Fast and secure ingress to remote AIs with ngrok, Deepseek, and Ollama

This blog post provides an updated guide on setting up and validating large language models (LLMs) like Deepseek-R1 using a combination of local and remote resources to balance simplicity, cost, and efficiency. It discusses the advantages and challenges of developing LLMs locally versus using hosted or self-hosted remote compute solutions, highlighting the constraints of local development due to hardware limitations and collaboration difficulties. The text recommends a tech stack involving a Linux virtual machine with GPU acceleration, Ollama for managing LLM operations, and ngrok for secure and persistent access to remote services, facilitating a quick and effective setup for testing and collaborating on AI models without broad internet exposure. The guide outlines the process of launching a remote VM, installing necessary tools, and securely connecting them to external services, emphasizing the importance of implementing access control measures to protect the models from unauthorized use. Additionally, it invites users to further explore security options and engage with the community for ongoing support and learning opportunities.

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