We are looking to build a privacy-focused voice assistant. Is it possible to run Pipecat on hardware like a Raspberry Pi or NVIDIA Jetson? We want to know if the framework is light enough to handle the audio routing and local LLM execution without relying on cloud services, specifically focusing on the optimization of the audio transport layer.
3 answers
Running this on the edge is definitely feasible, but you have to be very selective about your components. On a Jetson Orin, it performs beautifully because you can leverage the GPU for the VAD and the local LLM inference. The framework itself is written in Python, but the heavy lifting in the transport layer is quite efficient. The main hurdle is the memory footprint of the speech-to-text model. I recommend using Faster-Whisper alongside the framework to keep the latency under 500ms. It's a great setup for industrial environments where constant internet connectivity isn't guaranteed.
Have you noticed any significant thermal throttling on the Raspberry Pi 5 when running the full pipeline? I am worried about the long-term stability of the audio stream.
It works well if you use the right Docker containers. There are some community-built images specifically optimized for ARM64 architectures that make the deployment very smooth.
That’s a good tip, Edward. Using those optimized images also helps in managing the complex C++ dependencies that some of the underlying audio libraries require during installation.
Ronald, the Pi 5 does get hot, but if you use an active cooler and stick to quantized models (like 4-bit GGUF), the framework's overhead is negligible. The stability is actually quite high as long as you don't oversubscribe the CPU cores with too many concurrent threads.