How to Deploy and Scale Generative AI Efficiently and Cost-Effectively – SPONSOR CONTENT FROM AWS & NVIDIA

For business leaders and developers alike, the question isn’t why generative artificial intelligence is being deployed across industries, but how—and how can we put it to work faster and with high performance?

The launch of ChatGPT in November 2022 marked the beginning of the large language model (LLM) explosion among end-users. LLMs are trained on vast amounts of data while providing the versatility and flexibility to simultaneously perform such tasks as answering questions, summarizing documents, and translating languages.

Today, organizations seek generative AI solutions to delight customers and empower in-house teams in equal measure. However, only 10% of companies worldwide are using generative AI at scale, according to McKinsey’s State of AI in early 2024 survey.

To continue to develop cutting-edge services and stay ahead of the competition, organizations must deploy and scale high-performance generative AI models and workloads securely, efficiently, and cost-effectively.

Accelerating Reinvention

Business leaders are realizing the true value of generative AI as it takes root across multiple industries. Organizations adopting LLMs and generative AI are 2.6 times more likely to increase revenue by at least 10%, according to Accenture.

However, as many as 30% of generative AI projects will be abandoned after proof of concept by 2025 due to poor data quality, inadequate risk controls, escalating costs, or unclear business value, according to Gartner. Much of the blame lies with the complexity of deploying large-scale generative AI capabilities.

Deployment Considerations

Not all generative AI services are created equal. Generative AI models are tailored to handle different tasks. Most organizations need a variety of models to generate text, images, video, speech, and synthetic data. They often choose between two approaches to deploying models:

1. Models built, trained, and deployed on easy-to-use third-party managed services.

2. Self-hosted solutions that rely on open-source and commercial tools.

Managed services are easy to set up and include user-friendly application programming interfaces (APIs) with robust model choices to build secure AI applications.

Self-hosted solutions require custom coding for APIs and further adjustment based on existing infrastructure. And organizations that choose this approach must factor in ongoing maintenance and updates to foundation models.

Ensuring an optimal user experience with high throughput, low latency, and security is often difficult to achieve on existing self-hosted solutions, where high throughput denotes the ability to process large volumes of data efficiently and low latency refers to the minimal delay in data transmission and real-time interaction.

Whichever approach an organization adopts, improving inference performance and keeping data secure is a complex, computationally intensive, and often time-consuming task.

Project Efficiency

Organizations face a few barriers when deploying generative AI and LLMs at scale. If not dealt with swiftly or efficiently, project progress and implementation timelines could be significantly delayed. Key considerations include:

Achieving low latency and high throughput. To ensure a good user experience, organizations need to respond to requests quickly and maintain high token throughput to scale effectively.

Consistency. Secure, stable, standardized inference platforms are a priority for most developers, who value an easy-to-use solution with consistent APIs.

Data security. Organizations must protect company data, client confidentiality, and personally identifiable information (PII) according to in-house policies and industry regulations.

Only by overcoming these challenges can organizations unleash generative AI and LLMs at scale.

Inference Microservices

To get ahead of the competition, developers need to find cost-efficient ways to enable the rapid, reliable, and secure deployment of high-performance generative AI and LLM models. An important measurement for cost efficiency is high throughput and low latency. Together, they have an impact on the delivery and efficiency of AI applications.

Easy-to-use inference microservices that run data through trained AI models connected to small independent software services with APIs can be a game-changer. They can provide instant access to a comprehensive range of generative AI models with industry-standard APIs, expanding into open-source and custom foundation models, that can seamlessly integrate with existing infrastructure and cloud services. They can help developers overcome the challenges that come with building AI applications while optimizing model performance and allowing for both high throughput and low latency.

Enterprise-grade support is also essential for businesses running generative AI in production. Organizations save valuable time by getting continuous updates, dedicated feature branches, security patching, and rigorous validation processes.

Hippocratic AI, a leading healthcare startup focused on generative AI, uses inference microservices to deploy over 25 LLMs, each with more than 70 billion parameters, to create an empathetic customer service agent avatar with increased security and reduced AI hallucinations. The underlying AI models, totaling over 1 trillion parameters, have led to fluid, real-time discussions between patients and virtual agents.

Generate new possibilities

Generative AI is transforming the way organizations do business today. As this technology continues to grow, businesses need the benefit of low latency and high throughput as they deploy generative AI at scale.

Organizations adopting inference microservices to address these challenges securely, efficiently, and economically can position themselves for success and leading their sectors.

Learn more about NVIDIA NIM inference microservices on AWS.