Hardware Dynamics: Choosing the Right Compute for the Right Workload on Vantage

As compute demands evolve across industries—from startups training foundation models to biotech teams running genomics pipelines—choosing the right hardware isn't just a technical decision, it's strategic.

At Vantage, we believe cloud HPC should be powerful, transparent, and tailored. This guide highlights core hardware options currently available on the Vantage platform and maps them to real-world use cases to help you select the optimal hardware for your workload.

Want a broader decision-making framework? Check out our guide on choosing the right compute.

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Current Market Hardware Options

Here's an overview of hardware commonly used by different workloads:

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NVIDIA H100 SXM

Best for: Foundation model training, dense L/transformer workloads, AI infrastructure

• Peak FP8/F16 performance with Transformer Engine
  
• HBM3 memory: up to 3.35 TB/s bandwidth
  
• NVLink for rapid intra-node GPU communication

Ideal for: Frontier AI startups, ML researchers, enterprise-scale model development.

AMD EPYC “Genoa” CPUs (96-core)

Best for: High-performance CPU workloads—simulations, genomics, rendering, data preparation

• Zen 4 architecture, built on 5nm
  
• High memory bandwidth and PCIe Gen5
  
• Exceptional performance-per-dollar for multithreaded tasks

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Ideal for: Research labs, bioinformatics, simulation-intensive applications.

NVIDIA A100 PCIe GPUs

Best for: Inference, training mid-size models, analytics

• PCIe Gen4 interface
  
• 40–80GB GPU memory options
  
• Excellent balance of performance and cost-efficiency

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Ideal for: Fintech, applied AI, NLP inference, early-stage training pipelines.

NVIDIA Grace Hopper Superchip (GH200)

Best for: Large-scale AI/HPC workloads, accelerated compute, hybrid training/inference workloads

• Integrated Grace CPU and Hopper GPU
  
• High-bandwidth, coherent CPU-GPU memory interface
  
• Scalable performance for diverse workloads

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Ideal for: Advanced AI research, large-scale HPC deployments, integrated training and inference.

ARM-based CPUs

Best for: Power-efficient, scalable workloads, cloud-native and edge computing

• High performance-per-watt
  
• Scalable architecture suitable for diverse workloads
  
• Excellent for containerized applications and microservices

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Ideal for: Cloud-native applications, edge deployments, effici compute clusters.

High-Speed NVMe Storage (PCIe Gen5)

Best for: Fast checkpointing, large intermediate files, IOPS-intensive workflows

• PCe Gen5 NVMe SSDs for maximum throughput
  
• Ultra-low latency and high sequential read/write speeds
  
• Distributed storage volumes for scalability
  
  

400G Infiniband & RoCEv2 Networking

Best for: Low-latency, high-throughput multi-node workloads (MPI, CFD, etc.)

• 400Gbps bandwidth for ultra-fast inter-node communication
  
• Minimal latency for rapid multi-node scaling
  
  

Hardware : Use-Case

Robotics

Using H100 GPUs to train reinforcement learning (RL) models within synthetic simulation environments, enabling faster training cycles and more robust robotic control algorithms.

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Biotech

Leveraging AMD Genoa CPUs to perform genome alignments and bioinformatics analyses, achieving 40% faster results compared to traditional cloud solutions.

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Fintech

Deploying NVIDIA A100 GPUs to serve transformer-based NLP models for real-time inference, consistently achieving latency below 20 milliseconds for financial services.

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Automotive

Employing the NVIDIA Grace Hopper Superchip (GH200) for large-scale autonomous vehicle simulation, AI model training, and real-time inference workloads, significantly speeding up AI-driven vehicle development and validation.

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Aerospace

Accelerating aerodynamic modeling and computational fluid dynamics (CFD) using AMD Genoa CPUs and 400G Infiniband networking, dramatically reducing simulation run times and enabling quicker iterative aircraft design processes.

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Defense

Utilizing ARM-based CPUs for secure, power-efficient edge computing in distributed surveillance and intelligence-gathering applications, optimizing thermal efficiency, and extending operational longevity in challenging environments.

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Strategic Hardware Alignment

Choosing the right infrastructure involves aligning compute, storage, and networking to your specific workload. Proper alignment reduces waste, enhances performance, and accelerates outcomes:

• Compute: Dictates throughput and cost-efficiency, from frontier AI models (H100, GH200) to CPU-heavy genomics (AMD Genoa) and efficient edge deployments (ARM).
  
• Storage: Latest PCIe Gen5 NVMe storage enables rapid checkpointing and high-speed data handling, crucial for intensive AI workloads and real-time inference.
  
• Networking: 400G Infiniband and RoCEv2 ensure minimal latency and maximum bandwidth, optimal for MPI-based HPC workloads and distributed simulations.
  

Smart hardware choices shape outcomes, reducing time-to-value and overhead for HPC and AI/ML workloads.

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At Vantage: Your Stack, Simplified

• Bring your containers.
  
• Pre-configured ML/HPC images.
  
• Launch jobs in minutes, no vendor lock-in.
  
• Access the latest networking, storage, CPUs, and GPUs.