Local AI Development Lab - GPU-Powered Homelab
High-performance home AI lab with RTX 5070 Ti GPU running local LLMs, Whisper STT, and creative AI models. Powers production voice agents, saves $6K/year vs cloud, complete privacy.
<div class="space-y-6">
<div class="prose prose-invert max-w-none">
<h2>🎯 Project Overview</h2>
<p>
Built a <strong>high-performance home AI laboratory</strong> 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.
</p>
<h3>💼 Business Impact</h3>
<ul>
<li><strong>Zero cloud costs:</strong> No per-token charges, no surprise bills—complete cost control</li>
<li><strong>Privacy-first:</strong> Sensitive data never leaves the network—GDPR compliant by design</li>
<li><strong>Rapid experimentation:</strong> No API rate limits or quotas—iterate freely</li>
<li><strong>Production-ready:</strong> Powers real customer-facing AI voice agents</li>
<li><strong>ROI achieved:</strong> Hardware investment paid for itself in 6 months vs cloud costs</li>
</ul>
<h2>🛠️ Hardware Specifications</h2>
<div class="grid md:grid-cols-2 gap-4 my-4">
<div class="bg-gray-800 p-4 rounded-lg">
<h4 class="text-green-400 mb-2">🖥️ Computing Power</h4>
<ul class="space-y-2 text-sm">
<li><strong>CPU:</strong> AMD Ryzen 9 9950X3D (16C/32T)</li>
<li><strong>GPU:</strong> NVIDIA RTX 5070 Ti (16GB VRAM)</li>
<li><strong>RAM:</strong> 64GB DDR5-6000 CL30</li>
<li><strong>Purpose:</strong> Parallel LLM inference, training</li>
</ul>
</div>
<div class="bg-gray-800 p-4 rounded-lg">
<h4 class="text-blue-400 mb-2">💾 Storage System</h4>
<ul class="space-y-2 text-sm">
<li><strong>Boot:</strong> 1TB NVMe Gen4 (OS + apps)</li>
<li><strong>Models:</strong> 4TB NVMe Gen4 (model library)</li>
<li><strong>Data:</strong> 5TB SSD RAID (datasets, checkpoints)</li>
<li><strong>Backup:</strong> 10TB ZFS pool (TrueNAS)</li>
</ul>
</div>
<div class="bg-gray-800 p-4 rounded-lg">
<h4 class="text-purple-400 mb-2">🌐 Networking</h4>
<ul class="space-y-2 text-sm">
<li><strong>Internal:</strong> 10Gbps fiber between nodes</li>
<li><strong>External:</strong> 1Gbps fiber internet</li>
<li><strong>VPN:</strong> Tailscale for remote access</li>
<li><strong>VLAN:</strong> Segmented network for security</li>
</ul>
</div>
<div class="bg-gray-800 p-4 rounded-lg">
<h4 class="text-yellow-400 mb-2">❄️ Cooling & Power</h4>
<ul class="space-y-2 text-sm">
<li><strong>CPU:</strong> 360mm AIO liquid cooler</li>
<li><strong>Case:</strong> High airflow with 9x fans</li>
<li><strong>PSU:</strong> 1000W 80+ Titanium</li>
<li><strong>UPS:</strong> 1500VA for clean power</li>
</ul>
</div>
</div>
<h2>💻 Software Stack</h2>
<h3>Operating System & Virtualization</h3>
<ul>
<li><strong>Proxmox VE 8.1</strong> - Hypervisor for VM orchestration</li>
<li><strong>Ubuntu 22.04 LTS</strong> - Primary development VM</li>
<li><strong>TrueNAS SCALE</strong> - ZFS storage and backup VM</li>
<li><strong>Docker & Docker Compose</strong> - Container management</li>
</ul>
<h3>AI/ML Framework Stack</h3>
<ul>
<li><strong>PyTorch 2.1</strong> with CUDA 12.1 - Deep learning framework</li>
<li><strong>Transformers</strong> (Hugging Face) - Pre-trained models</li>
<li><strong>LangChain</strong> - LLM application framework</li>
<li><strong>ChromaDB</strong> - Vector database for RAG</li>
<li><strong>FastAPI</strong> - API server for model serving</li>
</ul>
<h3>Model Serving & Inference</h3>
<ul>
<li><strong>ollama</strong> - Easy local LLM deployment</li>
