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...
Your AI. Your Data. In an era of ubiquitous cloud services, this simple principle is gaining traction among business leaders. Recent high-profile data leaks and stringent regulations have made companies increasingly wary of sending sensitive information to third-party AI platforms. A 2023 GitLab survey revealed that 95% of senior technology executives prioritize data privacy and IP protection when selecting an AI tool ( Survey: AI Adoption Faces Data Privacy, IP and Security Concerns ). Likewise, a KPMG study found 75% of executives feel AI adoption is moving faster than it should due to data privacy and ethical concerns ( The Rise of Privacy-First AI: Balancing Innovation and Data... ). Incidents like Samsung banning internal use of ChatGPT after a source code leak only underscore these fears ( Samsung Bans Staff From Using AI Like ChatGPT, Bard After Data Leak - Business Insider ). Businesses are clearly asking: How can we harness AI’s power without compromising control over our...