How to Deploy Lightweight Language Models on Embedded Linux with LiteLLM
7 hours ago
- #Local Inference
- #AI
- #Embedded Systems
- LiteLLM enables local AI inference on resource-constrained devices, reducing latency and improving data privacy.
- Setup requires a Linux-based OS, Python 3.7+, and internet access for package downloads.
- Install LiteLLM in a virtual environment using pip and configure it via a YAML file.
- Ollama is used to host LLMs locally, with models like codegemma:2b being pulled for local use.
- Launch the LiteLLM proxy server to make local AI models accessible via a consistent API.
- Test the deployment with a Python script to ensure the setup is working correctly.
- Optimize performance by choosing lightweight models like DistilBERT, TinyBERT, or MobileBERT.
- Adjust LiteLLM settings such as max_tokens and max_parallel_requests to enhance performance on embedded devices.
- Secure the setup with firewalls and authentication, and monitor performance using LiteLLM's logging capabilities.
- LiteLLM simplifies deploying AI solutions on embedded systems, enabling real-time features at the edge.