High-Performance GPU Data Center Architecture
Stixor developed a multi-vendor GPU cloud platform to provide organizations with high-performance AI infrastructure capable of handling diverse AI/ML workloads efficiently. Supporting Huawei Ascend and NVIDIA GPUs, the platform enables enterprises and public-sector organizations to develop, train, and deploy AI models quickly, securely, and cost-effectively.
≥40%
GPU Utilization
≥99%
System Uptime
≥35%
Model Deployment Speed
≥25%
Cost Optimization
Business Overview & Strategic Direction
EXECUTIVE SUMMARY
Enterprises and public-sector organizations face challenges with GPU-intensive AI workloads, including scaling limitations, high operational costs, and complex infrastructure management. Traditional on-premises setups are expensive, inflexible, and difficult to scale for AI innovation.
Stixor’s multi-vendor GPU cloud platform addresses these issues by providing secure, scalable AI-as-a-Service infrastructure, integrating enterprise-grade frameworks, and enabling rapid deployment of AI workloads. The platform empowers enterprises, SMEs, startups, and governments to innovate efficiently while maintaining compliance, reliability, and cost optimization.
Operational Challenges
Managing a high-performance AI cloud platform involves handling GPU-intensive workloads, ensuring 24×7 monitoring, dynamically allocating resources across multiple tenants, maintaining strict compliance standards, and optimizing costs through pay-as-you-grow consumption
PROBLEM STATEMENT
GPU-Intensive AI/ML Workloads:
Handling large-scale deep learning, generative AI, and computer vision tasks
24×7 NOC Monitoring
Continuous monitoring for network, hardware, and AI workloads
Multi-Tenant GPU Allocation
Dynamic resource sharing across departments, clients, and workloads
Compliance Requirements
ISO 27001, GDPR, and regional data residency
Multi-Stage AI Platform for GPU Cloud
A structured, AI-enabled lifecycle designed to ensure performance, scalability, security, and operational efficiency.
Our Solution
High GPU Workload Complexity
Managing multiple AI workloads across diverse GPUs often leads to resource inefficiency and scheduling delays.
Limited Real-Time Insights
Without centralized monitoring, organizations struggle to analyze performance, detect bottlenecks, and prevent failures.
Cost & Compliance Constraints
Enterprises face high infrastructure costs and must meet strict ISO 27001 and GDPR compliance requirements.
01
GPU & AI Infrastructure
- Huawei Ascend AI Processors (310/360/910) for deep learning training & inference
- NVIDIA GPUs (A100/H100/Blackwell) for generative AI, NLP, CV, simulations
- Atlas & NVIDIA-certified servers for high-throughput, energy-efficient computing
02
AI Frameworks & Software Stack
- NVIDIA AI Enterprise stack: TensorRT, cuDNN, RAPIDS, enterprise runtime for production AI
- Model conversion tools: TensorFlow, PyTorch, ONNX → MindSpore or NVIDIA runtime
- ModelArts, MindX DL/Serve, AICS for distributed training and multi-model serving
03
Security & Compliance
- Threat mitigation: model theft, data poisoning, malicious inputs
- Compliance-ready architecture: ISO 27001, GDPR
- Encrypted communication, audit logging, and NOC-based threat monitoring
Transformative Outcomes Across All Metrics
IMPACT & RESULTS
Operational Performance
≥40%
GPU Utilization
≥99%
System Uptime
≥35%
Model Deployment Speed
Operational Efficiency
≥25%
Cost Optimization
≥30%
NOC Efficiency
ISO 27001
Compliance & Security
Technology Stack
TOOLS USED
Python / PyTorch
TensorFlow
Prometheus
NVIDIA CUDA
Discuss Your Enterprise
Use Case
From small to large scale enterprises, we deliver next-gen AI, data engineering, and actionable insights.
