Hey everyone,
I wanted to share a quick update on my real-time talking avatar implementation using LiveKit, along with a few technical takeaways for anyone pushing CPU-bound media pipelines to their limit.
I’m currently running my stack on a bare-metal Intel Xeon server without dedicated GPU acceleration. To get a real-time conversational agent up and running, I pushed as much offloading to the client side as possible:
- Client-Side STT/TTS & VAD: Processed user audio, voice activity, and input directly on the user’s device/browser.
- Push-to-Talk Flow: Minimizing continuous streaming overhead on the agent side.
- LiveKit Egress/Ingress: Routing client-processed audio and streams back through the agent framework.
While this architectural split successfully got the pipeline working, the host CPU load remained steep when orchestrating the avatar state, tensor processing, and real-time frame generation. Under sustained interactions, the system would run out of compute headroom, throwing underlying matrix/tensor computation failures (exit codes 4 / 7 — indicating core mathematical calculation/instruction overhead bottlenecks on the CPU).
Next Steps: I’ve decided to put active development of the real-time talking avatar on pause until I drop a dedicated GPU into the bare-metal host. The LiveKit media routing and WebRTC pipeline handled their end gracefully, but generating dynamic avatar frames without CUDA/hardware-accelerated matrix math simply puts too much strain on host CPU resources for a smooth production experience.
Once the GPU is installed and matrix ops are offloaded to CUDA, I’ll bring the avatar back online!
Huge thanks to the community for the ongoing work on the Python/Node AI agents SDKs.