Local LLM inference – impressive but too hard to work with
a year ago
- #Local Inference
- #LLM
- #Edge Computing
- Local LLM inference is possible but not yet production-ready.
- Compute trends are shifting back towards the edge (local devices) due to benefits like cost, privacy, speed, and offline use.
- Frameworks tested include llama.cpp, Ollama, and WebLLM, with llama.cpp and Ollama showing the best performance.
- Performance metrics show local inference is slower than cloud solutions like OpenAI's GPT-4.0-mini.
- Challenges include finding and deploying the right model for specific tasks and the large size of models leading to slow downloads.
- Future solutions need to simplify training and deploying small, task-specific models and integrate seamlessly with cloud LLMs.