Organizations Using litellm for Lightweight LLM Inference, Edge Deployment, and Scalable On-Device AI
Explore organizations tagged with the 'litellm' tag to discover projects and teams implementing lightweight LLM inference, on-device AI, and edge deployment patterns. This curated list of organizations (nav: organizations; pillar: tags) highlights real-world litellm usage in production — including model quantization, low-latency inference, memory-efficient architectures, PyTorch/ONNX interoperability, and server-to-edge pipelines — enabling you to compare performance, integration approaches, and deployment costs. Use the filtering UI to narrow results by deployment target, benchmark metrics, license, and contribution activity; view organization profiles to access code, documentation, reproducible benchmarks, and contact info. Gain actionable insights on optimizing inference latency, model size, and cost-per-inference, then filter, evaluate case studies, or connect with teams adopting litellm.
