My Home AI Lab Setup — GPU Computing for Local LLMs

From experimenting with speech-to-text to training lightweight predictive models, I've created a personal AI lab at home powered by high-end consumer hardware. The goal? Run local LLMs, real-time voice agents, VLMs, and GPU-accelerated automation workflows without relying on cloud costs.

💻 Hardware Breakdown

| Component | Model | Purpose | |-----------|-------|---------| | CPU | AMD Ryzen 9 9950X3D | Parallel inferencing & multitasking | | GPU | NVIDIA RTX 5070 Ti | CUDA compute for LLMs & VLMs | | RAM | 64GB DDR5 | Dataset handling & VRAM offload | | Storage | 10TB SSD+NVMe | Model library + training checkpoints |

🧠 Software & Tools

PyTorch + CUDA + cuDNN
ollama / vLLM for local LLMs
Whisper.cpp for speech-to-text
ComfyUI for AI video generation
Proxmox VMs for isolated services
TrueNAS SCALE for ZFS storage pools

🚀 Use Cases I Run Locally

Real-time transcription for YouTube videos
Smart monitoring for VoIP logs
AI-powered personal assistants
WebRTC voicebot experiments

🌍 Why Local Instead of Cloud?

✔ Zero monthly rental for compute
✔ Full privacy of internal tools
✔ Instant experimentation & control

Conclusion

My home AI lab continues to evolve with each project—combining real-time communication, automation, and machine learning into a unified environment.

My Home AI Lab Setup — GPU Computing for Local LLMs