summary: “Edge computing processes data near the source to reduce latency and bandwidth, exemplified by CPU-optimized AI models like Kitten TTS and Google Gemma 4.” updated: 2026-04-14 group: research-practice-sensemaking
Edge Computing
Summary
Edge computing processes data near the source to reduce latency and bandwidth, exemplified by CPU-optimized AI models like Kitten TTS and Google Gemma 4.
Seed Sources
Key Examples
- Kitten TTS: Open-source, CPU-optimized text-to-speech framework (model size <25MB) for edge deployment and browser-based applications, developed by Kitten ML.
- Reviewed on Sam Witteveen channel (2026-04-14).
- Focus on extreme efficiency and small file sizes.
- Video review available: CPU optimised TTS - Kitten AI.
Source Notes
- 2026-04-08: Llama.cpp: Local LLM Inference for Accessible, Private AI Clip title: What Is Llama.cpp? The LLM Inference Engine for Local AI Author / channel: IBM Technology URL: https://www.youtube.com/watch?v=P8m5eHAyrFM Summary The video introduces LLama C++, an open-sour (Llama.cpp: Local LLM Inference for Accessible, Private AI)
- 2026-04-10: Llama.cpp: Local LLM Inference for Accessible, Private AI Clip title: What Is Llama.cpp? The LLM Inference Engine for Local AI Author / channel: IBM Technology *URL: (Llamacpp Local LLM Inference for Accessible Private AI)