Fine-Tune, Deploy & Serve Open LLMs with Crusoe Intelligence Foundry

github code: https://github.com/sourangshupal/crusoe-cloud-video-demo Managing GPU infrastructure shouldn't slow down AI development. In this video, I demonstrate how to build a complete LLM fine-tuning and deployment pipeline using Crusoe Intelligence Foundry, from dataset preparation to production inference—all without provisioning or managing GPUs. Using a synthetic financial PII dataset, we fine-tune Qwen3 8B, compare it against Llama 3.3 70B, and deploy the resulting model to a dedicated production endpoint using Crusoe's managed AI platform. What you'll learn ✅ Understand Crusoe Cloud and Crusoe Intelligence Foundry ✅ Prepare a Hugging Face dataset for supervised fine-tuning ✅ Fine-tune Qwen3 8B using Serverless Fine-Tuning ✅ Monitor training jobs and model metrics ✅ Run Serverless Inference using an OpenAI-compatible API ✅ Deploy a fine-tuned model with Self-Serve Deployments ✅ Compare dedicated deployments vs serverless inference ✅ Download LoRA adapter weights for complete portability ✅ Evaluate model performance using Detection F1 and Typed F1 Technologies Covered * Crusoe Intelligence Foundry * Crusoe Cloud * Serverless Fine-Tuning * Serverless Inference * Self-Serve Deployments * Qwen3 8B * Llama 3.3 70B * LoRA Fine-Tuning * Hugging Face Datasets * OpenAI Python SDK * Production LLM Deployment This tutorial is ideal for AI Engineers, Machine Learning Engineers, Data Scientists, and developers looking to build production-ready LLM applications without the complexity of managing GPU infrastructure. 📌 Sponsor: Crusoe Cloud 📌 Disclosure: This video is sponsored by Crusoe Cloud. All opinions and the hands-on demonstration are my own. 👍 If you enjoyed this tutorial, don't forget to Like, Share, and Subscribe for more videos on LLMs, AI Engineering, MLOps, Fine-Tuning, and Generative AI. #CrusoeCloud #CrusoeIntelligenceFoundry #FineTuning #LLM #OpenSourceAI #Qwen3 #Llama #GenerativeAI #MLOps #MachineLearning #AIEngineering