Artificial intelligence (AI) is becoming increasingly ubiquitous, expanding from traditional computing devices to encompass robots, self-driving vehicles, and various digital environments. Jensen Huang, CEO of NVIDIA, highlighted this trend during the recent GTC conference.
Growth of Generative AI
Recent data indicates that generative AI is rapidly gaining traction, with 75% of knowledge workers leveraging it for tasks such as content creation and code automation, according to Accenture. As organizations navigate the deployment of AI, a well-defined, centralized AI strategy emerges as crucial. Different applications necessitate specific models and processes, as well as access to high-quality data to optimize efficiency and productivity.
Challenges in AI Implementation
In addition to productivity gains, organizations must remain vigilant about budgets and data security during AI project execution. The rollout of AI technologies comes with its own set of challenges, including skill shortages that can hinder effective implementation.
Decision-makers face various factors when considering AI workloads, leading to increased complexity despite the appeal of having more choices. Many organizations turn to large language models (LLMs), which require extensive training, fine-tuning, and optimization, but they may opt to utilize LLMs managed by public cloud providers or run their selected models on external platforms.
On-Premises vs. Cloud Deployment
While public cloud solutions can expedite deployment timelines, they often come with drawbacks such as unpredictable costs, latency, and concerns regarding data security and sovereignty. In contrast, on-premises infrastructure allows organizations to maintain control over all aspects of their AI deployment, particularly crucial for managing sensitive data and protecting intellectual property.
Many businesses are increasingly aligning their operations with data security regulations, which can stipulate that information must remain within specific geographic boundaries. By running AI workloads locally, organizations can ensure compliance and avoid unnecessary data transfers.
Cost-Effectiveness of On-Premises Solutions
Research from Enterprise Strategy Group (ESG) reveals that on-premises deployments can significantly reduce costs compared to public cloud solutions. A recent study assessed the expenses associated with a text-based chatbot powered by a 70 billion parameter open-source LLM utilizing retrieval-augmented generation (RAG) on-premises, versus using a comparable solution from Amazon Web Services.
The findings indicated that on-premises implementation could be up to 62% more cost-effective for supporting inferencing tasks. Furthermore, compared to an API-based service like OpenAI, the on-premises approach proved to be as much as 75% more economical, although potential savings may vary based on specific use cases.
Innovations in AI Infrastructure
The on-premises solution discussed in the ESG study featured the Dell AI Factory, which integrates Dell hardware with professional services and connects to a diverse software ecosystem to support current and future AI applications. This modern approach is designed to help organizations effectively scale their AI initiatives and improve overall business outcomes.
Establishing a centralized strategy for AI deployment is essential, and the Dell AI Factory aims to guide organizations throughout their AI journey.