<li><strong>vLLM</strong> - High-throughput inference server</li>
<li><strong>llama.cpp</strong> - CPU/GPU hybrid inference</li>
<li><strong>Whisper.cpp</strong> - Real-time speech-to-text</li>
<li><strong>Piper TTS</strong> - Natural voice synthesis</li>
</ul>
<h3>Creative AI Tools</h3>
<ul>
<li><strong>ComfyUI</strong> - Visual workflow for image/video generation</li>
<li><strong>Stable Diffusion XL</strong> - Image generation</li>
<li><strong>AnimateDiff</strong> - Video generation</li>
<li><strong>ControlNet</strong> - Guided image synthesis</li>
</ul>
<h2>🚀 Real-World Use Cases</h2>
<div class="bg-gray-800 p-4 rounded-lg my-4">
<h4 class="text-green-400 mb-2">1. Production AI Voice Agents</h4>
<p>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.</p>
<p><strong>Volume:</strong> 500+ calls/day processed entirely on local hardware.</p>
</div>
<div class="bg-gray-800 p-4 rounded-lg my-4">
<h4 class="text-blue-400 mb-2">2. Real-Time Transcription Pipeline</h4>
<p>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.</p>
<p><strong>Speed:</strong> 12x real-time on GPU vs 2x on cloud APIs.</p>
</div>
<div class="bg-gray-800 p-4 rounded-lg my-4">
<h4 class="text-purple-400 mb-2">3. Smart VoIP Log Analysis</h4>
<p>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.</p>
<p><strong>Impact:</strong> Reduced troubleshooting time from hours to minutes.</p>
</div>
<div class="bg-gray-800 p-4 rounded-lg my-4">
<h4 class="text-yellow-400 mb-2">4. Model Fine-Tuning & Experiments</h4>
<p>Fine-tuning Llama models for domain-specific tasks (VoIP terminology, technical support). Training on local hardware with complete control over data and hyperparameters.</p>
<p><strong>Flexibility:</strong> Iterate rapidly without cloud wait times or costs.</p>
</div>
<h2>🔧 Technical Challenges Solved</h2>
<div class="border-l-4 border-yellow-500 pl-4 my-4">
<h4 class="font-bold">GPU Passthrough to Proxmox VM</h4>
<p><strong>Challenge:</strong> Proxmox doesn't natively expose GPU to VMs efficiently for AI workloads.</p>
<p><strong>Solution:</strong> Configured IOMMU groups, implemented GPU passthrough with VFIO-PCI, and optimized BIOS settings for maximum performance. Achieved near-bare-metal GPU performance in VM.</p>
</div>
<div class="border-l-4 border-yellow-500 pl-4 my-4">
<h4 class="font-bold">VRAM Limitations (16GB)</h4>
<p><strong>Challenge:</strong> 16GB VRAM insufficient for full-precision 70B parameter models.</p>
<p><strong>Solution:</strong> 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.</p>
</div>
<div class="border-l-4 border-yellow-500 pl-4 my-4">
<h4 class="font-bold">Model Storage & Management</h4>
<p><strong>Challenge:</strong> Dozens of models (50-100GB each) scattered across drives, hard to track versions.</p>
<p><strong>Solution:</strong> Built model registry on TrueNAS with ZFS datasets. Automatic deduplication saves space, snapshots enable easy rollback. Symlinks for quick access from VMs.</p>
</div>
<div class="border-l-4 border-yellow-500 pl-4 my-4">
<h4 class="font-bold">Thermal Management</h4>
<p><strong>Challenge:</strong> 24/7 operation at full load = high heat and potential thermal throttling.</p>
<p><strong>Solution:</strong> Aggressive cooling with custom fan curves, undervolting CPU/GPU for efficiency, and scheduled heavy tasks during off-peak hours (cheaper electricity, cooler ambient temps).</p>
</div>
<h2>📊 Performance Benchmarks</h2>
<table class="w-full my-4">
