🎯 Project Overview

Built a high-performance home AI laboratory for running local LLMs, training models, and experimenting with cutting-edge AI technologies—all without relying on expensive cloud services. The lab serves as both a development environment and production infrastructure for AI-powered VoIP applications.

💼 Business Impact

Zero cloud costs: No per-token charges, no surprise bills—complete cost control
Privacy-first: Sensitive data never leaves the network—GDPR compliant by design
Rapid experimentation: No API rate limits or quotas—iterate freely
Production-ready: Powers real customer-facing AI voice agents
ROI achieved: Hardware investment paid for itself in 6 months vs cloud costs

🛠️ Hardware Specifications

🖥️ Computing Power

CPU: AMD Ryzen 9 9950X3D (16C/32T)
GPU: NVIDIA RTX 5070 Ti (16GB VRAM)
RAM: 64GB DDR5-6000 CL30
Purpose: Parallel LLM inference, training

💾 Storage System

Boot: 1TB NVMe Gen4 (OS + apps)
Models: 4TB NVMe Gen4 (model library)
Data: 5TB SSD RAID (datasets, checkpoints)
Backup: 10TB ZFS pool (TrueNAS)

🌐 Networking

Internal: 10Gbps fiber between nodes
External: 1Gbps fiber internet
VPN: Tailscale for remote access
VLAN: Segmented network for security

❄️ Cooling & Power

CPU: 360mm AIO liquid cooler
Case: High airflow with 9x fans
PSU: 1000W 80+ Titanium
UPS: 1500VA for clean power

💻 Software Stack

Operating System & Virtualization

Proxmox VE 8.1 - Hypervisor for VM orchestration
Ubuntu 22.04 LTS - Primary development VM
TrueNAS SCALE - ZFS storage and backup VM
Docker & Docker Compose - Container management

AI/ML Framework Stack

PyTorch 2.1 with CUDA 12.1 - Deep learning framework
Transformers (Hugging Face) - Pre-trained models
LangChain - LLM application framework
ChromaDB - Vector database for RAG
FastAPI - API server for model serving

Model Serving & Inference

ollama - Easy local LLM deployment
vLLM - High-throughput inference server
llama.cpp - CPU/GPU hybrid inference
Whisper.cpp - Real-time speech-to-text
Piper TTS - Natural voice synthesis

Creative AI Tools

ComfyUI - Visual workflow for image/video generation
Stable Diffusion XL - Image generation
AnimateDiff - Video generation
ControlNet - Guided image synthesis

🚀 Real-World Use Cases

1. Production AI Voice Agents

Powers customer support voice bots for live VoIP calls. Whisper transcribes speech, Llama 3.1 70B (quantized) generates responses, Piper TTS synthesizes voice. Total latency: <2.5 seconds.

Volume: 500+ calls/day processed entirely on local hardware.

2. Real-Time Transcription Pipeline

Batch transcription of YouTube videos, meeting recordings, and VoIP calls using Whisper Large-v3. Processes 1 hour of audio in under 5 minutes with near-perfect accuracy.

Speed: 12x real-time on GPU vs 2x on cloud APIs.

3. Smart VoIP Log Analysis

Integrated Llama 3.1 70B to analyze Asterisk logs automatically. Detects anomalies, identifies root causes, suggests fixes. No data leaves the network—critical for client confidentiality.

Impact: Reduced troubleshooting time from hours to minutes.

4. Model Fine-Tuning & Experiments

Fine-tuning Llama models for domain-specific tasks (VoIP terminology, technical support). Training on local hardware with complete control over data and hyperparameters.

Flexibility: Iterate rapidly without cloud wait times or costs.

🔧 Technical Challenges Solved

GPU Passthrough to Proxmox VM

Challenge: Proxmox doesn't natively expose GPU to VMs efficiently for AI workloads.

Solution: Configured IOMMU groups, implemented GPU passthrough with VFIO-PCI, and optimized BIOS settings for maximum performance. Achieved near-bare-metal GPU performance in VM.

VRAM Limitations (16GB)

Challenge: 16GB VRAM insufficient for full-precision 70B parameter models.

Solution: Use 4-bit quantization (GGUF format) with llama.cpp. Offload layers to system RAM when needed. Llama 3.1 70B runs at ~20 tokens/sec with this hybrid approach.

Model Storage & Management

Challenge: Dozens of models (50-100GB each) scattered across drives, hard to track versions.

Solution: Built model registry on TrueNAS with ZFS datasets. Automatic deduplication saves space, snapshots enable easy rollback. Symlinks for quick access from VMs.

Thermal Management

Challenge: 24/7 operation at full load = high heat and potential thermal throttling.

Solution: Aggressive cooling with custom fan curves, undervolting CPU/GPU for efficiency, and scheduled heavy tasks during off-peak hours (cheaper electricity, cooler ambient temps).

📊 Performance Benchmarks

Task	Local Lab	Cloud (GPU Instance)
Llama 70B Inference (1K tokens)	~50 seconds (20 tok/s)	~40 seconds (25 tok/s)
Whisper Large-v3 (1 hour audio)	~5 minutes	~8 minutes (API queuing)
Stable Diffusion XL (1 image)	~12 seconds	~15 seconds
Fine-tuning Llama 7B (1 epoch)	~45 minutes	~50 minutes

Note: Local performance within 80-100% of cloud with zero per-use costs and complete privacy.

💰 Cost Analysis

Item	Cloud (Annual)	Local (One-Time)
GPU Compute (RTX 4090 equiv)	$3,600/year	$1,800
Storage (10TB)	$1,200/year	$400
CPU + RAM	$1,500/year	$800
Electricity (24/7)	$0	~$300/year
Total (Year 1)	$6,300	$3,300
Total (Year 2+)	$6,300/year	$300/year

💡 ROI Achieved: Hardware paid for itself in 6 months. After that, essentially free compute forever (minus electricity).

🔮 Future Upgrades

🔹 Add second RTX 5070 Ti for multi-model parallel serving
🔹 Upgrade to 128GB RAM for larger context windows
🔹 Build custom voice cloning pipeline
🔹 Train domain-specific models for VoIP troubleshooting
🔹 Integrate with home automation (AI-controlled smart home)

🚀 Results

✅ Powers production AI voice agents handling 500+ calls/day
✅ Processes 100+ hours of audio transcription monthly
✅ Saves $6,000+/year vs cloud GPU instances
✅ Complete data privacy—no sensitive info leaves network
✅ Rapid experimentation with zero API limits

🔗 Related Content

Read the full setup guide: My Home AI Lab Setup — GPU Computing for Local LLMs

Local AI Development Lab - GPU-Powered Homelab