Question: Is there a way to fine-tune large language models while keeping data private and in control, without relying on GPUs?

If you want to fine-tune large language models without relying on GPUs and want to keep your data private and in your control, Numenta could be an interesting choice. The platform lets businesses run AI models on CPUs, with features like real-time performance optimization, multi-tenancy and easy scaling. Numenta also supports fine-tuning generative and non-generative LLMs on CPUs, which is particularly useful for gaming and customer support.

Another interesting platform is ThirdAI, which makes large language models usable and customizable without requiring special hardware. It offers features like document intelligence, customer experience enhancement and generative AI for summarizing long documents. ThirdAI is designed to be integrated with existing processes and infrastructure with a simple interface and tiered pricing plans, offering a flexible option for different organizational needs.

For those concerned about data sovereignty and independence from third-party providers, Prem is a development environment for personalized LLMs that can be trained on-premise. The platform handles complex tasks like prompt engineering and deployment, so companies can keep sensitive data in house. Prem offers customization options and a library of open-source Small Language Models (SLMs) for different use cases, which can greatly improve compliance management and other business processes.

Last, ClearGPT is an enterprise-grade platform for internal use that focuses on security, performance and data governance. It has zero data leakage and maximum control over corporate IP, with features like role-based access and a continuous feedback loop. ClearGPT offers the highest model performance and customization, so it's a good option for companies that want to build AI into their operations without vendor lock-ins.