<thead class="bg-gray-700">
<tr>
<th class="p-3 text-left">Task</th>
<th class="p-3 text-left">Local Lab</th>
<th class="p-3 text-left">Cloud (GPU Instance)</th>
</tr>
</thead>
<tbody class="divide-y divide-gray-700">
<tr>
<td class="p-3">Llama 70B Inference (1K tokens)</td>
<td class="p-3 text-green-400">~50 seconds (20 tok/s)</td>
<td class="p-3">~40 seconds (25 tok/s)</td>
</tr>
<tr>
<td class="p-3">Whisper Large-v3 (1 hour audio)</td>
<td class="p-3 text-green-400">~5 minutes</td>
<td class="p-3">~8 minutes (API queuing)</td>
</tr>
<tr>
<td class="p-3">Stable Diffusion XL (1 image)</td>
<td class="p-3 text-green-400">~12 seconds</td>
<td class="p-3">~15 seconds</td>
</tr>
<tr>
<td class="p-3">Fine-tuning Llama 7B (1 epoch)</td>
<td class="p-3 text-green-400">~45 minutes</td>
<td class="p-3">~50 minutes</td>
</tr>
</tbody>
</table>
<p class="text-sm text-gray-400">Note: Local performance within 80-100% of cloud with zero per-use costs and complete privacy.</p>
<h2>💰 Cost Analysis</h2>
<table class="w-full my-4">
<thead class="bg-gray-700">
<tr>
<th class="p-3 text-left">Item</th>
<th class="p-3 text-left">Cloud (Annual)</th>
<th class="p-3 text-left">Local (One-Time)</th>
</tr>
</thead>
<tbody class="divide-y divide-gray-700">
<tr>
<td class="p-3">GPU Compute (RTX 4090 equiv)</td>
<td class="p-3">$3,600/year</td>
<td class="p-3 text-green-400">$1,800</td>
</tr>
<tr>
<td class="p-3">Storage (10TB)</td>
<td class="p-3">$1,200/year</td>
<td class="p-3 text-green-400">$400</td>
</tr>
<tr>
<td class="p-3">CPU + RAM</td>
<td class="p-3">$1,500/year</td>
<td class="p-3 text-green-400">$800</td>
</tr>
<tr>
<td class="p-3">Electricity (24/7)</td>
<td class="p-3">$0</td>
<td class="p-3">~$300/year</td>
</tr>
<tr class="bg-gray-700 font-bold">
<td class="p-3">Total (Year 1)</td>
<td class="p-3">$6,300</td>
<td class="p-3 text-green-400">$3,300</td>
</tr>
<tr class="bg-gray-700 font-bold">
<td class="p-3">Total (Year 2+)</td>
<td class="p-3">$6,300/year</td>
<td class="p-3 text-green-400">$300/year</td>
</tr>
</tbody>
</table>
<p class="p-4 bg-green-900/30 border-l-4 border-green-500 rounded my-4">
💡 <strong>ROI Achieved:</strong> Hardware paid for itself in 6 months. After that, essentially free compute forever (minus electricity).
</p>
<h2>🔮 Future Upgrades</h2>
<ul>
<li>🔹 Add second RTX 5070 Ti for multi-model parallel serving</li>
<li>🔹 Upgrade to 128GB RAM for larger context windows</li>
<li>🔹 Build custom voice cloning pipeline</li>
<li>🔹 Train domain-specific models for VoIP troubleshooting</li>
<li>🔹 Integrate with home automation (AI-controlled smart home)</li>
</ul>
<h2>🚀 Results</h2>
<ul>
<li>✅ Powers <strong>production AI voice agents</strong> handling 500+ calls/day</li>
<li>✅ Processes <strong>100+ hours</strong> of audio transcription monthly</li>
<li>✅ Saves <strong>$6,000+/year</strong> vs cloud GPU instances</li>
<li>✅ Complete <strong>data privacy</strong>—no sensitive info leaves network</li>
<li>✅ Rapid experimentation with <strong>zero API limits</strong></li>
</ul>
<h2>🔗 Related Content</h2>
<p>Read the full setup guide: <a href="/blog/home-ai-lab-setup-gpu-computing-local-llms" class="text-blue-400 hover:underline">My Home AI Lab Setup — GPU Computing for Local LLMs</a></p>
</div>
</div>
<div class="prose prose-invert max-w-none">
<h2>🎯 Project Overview</h2>
<p>
Built a <strong>high-performance home AI laboratory</strong> 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.
