Skip to main content

Posts

Technical Insight: Running Large Language Models on Commodity Hardware

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...
Recent posts

Your AI. Your Data: The Case for On-Premises AI in a Privacy-Focused Era

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...

Enterprise AI Governance 101: Policies for Responsible AI Deployment

Introduction to Enterprise AI Governance Enterprise AI governance refers to the policies and frameworks that ensure artificial intelligence is used responsibly and effectively within an organization. As businesses increasingly adopt AI solutions, executives are recognizing that strong governance is not a “nice to have” but a critical requirement. In fact, a recent survey found 95% of organizations plan to update or replace their AI governance frameworks to meet evolving expectations for responsible AI ( AI leaders reveal responsible AI governance insights | Domino Data Lab ). This comes as no surprise: while 75% of enterprises are implementing AI, 72% report major data quality and scaling issues in their AI initiatives ( F5 Study: Enterprises Plowing Ahead with AI Deployment Despite Gaps in Data Governance and Security Concerns | F5 ). Without proper governance, AI projects can run into compliance problems, biased outcomes, security breaches, or simply fail to deliver ROI. For busi...