NVIDIA AI & GPU INFRASTRUCTURE
AI Infrastructure,
Built as One System.
Dayo-Tech designs and delivers NVIDIA-powered AI and GPU
environments—integrating compute, high-speed networking,
storage, orchestration and security into one production-
ready infrastructure.
AI / ML WORKLOADS
ORCHESTRATION
COMPUTE
NETWORKING
STORAGE
SECURITY & INTEGRATION
01 — THE CHALLENGE
AI Performance Depends
on More Than GPUs.
High-performance GPUs are only one part of an AI environment. Real-world performance depends on
how compute, networking, storage and software work together—without creating bottlenecks across
the infrastructure.
GPU Compute
Keep expensive compute working.
GPU resources need to be sized and
architected around actual AI workloads,
utilization and future growth.
Networking
Move data without slowing GPUs
down.
High-speed, low-latency networking is
critical when workloads scale across
multiple GPU systems.
Storage
Feed the workload fast enough.
AI infrastructure requires storage
designed for high-throughput datasets,
checkpoints and concurrent workloads.
Orchestration
Turn infrastructure into a usable
platform.
Workload management and
orchestration help teams share
resources, scale workloads and use GPU
infrastructure efficiently.
The infrastructure performs as well as its weakest layer.
02 — WHAT WE BUILD
The Building Blocks of AI Infrastructure.
05
Security & Integration
Fit AI into the real enterprise.
Security, isolation, identity and
integration requirements are
designed into the environment
from the start—including private
and regulated deployments.
04
Orchestration &
Workload Management
Turn infrastructure into a
platform.
Kubernetes and workload-
management technologies help
teams allocate resources, run
workloads and efficiently use
shared GPU infrastructure.
03
AI-Ready Storage
Keep data moving to the GPUs.
High-performance storage
architectures for large datasets,
parallel workloads, checkpoints
and demanding AI workloads.
02
High-Speed Networking
Connect GPUs at full speed.
High-bandwidth, low-latency
Ethernet and InfiniBand
architectures for distributed AI
workloads and high-performance
GPU communication.
01
GPU Compute
Built around the workload.
NVIDIA DGX systems, GPU servers
and multi-GPU clusters designed
around performance requirements,
workload characteristics and
future scale.
03 — ONE INTEGRATED ARCHITECTURE
Designed as One System.
Every layer is designed around the workload—and around the layers it needs to work with.
ORCHESTRATION
Kubernetes • Workload Management
COMPUTE
DGX / GPU Clusters
NETWORKING
Ethernet / InfiniBand
STORAGE
AI-Ready Storage
SECURITY & INTEGRATION
Identity • Isolation • Enterprise Systems
DESIGN
INTEGRATE
VALIDATE
From architecture to a production-ready environment.
04 — ENVIRONMENTS
Built for the Environment
You Operate In.
Different organizations face different constraints. We design the infrastructure around your
workloads, scale, security requirements and operating model.
Regulated & Controlled Environments
Run AI where security cannot be an afterthought.
Isolated, governed and air-gapped environments designed
around strict security, data and operational requirements.
Research & Academia
Share powerful infrastructure without compromising control.
GPU and HPC environments designed for demanding
research workloads, shared resources and multi-user or multi-
institution requirements.
Enterprise AI
Move AI from experimentation to production.
Private and enterprise-grade AI environments designed to
integrate with existing infrastructure, security policies and
operational requirements.
AI & Deep-Tech Startups
Build for today. Architect for what comes next.
From an initial GPU environment to production-scale AI
infrastructure, designed to grow without rebuilding the
foundation at every stage.
05 — PROVEN IN PRODUCTION
Proven in Production.
Real AI infrastructure projects. Different environments, different constraints.
AI & DEEP-TECH STARTUP
Full-Stack AI Infrastructure
NVIDIA GPU compute, 200Gb InfiniBand
and 1.5PB storage designed as one scalable
AI environment.
FINANCIAL SERVICES
Governed GPU AI Platform
Enterprise GPU infrastructure designed around
the governance and operational requirements of a
regulated banking environment.
CONTROLLED ENVIRONMENT
Air-Gapped AI Research
Secure GPU infrastructure providing researchers
with controlled self-service AI capabilities inside
an isolated environment.
06 — WHY DAYO-TECH
Why Dayo-Tech for AI Infrastructure?
Complex AI environments require architecture, integration and accountability across the
complete infrastructure.
04
From Design to Validation
Architecture is only successful when it works in production.
From requirements and architecture through integration,
deployment and validation of the complete environment.
03
Built for Complex Environments
From fast-growing startups to regulated organizations.
Experience across high-scale GPU environments, shared research
infrastructure and controlled enterprise deployments.
02
NVIDIA Elite Partner
NVIDIA expertise backed by an official partnership.
Dayo-Tech brings NVIDIA technologies into complete AI
infrastructure architectures—from GPU compute to the systems
around it.
01
End-to-End Architecture
One architecture. Not a collection of products.
Compute, networking, storage, orchestration and security are
designed around the workload as one environment.
07 — FAQ
AI Infrastructure — Frequently Asked Questions
A GPU server is a single system containing one or more GPUs. A GPU cluster connects multiple GPU servers through high-speed networking and shared infrastructure to support larger, distributed AI workloads.
NVIDIA DGX is typically considered when organizations need a validated, high-performance platform for demanding AI workloads and want an integrated approach to compute, networking and software.
Yes. AI infrastructure can be deployed on-premises, in private environments or as part of a hybrid architecture, depending on workload, security, data and operational requirements.
GPUs depend on fast access to data and efficient communication between systems. Networking and storage that cannot keep pace with GPU workloads can create bottlenecks and reduce overall infrastructure performance.
Yes. Dayo-Tech can assess existing compute, networking, storage and enterprise systems and design an AI infrastructure approach that integrates with or builds upon the current environment where appropriate.