</p>
<h3>💼 Business Impact</h3>
<ul>
<li><strong>Zero cloud costs:</strong> No per-token charges, no surprise bills—complete cost control</li>
<li><strong>Privacy-first:</strong> Sensitive data never leaves the network—GDPR compliant by design</li>
<li><strong>Rapid experimentation:</strong> No API rate limits or quotas—iterate freely</li>
<li><strong>Production-ready:</strong> Powers real customer-facing AI voice agents</li>
<li><strong>ROI achieved:</strong> Hardware investment paid for itself in 6 months vs cloud costs</li>
</ul>
<h2>🛠️ Hardware Specifications</h2>
<div class="grid md:grid-cols-2 gap-4 my-4">
<div class="bg-gray-800 p-4 rounded-lg">
<h4 class="text-green-400 mb-2">🖥️ Computing Power</h4>
<ul class="space-y-2 text-sm">
<li><strong>CPU:</strong> AMD Ryzen 9 9950X3D (16C/32T)</li>
<li><strong>GPU:</strong> NVIDIA RTX 5070 Ti (16GB VRAM)</li>
<li><strong>RAM:</strong> 64GB DDR5-6000 CL30</li>
<li><strong>Purpose:</strong> Parallel LLM inference, training</li>
</ul>
</div>
<div class="bg-gray-800 p-4 rounded-lg">
<h4 class="text-blue-400 mb-2">💾 Storage System</h4>
<ul class="space-y-2 text-sm">
<li><strong>Boot:</strong> 1TB NVMe Gen4 (OS + apps)</li>
<li><strong>Models:</strong> 4TB NVMe Gen4 (model library)</li>
<li><strong>Data:</strong> 5TB SSD RAID (datasets, checkpoints)</li>
<li><strong>Backup:</strong> 10TB ZFS pool (TrueNAS)</li>
</ul>
</div>
<div class="bg-gray-800 p-4 rounded-lg">
<h4 class="text-purple-400 mb-2">🌐 Networking</h4>
<ul class="space-y-2 text-sm">
<li><strong>Internal:</strong> 10Gbps fiber between nodes</li>
<li><strong>External:</strong> 1Gbps fiber internet</li>
<li><strong>VPN:</strong> Tailscale for remote access</li>
<li><strong>VLAN:</strong> Segmented network for security</li>
</ul>
</div>
<div class="bg-gray-800 p-4 rounded-lg">
<h4 class="text-yellow-400 mb-2">❄️ Cooling & Power</h4>
<ul class="space-y-2 text-sm">
<li><strong>CPU:</strong> 360mm AIO liquid cooler</li>
<li><strong>Case:</strong> High airflow with 9x fans</li>
<li><strong>PSU:</strong> 1000W 80+ Titanium</li>
<li><strong>UPS:</strong> 1500VA for clean power</li>
</ul>
</div>
</div>
<h2>💻 Software Stack</h2>
<h3>Operating System & Virtualization</h3>
<ul>
<li><strong>Proxmox VE 8.1</strong> - Hypervisor for VM orchestration</li>
<li><strong>Ubuntu 22.04 LTS</strong> - Primary development VM</li>
<li><strong>TrueNAS SCALE</strong> - ZFS storage and backup VM</li>
<li><strong>Docker & Docker Compose</strong> - Container management</li>
</ul>
<h3>AI/ML Framework Stack</h3>
<ul>
<li><strong>PyTorch 2.1</strong> with CUDA 12.1 - Deep learning framework</li>
<li><strong>Transformers</strong> (Hugging Face) - Pre-trained models</li>
<li><strong>LangChain</strong> - LLM application framework</li>
<li><strong>ChromaDB</strong> - Vector database for RAG</li>
<li><strong>FastAPI</strong> - API server for model serving</li>
</ul>
<h3>Model Serving & Inference</h3>
<ul>
<li><strong>ollama</strong> - Easy local LLM deployment</li>
<li><strong>vLLM</strong> - High-throughput inference server</li>
<li><strong>llama.cpp</strong> - CPU/GPU hybrid inference</li>
<li><strong>Whisper.cpp</strong> - Real-time speech-to-text</li>
<li><strong>Piper TTS</strong> - Natural voice synthesis</li>
