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AI Setup Details (Planned)

Note: This AI infrastructure is currently planned and awaiting hardware acquisition. The specifications below represent the intended setup.

Hardware Configuration

Dedicated AI Hardware

  • GPU: 2x NVIDIA RTX 3090 (48GB VRAM)
  • CPU: AMD Ryzen 9 5900X (12 cores, 24 threads)
  • Storage: 2TB NVMe SSD dedicated to AI models and datasets
  • Cooling: Enhanced cooling solution for sustained workloads
  • Power Management: Configured for optimal performance/power ratio

Software Stack

Model Serving Infrastructure

  • Kubernetes: Single-node cluster for orchestration
  • OpenWebUI: Web interface with SSO integration (GitHub, Google, Microsoft)
  • Ollama: For large language models
  • A1111: For image generation

Self-Hosted Models

Large Language Models

  • Mistral 14B
  • Llama 4 17B
  • Phi-4
  • Qwen-coder 2.5 32B
  • Qwen 2.5 72B
  • DeepSeek-R1 70B
  • Gemma 3 27B
  • QwQ 32B
  • Qwen-VL 2.5 72B

Image Generation Models

  • Stable Diffusion: For high-quality image generation

Specialized Models

  • Whisper: For speech-to-text transcription
  • Embedding models: For semantic search and RAG applications
  • Classification models: For various automation tasks

Quantization Techniques

Model Optimization

  • GGUF Format: Conversion from original model formats
  • Quantization Levels: Experimentation with 4-bit to 16fp precision
  • Pruning: Selective neuron removal for model compression
  • KV Cache Optimization: For improved inference speed

Performance Benchmarks

  • Tokens per Second: Measurements across different models and quantization levels
  • Memory Usage: Tracking of VRAM and system RAM requirements
  • Quality Assessment: Evaluation of output quality vs. model size

Integration with Services

Web Interface

  • OpenWebUI: Kubernetes-hosted UI for interacting with models
  • Traefik Ingress: Secure HTTPS access with Let's Encrypt
  • API Access: REST API for programmatic access
  • SSO Authentication: Secure access control with multiple providers

Automation

  • Document Processing: Automated analysis of documents
  • Content Generation: Assistance for writing and creative tasks
  • Code Assistance: Integration with development environment

Knowledge Base

  • Vector Database: For storing and retrieving embeddings
  • Document Indexing: Processing of personal knowledge base
  • Retrieval Augmented Generation: Enhanced responses with personal data

Resource Management

Scheduling

  • Priority System: Resource allocation based on task importance
  • Queue Management: Handling of multiple inference requests
  • Batch Processing: Optimized for throughput when appropriate

Monitoring

  • Prometheus & Grafana: Comprehensive monitoring stack
  • Service Monitors: Custom monitoring for AI services
  • GPU Utilization: Real-time tracking of GPU usage
  • Inference Metrics: Response times and throughput
  • Error Tracking: Logging and alerting for model issues
  • AlertManager: Notifications for critical issues

Future Enhancements

Software Improvements

  • Model Distillation: Creating smaller, specialized models
  • Fine-tuning: Custom adaptations for specific use cases
  • Distributed Inference: Splitting workloads across multiple nodes

Integration Expansion

  • Voice Interface: Real-time speech interaction
  • Smart Home Integration: AI-powered automation
  • Personal Assistant: Comprehensive AI assistant with home lab awareness