GPU-Accelerated AI Server Infrastructure

Status: Active Development | Domain: AI Infrastructure, MLOps, Computer Vision

Enterprise-grade AI infrastructure leveraging NVIDIA RTX 3090 (24GB VRAM) for distributed computing, designed specifically for high-performance AI, Computer Vision, and Language Processing applications.

🚀 Overview

An end-to-end framework enabling seamless deployment of heavily parallelized Machine Learning models. Built from the ground up to solve the challenges of GPU resource management, request queuing, and real-time monitoring, this server provides a robust API gateway for high-throughput AI services (e.g., real-time image processing, inference pipelines).

🏗 Architecture

System Architecture

graph TB
    subgraph External["External Layer"]
        CloudFlare["Cloudflare SSL"]
        Domain["statista.live"]
    end

    subgraph Services["Service Layer"]
        Nginx["Nginx Reverse Proxy"]
        Grafana["Monitoring Dashboard"]
        Prometheus["Metrics Collection"]
        Redis["Cache Layer"]
    end

    subgraph Core["AI Core"]
        API["FastAPI Server"]
        GPUMgr["GPU Resource Manager"]
        TaskQueue["Task Scheduler"]
        
        subgraph ML["ML Components"]
            RAG["RAG System"]
            CV["Computer Vision"]
            NLP["Language Processing"]
        end
    end

    subgraph Hardware["Hardware Layer"]
        GPU["RTX 3090 GPU"]
        CPU["Ryzen 5 5600X"]
        Memory["24GB RAM"]
    end

    External --> Services
    Services --> Core
    Core --> Hardware

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Resource Management

sequenceDiagram
    participant Client
    participant Nginx
    participant API
    participant GPU
    participant Monitor

    Client->>Nginx: HTTPS Request
    Nginx->>API: Forward Request
    API->>GPU: Request Resources
    GPU-->>Monitor: Report Status
    Monitor-->>API: Resource Status
    
    alt Resources Available
        API->>GPU: Allocate Memory
        GPU-->>API: Task Complete
        API-->>Client: Success Response
    else Resources Exhausted
        API-->>Client: Retry with Backoff
    end

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💻 Core Components

AI Server

• FastAPI-based REST API for asynchronous, non-blocking inference.
• Real-time GPU resource management (VRAM allocation, state tracking).
• Task queueing and dynamic scheduling for concurrent model execution.
• Automatic memory optimization (cache clearing, batch sizing).

Monitoring & Telemetry

• Exposes real-time GPU metrics to Prometheus/Grafana.
• System resource tracking for proactive bottleneck detection.
• Task performance analytics (latency, throughput, TFLOPS).

Security & Gateway

• Cloudflare SSL/TLS for secure ingress.
• Rate limiting and API authentication.
• Network isolation using Docker-compose.

📊 Performance Metrics

GPU Benchmarks

# Latest benchmark results
{
    "matmul_10000x10000": {
        "compute_time_ms": 84.02,
        "tflops": 11.96
    },
    "memory_bandwidth": {
        "size_gb": 0.5,
        "bandwidth_gbps": 170.04
    }
}

System Specifications

Component	Specification	Performance
GPU	RTX 3090 24GB	35.58 TFLOPS
CPU	Ryzen 5 5600X	6C/12T @ 4.6GHz
RAM	24GB DDR4	3200MHz
Storage	NVMe SSD	3.5GB/s Read

🚀 Quick Start

Prerequisites

# System Requirements
NVIDIA Driver >= 566.03
CUDA >= 12.3.1
Docker + NVIDIA Container Toolkit
Python >= 3.10

Installation

# Clone and setup
git clone https://github.com/geek2geeks/gpu-inference-server.git
cd gpu-inference-server

# Create environment
conda create -n pytorch_gpu python=3.10
conda activate pytorch_gpu

# Install dependencies
pip install -r requirements.txt

# Start services
docker-compose -f docker/docker-compose.yml up -d

Validation

# Run system validation
python scripts/monitoring/validate.py

# Run GPU benchmarks
python scripts/utils/benchmark.py

📋 API Documentation

Core Endpoints

/health:
  GET: System health status
  
/gpu/stats:
  GET: Real-time GPU metrics

/process-image:
  POST: GPU-accelerated image processing

Example Usage

import requests

# Health check
response = requests.get("https://api.statista.live/health")
print(response.json())

# GPU stats
stats = requests.get("https://api.statista.live/gpu/stats")
print(stats.json())

🔧 Development

Directory Structure

justica/
├── config/            # Service configurations
├── src/              # Source code
│   ├── api/          # FastAPI application
│   ├── core/         # Core utilities
│   └── ml/           # ML components
├── scripts/          # Utility scripts
└── docker/           # Container configs

Testing

# Run all tests
python -m pytest tests/

# Run GPU tests
python -m pytest tests/unit/test_gpu.py

📈 Status & Roadmap

Current Status

• ✅ GPU Infrastructure
• ✅ Basic API
• ✅ Monitoring
• 🚧 SSL/Domain
• 📅 ML Pipeline

Upcoming Features

1. RAG System Integration
2. Video Processing Pipeline
3. Custom ML Model Support
4. Advanced Monitoring

📄 License

MIT License - see LICENSE.md