</ul>
<h3>Creative AI Tools</h3>
<ul>
<li><strong>ComfyUI</strong> - Visual workflow for image/video generation</li>
<li><strong>Stable Diffusion XL</strong> - Image generation</li>
<li><strong>AnimateDiff</strong> - Video generation</li>
<li><strong>ControlNet</strong> - Guided image synthesis</li>
</ul>
<h2>🚀 Real-World Use Cases</h2>
<div class="bg-gray-800 p-4 rounded-lg my-4">
<h4 class="text-green-400 mb-2">1. Production AI Voice Agents</h4>
<p>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.</p>
<p><strong>Volume:</strong> 500+ calls/day processed entirely on local hardware.</p>
</div>
<div class="bg-gray-800 p-4 rounded-lg my-4">
<h4 class="text-blue-400 mb-2">2. Real-Time Transcription Pipeline</h4>
<p>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.</p>
<p><strong>Speed:</strong> 12x real-time on GPU vs 2x on cloud APIs.</p>
</div>
<div class="bg-gray-800 p-4 rounded-lg my-4">
<h4 class="text-purple-400 mb-2">3. Smart VoIP Log Analysis</h4>
<p>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.</p>
<p><strong>Impact:</strong> Reduced troubleshooting time from hours to minutes.</p>
</div>
<div class="bg-gray-800 p-4 rounded-lg my-4">
<h4 class="text-yellow-400 mb-2">4. Model Fine-Tuning & Experiments</h4>
<p>Fine-tuning Llama models for domain-specific tasks (VoIP terminology, technical support). Training on local hardware with complete control over data and hyperparameters.</p>
<p><strong>Flexibility:</strong> Iterate rapidly without cloud wait times or costs.</p>
</div>
<h2>🔧 Technical Challenges Solved</h2>
<div class="border-l-4 border-yellow-500 pl-4 my-4">
<h4 class="font-bold">GPU Passthrough to Proxmox VM</h4>
<p><strong>Challenge:</strong> Proxmox doesn't natively expose GPU to VMs efficiently for AI workloads.</p>
<p><strong>Solution:</strong> Configured IOMMU groups, implemented GPU passthrough with VFIO-PCI, and optimized BIOS settings for maximum performance. Achieved near-bare-metal GPU performance in VM.</p>
</div>
<div class="border-l-4 border-yellow-500 pl-4 my-4">
<h4 class="font-bold">VRAM Limitations (16GB)</h4>
<p><strong>Challenge:</strong> 16GB VRAM insufficient for full-precision 70B parameter models.</p>
<p><strong>Solution:</strong> 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.</p>
</div>
<div class="border-l-4 border-yellow-500 pl-4 my-4">
<h4 class="font-bold">Model Storage & Management</h4>
<p><strong>Challenge:</strong> Dozens of models (50-100GB each) scattered across drives, hard to track versions.</p>
<p><strong>Solution:</strong> Built model registry on TrueNAS with ZFS datasets. Automatic deduplication saves space, snapshots enable easy rollback. Symlinks for quick access from VMs.</p>
</div>
<div class="border-l-4 border-yellow-500 pl-4 my-4">
<h4 class="font-bold">Thermal Management</h4>
<p><strong>Challenge:</strong> 24/7 operation at full load = high heat and potential thermal throttling.</p>
<p><strong>Solution:</strong> Aggressive cooling with custom fan curves, undervolting CPU/GPU for efficiency, and scheduled heavy tasks during off-peak hours (cheaper electricity, cooler ambient temps).</p>
</div>
<h2>📊 Performance Benchmarks</h2>
<table class="w-full my-4">
<thead class="bg-gray-700">
