4 80GB when sustainability, compliance, and flexibility matter most. Hyperstack's 100% renewable energy infrastructure reduces carbon emissions, aligning with ESG goals for enterprises, while GDPR-compliant EU data centers mitigate data sovereignty risks. Per-minute billing suits bursty ML workloads, offering granular cost savings over fixed-hour models. This complements the A100's strengths in handling massive datasets and multi-precision compute (e.g., TF32, FP16), with provider-optimized networking enhancing distributed training. The AI Studio provides pre-built workflows for LLMs and diffusion models, accelerating time-to-insight. Ideal for teams prioritizing ethical, efficient GPU acceleration without performance trade-offs.
Live Pricing
Real-time NVIDIA A100 SXM4 80GB offers from Hyperstack
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
![]() Hyperstack | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 192 vCPU 960GB RAM 16100GB Storage | United States | $1.60/GPU/hr $12.80/hr total (8×) | Available |

Performance Notes
The NVIDIA A100 SXM4 80GB on Hyperstack excels in memory-intensive tasks, with 80GB HBM2e enabling large-batch training for models like GPT-scale transformers. Expect peak FP64 at 19.5 TFLOPS and up to 624 TFLOPS in sparse Tensor Core ops. Multi-GPU setups likely use NVLink (600 GB/s bidirectional) or InfiniBand for efficient scaling, though exact interconnect details are provider-specific. Fast NVMe storage supports I/O-heavy workloads; MIG allows up to 7 instances per GPU for isolation. Real-world Hyperstack benchmarks are sparse—users should validate with tools like MLPerf. Strengths: superior to PCIe variants in bandwidth; limitations: potential queue times during peak demand.
A provider focused on sustainable, enterprise-grade GPU acceleration using 100% renewable energy.
Best For
Unique Features
- 100% renewable energy
- AI Studio for generative AI workflows
VRAM
80GB
Architecture
Ampere
Tier
enterprise
Platform Features
Getting Started
Launching NVIDIA A100 SXM4 80GB on Hyperstack is efficient via their intuitive dashboard, supporting quick spins for prototyping or scaling. Enterprise users benefit from pre-loaded NVIDIA drivers, CUDA, and AI frameworks, with per-minute billing starting immediately upon launch.
Steps
- 1Create and verify a Hyperstack account, enabling enterprise billing and compliance features.
- 2Access the GPU console, search for NVIDIA A100 SXM4 80GB, and select instance size.
- 3Configure vCPUs, RAM, storage (e.g., NVMe SSD), and base image (Ubuntu/CUDA).
- 4Review per-minute costs, set auto-scaling if needed, and deploy the instance.
- 5Connect via SSH/JupyterLab, pull Docker images, and run workloads.
Pro Tips
- Leverage AI Studio for instant generative AI templates, cutting setup time by 50% for LLM fine-tuning.
- Activate MIG mode early for multi-tenant efficiency, partitioning one A100 into isolated slices.
- Track resource metrics in the dashboard to pause idle instances, maximizing per-minute savings.
Frequently Asked Questions
What is Hyperstack's billing model for NVIDIA A100 SXM4 80GB?▾
Hyperstack bills per-minute for GPU instances including NVIDIA A100 SXM4 80GB. Check their pricing page for the most current billing details.
Does Hyperstack offer spot instances for NVIDIA A100 SXM4 80GB?▾
No, Hyperstack does not currently offer spot instances for NVIDIA A100 SXM4 80GB. All instances are billed at on-demand rates. However, they do offer reserved instances for committed usage, which can provide significant discounts for long-term workloads.
How can I access NVIDIA A100 SXM4 80GB instances on Hyperstack?▾
Hyperstack provides access to NVIDIA A100 SXM4 80GB instances via SSH, built-in Jupyter notebooks, programmatic API. The built-in Jupyter notebook support makes it easy to start experimenting immediately without additional setup. SSH access gives you full control over the instance for custom configurations and production deployments. API access enables automation and integration with your existing ML pipelines and CI/CD workflows.
What compliance certifications does Hyperstack have for NVIDIA A100 SXM4 80GB workloads?▾
Hyperstack maintains GDPR, ISO 27001 certifications, making it suitable for regulated workloads. Contact Hyperstack directly for detailed compliance documentation and BAA agreements if needed.
Can I use NVIDIA A100 SXM4 80GB with Kubernetes on Hyperstack?▾
Yes, Hyperstack supports Kubernetes for orchestrating NVIDIA A100 SXM4 80GB workloads. This enables you to deploy scalable ML pipelines, manage distributed training jobs across multiple GPUs, and integrate with MLOps tools like Kubeflow, Argo Workflows, and KServe. Kubernetes support is essential for teams building production-grade ML infrastructure.
What are the specifications of the NVIDIA A100 SXM4 80GB?▾
The NVIDIA A100 SXM4 80GB features 80GB of high-bandwidth memory, built on NVIDIA's Ampere architecture. As an enterprise-tier GPU, it's designed for large-scale AI training, inference at scale, and demanding HPC workloads. The substantial VRAM capacity supports large language models, complex neural networks, and multi-model deployments.
What workloads is NVIDIA A100 SXM4 80GB on Hyperstack best suited for?▾
The NVIDIA A100 SXM4 80GB on Hyperstack is well-suited for large-scale AI/ML training, LLM fine-tuning, batch inference at scale, and high-performance computing workloads. Hyperstack specifically excels at: European enterprises requiring GDPR compliance; Sustainable computing initiatives. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.
Does Hyperstack offer reserved instances for NVIDIA A100 SXM4 80GB?▾
Yes, Hyperstack offers reserved instance pricing for NVIDIA A100 SXM4 80GB, which can provide significant discounts (typically 20-40% off on-demand rates) for committed usage periods. Reserved instances are ideal for predictable, long-running workloads like production inference services, ongoing training pipelines, or development environments that run continuously. Contact Hyperstack for current reserved pricing and commitment terms.
What unique features does Hyperstack offer for NVIDIA A100 SXM4 80GB?▾
Hyperstack differentiates itself with: 100% renewable energy; AI Studio for generative AI workflows. These features may provide advantages depending on your specific workflow requirements and technical needs. Evaluate how these capabilities align with your ML infrastructure goals when making your decision.
How do I get started with NVIDIA A100 SXM4 80GB on Hyperstack?▾
To get started with NVIDIA A100 SXM4 80GB on Hyperstack, visit https://www.hyperstack.cloud?utm_source=gpuperhour&utm_medium=referral to create an account. Most providers offer a straightforward signup process, and some provide initial credits for new users. Once registered, you can typically launch a NVIDIA A100 SXM4 80GB instance within minutes through their dashboard or API. We recommend starting with a small experiment to familiarize yourself with the platform before scaling up to larger workloads.
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