Compute Dynamics: Choosing the Right Hardware for Your Workload

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

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.

Current Market Hardware Options

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

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

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

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

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

Ideal for:

Cloud-native applications, edge deployments, efficient compute clusters.

High-Speed NVMe Storage (PCIe Gen5)

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

PCIe 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

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
Real-Time AI Inference	A100 + PCIe Gen5 NVMe	Low-latency GPU inference and fast storage
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

Robotics

H100 GPUs

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

Biotech

AMD Genoa CPUs

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

Fintech

NVIDIA A100 GPUs

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

Automotive

NVIDIA Grace Hopper Superchip (GH200)

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.

Aerospace

AMD Genoa CPUs + 400G Infiniband networking

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.

Defense

ARM-based CPUs

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.

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.

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.