In our last article in this series, we discussed the importance of data privacy while using AI tools such as ChatGPT and Gemini. We've touched on some of the benefits of Local LLMs to ensure data privacy, and now we are going to dive deeper into doing so. Read on to learn more about How can you take advantage of large language models (LLMs) while mitigating privacy concerns.
Benefits of Local AI models
This may surprise you, but you can run your own large language models that do not feed your conversations to a third party (think ChatGPT). This can be accomplished with open-source models that are trained and released for anybody to use. There are many advantages to this approach:
Privacy: You achieve real privacy of your conversations by preventing data from leaving your network for processing.
Freedom: AI models as a service often come with restrictions on what they can discuss based on the way they are trained, usually to prevent liability claims against the provider. This can affect your use case and even its performance if the AI decides it is a topic that it should not engage with. In contrast, hosting your own local LLMs allow you to remove these limitations.
Customization: Every company has unique needs. On-premise AI can be tailored to fit specific tasks by finetuning models on specialized knowledge related to your field, which might be lacking on a general model like ChatGPT. But even if you are able to customize models provided by a third party, you will surely have concerns about feeding it extremely sensitive information.
Offline Usage: Local LLMs work seamlessly even in remote or isolated areas with unreliable internet access. Whether you’re in a rural clinic or a research facility deep in the mountains, your LLM remains available to talk, regardless of connectivity.
Processing Speed: Greater speeds may be achieved by utilizing smaller models or better dedicated hardware used to run the models.
Cost Effective: Initial costs will be higher for setting up local AI models, but it can be cost effective long term if you use LLMs frequently. Cloud AI model services usually charge by usage or other recurring fees that do not exist on your own managed AI, and the fees can quickly balloon depending on how you use the service.
Considerations in Implementing Local LLM Solutions
Before committing fully to an on-premises AI solution, there are a few considerations that need to be addressed:
Resource Requirements: Setting up on-premises AI demands robust hardware configurations and powerful CPUs or GPUs for fast performance. However, the requirements of these models can be reduced significantly by reducing their size with minimal tradeoff in its "Intelligence".
Maintenance: Keeping AI models running smoothly requires periodic check-ups, updates, and safety measures. It is recommended to allocate time and effort for maintenance to avoid issues and minimize downtime.
Connectivity: You will need a secure channel to access your on-premises AI model if you are away from the office. This can be achieved using a Virtual Private Network, or VPN.
The Future of Local LLMs and On-Premises AI Solutions
Local AI through open source LLMs is only in its infancy. In developments to come, we expect to see these changes in the open source and local AI landscapes:
Federated Learning: This means that multiple people can train AI models by working together, removing the need for large operations to train "Base Models”.
Data Privacy and Security: People are recognizing the importance of privacy and security in their daily lives and conversations. If confidentiality is a concern for companies, local AI models shine through in ability to be hosted on-premises. Organizations will ultimately prioritize solutions that safeguard their sensitive information.
Personalized Models: Nvidia is already working on their push for generative AI using their GPUs. We believe they will be moving towards the use of future consumer grade "AI GPUs" with AI efficient architectures that will enable and accelerate the use of local models for consumers. In the future, you may be able to download and train models to fit your personal needs and perform tasks that current cloud models are incapable or insecure to use for.
Conclusion
The use of Large Language Models, and the field of Artificial Intelligence as a whole, has seen explosive growth due to the shocking ability of perceived inference and reasoning. LLMs are already playing a big role in automating tasks that were previously not feasible such as summarization, analyzing text, providing customer support, and numerous other solutions that leverage text generation. However, there are different implementations that can be used: Cloud based models provided as a service and locally hosted models. Each have their own advantages and disadvantages, and you always want to make an informed decision when it comes to your use of AI with sensitive data. Understanding the tradeoffs you are making is crucial, and the privacy concerns with cloud AI services could be the deal breaker that pushes your business to take control by hosting your own AI models.
However, you need to understand how to best fit the models into your process. You will require a team of professionals who are knowledgeable in the constantly evolving AI technologies, such as LLMs. At Antemodal, we stay up to date with the latest advances in AI so you can be sure we will implement the best suited solutions. Contact us if you want to learn more about AI and how it can supercharge your business goals!
Want private and secure AI tools?
Contact us today to learn more.