Open Source Inferencing

Open source inference frameworks for large language models (LLMs) provide flexible, efficient, and cost-effective solutions to deploy, serve, and optimize AI models across diverse hardware environments. Popular tools like Ollama offer user-friendly, cross-platform local inference with customization and offline operation, while vLLM delivers high-throughput, low-latency inference optimized for cloud and edge deployments. Other frameworks such as LocalAI and OpenLLM cater to seamless API integration and scaling for multi-user scenarios. These frameworks typically support numerous model architectures, promote performance optimizations like dynamic batching and concurrency, and facilitate easy model management, enabling developers and researchers to run powerful LLMs efficiently without relying solely on proprietary cloud services. Overall, they empower broader access to advanced language AI through community-driven innovation and adaptable infrastructure

📄️ Open Source Inferencing

The Open Source Inferencing feature enables users to leverage powerful, community-driven AI models for real-time inference without vendor lock-in. By integrating open source models and tools, users gain flexibility, transparency, and control over their inferencing pipelines, enabling customization and collaboration.This empowers developers and data scientists to:

📄️ User guide

This guide will help you navigate the platform’s LLM Tools section to use pre-trained open source models for inference. Follow these steps to select a model, input your query data, and understand the results.