Choosing the Right Hardware for Your Workload

Choosing the right compute for your workload can be complicated. Let's break it down.

As computational demands grow across industries, from AI startups training foundation models to biotech teams running genomics pipelines, hardware selection has become both a technical and strategic decision.

Current Market Hardware Options

NVIDIA H100 SXM

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

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

AMD EPYC "Genoa" CPUs (96-core)

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

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

NVIDIA A100 PCIe GPUs

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

• PCIe Gen4 interface
• 40-80GB GPU memory options
• Balanced performance and cost-efficiency

NVIDIA Grace Hopper Superchip (GH200)

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

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

ARM-based CPUs

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

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

Hardware Use-Case Mapping

Use Case	Recommended Hardware	Why It Works
Foundation Model Training	NVIDIA H100 SXM	Peak FP8/F16, NVLink, massive memory bandwidth
Genomics / Bioinformatics	AMD EPYC Genoa	High-core count, optimal for CPU-heavy workloads
LLM Inference	A100 PCIe + PCIe Gen5 NVMe	Efficient inference with rapid I/O
Finetuning AI Models	A100 or H100	Balanced, cost-effective GPU training
Engineering Simulations / CFD	Genoa CPUs + 400G Infiniband	CPU power + ultra-high-speed MPI networking
Large-scale AI/HPC Hybrid	NVIDIA Grace Hopper GH200	Integrated CPU-GPU for unified computing
Cloud-native / Edge Computing	ARM-based CPUs	Scalable, efficient, suitable for containers/edge

Real-World Use Case Examples

• Robotics: Using H100 GPUs to train reinforcement learning models within synthetic simulation environments
• Biotech: Leveraging AMD Genoa CPUs to perform genome alignments, achieving 40% faster results compared to traditional cloud solutions
• Fintech: Deploying NVIDIA A100 GPUs to serve transformer-based NLP models for real-time inference, consistently achieving latency below 20 milliseconds
• Automotive: Employing the NVIDIA Grace Hopper Superchip for large-scale autonomous vehicle simulation and AI model training
• Aerospace: Accelerating aerodynamic modeling and CFD using AMD Genoa CPUs and 400G Infiniband networking

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