High-Performance Compute & AI Infrastructure

Service Overview

High-performance computing (HPC) was once the exclusive domain of government labs, universities, and large industries like aerospace or oil exploration. Traditionally used for physics simulations and analyzing massive datasets, HPC has rapidly democratized thanks to specialized co-processors like GPUs and FPGAs. Workloads that once demanded entire data centers can now run on small server grids, leveraging the exponential growth in GPU power.

Today, HPC is found in finance, DNA sequencing, sustainable energy simulation, and computational chemistry, to name a few. But nowhere is the impact greater than in machine learning—where deep learning algorithms are enabling computers to surpass human abilities in fields like real-time video analysis, translation, and autonomous vehicles.

arqitekta has the knowhow to build modern HPC environments from scratch—on-prem, cloud, or hybrid. We design compute nodes, choose GPU accelerators, select high-speed interconnects, and work with frameworks like TensorFlow, Microsoft Cognitive Toolkit, and Caffe2. From algorithm training to full-scale deployment, we help you turn AI ambition into reality.

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The HPC/AI Revolution

From Exclusive to Accessible

Traditional HPC (Pre-2010)

• Users: Government labs, oil & gas, aerospace
• Cost: $10M-100M+ installations
• Access: PhD required, batch jobs, long waits
• Applications: Weather, nuclear simulation, seismic

Modern HPC/AI (Today)

• Users: Every industry, startups to enterprises
• Cost: $100K-10M typical, cloud options available
• Access: APIs, notebooks, real-time inference
• Applications: ML/AI, analytics, research, product development

Why Now?

GPU Evolution

2010: 1 TFLOPS (Tesla C2050)
2015: 7 TFLOPS (Tesla K80)
2020: 156 TFLOPS (A100)
2023: 1000+ TFLOPS (H100)

Cost Performance

• 100x improvement in 10 years
• Cloud accessibility (pay per use)
• Commodity hardware options
• Open source frameworks

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Architecture Components

Compute Infrastructure

GPU Accelerators

NVIDIA Options

• H100: Ultimate performance for large models
• A100: Workhorse for training and inference
• A40/A30: Cost-effective for many workloads
• T4: Inference optimization

Alternative Accelerators

• AMD MI250X: Open ecosystem option
• Intel Gaudi2: Efficient training
• Google TPU: Cloud-native option
• AWS Inferentia: Inference specialization

CPU Considerations

• AMD EPYC: High memory bandwidth
• Intel Xeon: Broad ecosystem support
• ARM: Power efficiency for edge
• POWER: Legacy HPC compatibility

Memory Architecture

┌─────────────┐
│   HBM3      │ <- 3TB/s GPU Memory
├─────────────┤
│   GDDR6     │ <- 1TB/s GPU Memory
├─────────────┤
│   DDR5      │ <- 200GB/s System Memory
├─────────────┤
│   NVMe      │ <- 7GB/s Storage
└─────────────┘

Interconnect Technologies

Intra-Node

• NVLink: 900GB/s GPU-to-GPU
• PCIe 5.0: 128GB/s per slot
• CXL: Memory pooling
• Infinity Fabric: AMD ecosystem

Inter-Node

• InfiniBand HDR: 200Gbps RDMA
• Ethernet: 100-400Gbps options
• Slingshot: Cray/HPE fabric
• OmniPath: Intel fabric (legacy)

Storage Architecture

High-Performance Storage

• Parallel File Systems: Lustre, GPFS, BeeGFS
• NVMe Arrays: Sub-millisecond latency
• Burst Buffers: Intermediate tier
• Object Storage: Training data repository

Data Movement

Archive → Object Store → Parallel FS → Local SSD → GPU Memory
  1GB/s     10GB/s        100GB/s      7GB/s      3TB/s

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AI/ML Workload Optimization

Training Infrastructure

Single Node Training

Configuration Example

• 8x A100 80GB GPUs
• 2x AMD EPYC 64-core
• 2TB DDR5 RAM
• 30TB NVMe storage
• Performance: 5 PetaFLOPS

Multi-Node Training

Scale-Out Cluster

• 16-256 nodes typical
• InfiniBand interconnect
• Distributed training frameworks
• Performance: 100+ PetaFLOPS

Inference Deployment

Edge Inference

• NVIDIA Jetson platform
• Intel NCS
• Google Coral
• Custom FPGA/ASIC

Data Center Inference

• GPU sharing (MIG, vGPU)
• Inference servers (Triton)
• Model optimization (TensorRT)
• Kubernetes orchestration

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Industry Applications

Financial Services

Use Case: Risk modeling and fraud detection

Requirements

• Real-time inference (<10ms)
• Regulatory compliance
• High availability
• Audit trails

