Large Language Models (LLMs) like GPT-4 have taken the business world by storm. Yet many assume these powerful AI tools can only run in the cloud or on specialized supercomputers. In reality, a new trend is emerging: running LLMs on commodity hardware – the kind of servers and devices many companies already own or can easily acquire. Business leaders are paying attention because this approach promises greater privacy, regulatory compliance, and long-term cost savings . In this deep dive, we explore why organizations are bringing AI in-house, how they’re optimizing models for local deployment, and what trade-offs to consider. We’ll also share industry research and real-life examples of businesses gaining an edge with local AI. The Shift Toward Local AI Solutions in Business Enterprise adoption of AI is accelerating across the globe. A May 2024 McKinsey survey reported that 65% of organizations are now regularly using generative AI, nearly double the share from ten months prior ( Get...
Healthcare organizations manage some of the most sensitive data around – patient health information. In an age of AI-driven solutions, protecting that data is paramount. Business leaders in healthcare are now exploring local AI deployments as a way to enhance privacy, maintain compliance, and keep control of their data. This deep-dive looks at why data privacy is so critical in healthcare, how AI impacts regulatory compliance (HIPAA, GDPR and more), and why many providers are turning to local AI. We’ll also compare local vs cloud AI (and the risks of cloud-based approaches), highlight case studies of local AI adoption for patient data security, and see how Software Tailor’s approach – “Your AI. Your Data.” – stacks up against other solutions. The Importance of Data Privacy in Healthcare Protecting patient data isn’t just an ethical duty – it’s a legal imperative. Health records contain personal identifiers, medical histories, diagnoses, and financial details. If this information i...