<tr>
<th class="p-3 text-left">Task</th>
<th class="p-3 text-left">Local Lab</th>
<th class="p-3 text-left">Cloud (GPU Instance)</th>
</tr>
</thead>
<tbody class="divide-y divide-gray-700">
<tr>
<td class="p-3">Llama 70B Inference (1K tokens)</td>
<td class="p-3 text-green-400">~50 seconds (20 tok/s)</td>
<td class="p-3">~40 seconds (25 tok/s)</td>
</tr>
<tr>
<td class="p-3">Whisper Large-v3 (1 hour audio)</td>
<td class="p-3 text-green-400">~5 minutes</td>
<td class="p-3">~8 minutes (API queuing)</td>
</tr>
<tr>
<td class="p-3">Stable Diffusion XL (1 image)</td>
<td class="p-3 text-green-400">~12 seconds</td>
<td class="p-3">~15 seconds</td>
</tr>
<tr>
<td class="p-3">Fine-tuning Llama 7B (1 epoch)</td>
<td class="p-3 text-green-400">~45 minutes</td>
<td class="p-3">~50 minutes</td>
</tr>
</tbody>
</table>
<p class="text-sm text-gray-400">Note: Local performance within 80-100% of cloud with zero per-use costs and complete privacy.</p>
<h2>💰 Cost Analysis</h2>
<table class="w-full my-4">
<thead class="bg-gray-700">
<tr>
<th class="p-3 text-left">Item</th>
<th class="p-3 text-left">Cloud (Annual)</th>
<th class="p-3 text-left">Local (One-Time)</th>
</tr>
</thead>
<tbody class="divide-y divide-gray-700">
<tr>
<td class="p-3">GPU Compute (RTX 4090 equiv)</td>
<td class="p-3">$3,600/year</td>
<td class="p-3 text-green-400">$1,800</td>
</tr>
<tr>
<td class="p-3">Storage (10TB)</td>
<td class="p-3">$1,200/year</td>
<td class="p-3 text-green-400">$400</td>
</tr>
<tr>
<td class="p-3">CPU + RAM</td>
<td class="p-3">$1,500/year</td>
<td class="p-3 text-green-400">$800</td>
</tr>
<tr>
<td class="p-3">Electricity (24/7)</td>
<td class="p-3">$0</td>
<td class="p-3">~$300/year</td>
</tr>
<tr class="bg-gray-700 font-bold">
<td class="p-3">Total (Year 1)</td>
<td class="p-3">$6,300</td>
<td class="p-3 text-green-400">$3,300</td>
</tr>
<tr class="bg-gray-700 font-bold">
<td class="p-3">Total (Year 2+)</td>
<td class="p-3">$6,300/year</td>
<td class="p-3 text-green-400">$300/year</td>
</tr>
</tbody>
</table>
<p class="p-4 bg-green-900/30 border-l-4 border-green-500 rounded my-4">
💡 <strong>ROI Achieved:</strong> Hardware paid for itself in 6 months. After that, essentially free compute forever (minus electricity).
</p>
<h2>🔮 Future Upgrades</h2>
<ul>
<li>🔹 Add second RTX 5070 Ti for multi-model parallel serving</li>
<li>🔹 Upgrade to 128GB RAM for larger context windows</li>
<li>🔹 Build custom voice cloning pipeline</li>
<li>🔹 Train domain-specific models for VoIP troubleshooting</li>
<li>🔹 Integrate with home automation (AI-controlled smart home)</li>
</ul>
<h2>🚀 Results</h2>
<ul>
<li>✅ Powers <strong>production AI voice agents</strong> handling 500+ calls/day</li>
<li>✅ Processes <strong>100+ hours</strong> of audio transcription monthly</li>
<li>✅ Saves <strong>$6,000+/year</strong> vs cloud GPU instances</li>
<li>✅ Complete <strong>data privacy</strong>—no sensitive info leaves network</li>
<li>✅ Rapid experimentation with <strong>zero API limits</strong></li>
</ul>
<h2>🔗 Related Content</h2>
<p>Read the full setup guide: <a href="/blog/home-ai-lab-setup-gpu-computing-local-llms" class="text-blue-400 hover:underline">My Home AI Lab Setup — GPU Computing for Local LLMs</a></p>
</div>
</div>