Solution Architecture

• GPU clusters for model training
• Inference at edge locations
• Redundant infrastructure
• Encrypted data pipelines

Life Sciences

Use Case: Drug discovery and genomics

Requirements

• Massive parallel processing
• Petabyte-scale data
• Researcher accessibility
• Collaboration tools

Solution Architecture

• HPC cluster with job scheduler
• High-memory nodes for assembly
• Jupyter hub for researchers
• Secure data sharing

Autonomous Systems

Use Case: Self-driving car development

Requirements

• Video processing pipelines
• Simulation environments
• Model versioning
• Safety certification

Solution Architecture

• GPU clusters for training
• Simulation farms
• Edge inference testing
• CI/CD for models

Energy & Climate

Use Case: Climate modeling and renewable optimization

Requirements

• Traditional HPC codes
• GPU acceleration
• Long-running jobs
• Data visualization

Solution Architecture

• Hybrid CPU/GPU nodes
• Parallel file system
• Visualization clusters
• Archive integration

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Framework Ecosystem

Training Frameworks

• TensorFlow: Google's framework
• PyTorch: Research favorite
• JAX: High-performance ML
• MXNet: Scalable training

Distributed Training

• Horovod: Uber's distributed framework
• DeepSpeed: Microsoft's optimization
• FairScale: Meta's scaling library
• Ray: Distributed AI platform

Inference Optimization

• TensorRT: NVIDIA optimization
• ONNX Runtime: Cross-platform
• OpenVINO: Intel optimization
• TensorFlow Lite: Mobile/edge

MLOps Platforms

• Kubeflow: Kubernetes-native
• MLflow: Experiment tracking
• Weights & Biases: Monitoring
• ClearML: Full lifecycle

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Design Patterns

Pattern 1: Shared Research Cluster

For: Universities, R&D departments

┌─────────────────┐
│   Login Nodes   │
├─────────────────┤
│  Job Scheduler  │ <- Slurm/PBS
├─────────────────┤
│ Compute Nodes   │ <- CPU+GPU
├─────────────────┤
│ Parallel Storage│ <- Lustre
└─────────────────┘

Pattern 2: AI Training Factory

For: ML engineering teams

┌─────────────────┐
│   Notebooks     │ <- JupyterHub
├─────────────────┤
│  Experiment Mgmt│ <- MLflow
├─────────────────┤
│ Training Cluster│ <- Kubernetes
├─────────────────┤
│ Model Registry  │ <- S3/Registry
└─────────────────┘

Pattern 3: Hybrid Edge-Cloud

For: IoT and real-time AI

┌─────────────────┐
│  Edge Devices   │ <- Inference
├─────────────────┤
│  Edge Servers   │ <- Aggregation
├─────────────────┤
│ Private Cloud   │ <- Training
├─────────────────┤
│ Public Cloud    │ <- Burst/Archive
└─────────────────┘

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Cost Optimization

CapEx vs OpEx Options

On-Premises

• Pros: Predictable costs, full control, data sovereignty
• Cons: High initial investment, ongoing maintenance
• Best for: Consistent workloads, sensitive data

Cloud HPC

• Pros: No CapEx, elastic scaling, latest hardware
• Cons: Data egress costs, less control
• Best for: Burst workloads, experimentation

Hybrid Model

• Pros: Optimize cost/performance, flexibility
• Cons: Complexity, multiple vendors
• Best for: Most enterprises

TCO Considerations

On-Prem 3-Year TCO:
- Hardware: 40%
- Power/Cooling: 20%
- Maintenance: 15%
- Personnel: 25%

Cloud 3-Year TCO:
- Compute: 60%
- Storage: 20%
- Egress: 10%
- Support: 10%

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Implementation Approach

Phase 1: Requirements & Design

Weeks 1-4

• Workload analysis
• Performance requirements
• Architecture design
• Vendor selection

Phase 2: Proof of Concept

Weeks 5-8

• Benchmark testing
• Framework validation
• Scaling tests
• Cost modeling

Phase 3: Production Build

Months 3-6

• Hardware procurement
• Software deployment
• Integration testing
• Team training

Phase 4: Operationalization

Ongoing

• Performance tuning
• Capacity management
• Cost optimization
• Continuous improvement

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Success Metrics

Performance KPIs

• FLOPS utilization: >80%
• Job queue time: <5 minutes
• Model training speed: 10x improvement
• Inference latency: <100ms

Business KPIs

• Time to market: 50% reduction
• Experiment velocity: 10x increase
• Model accuracy: Significant improvement
• ROI: 200-400% typical