For business leaders and developers, the question is not why generative artificial intelligence is being deployed across industries, but how can generative artificial intelligence work faster and with higher performance? That’s what it means.
The launch of ChatGPT in November 2022 marked the beginning of an explosion of large-scale language models (LLMs) among end users. LLM is trained on vast amounts of data, giving it the versatility and flexibility to perform tasks such as answering questions, summarizing documents, and translating languages simultaneously.
Today, organizations are looking for generative AI solutions to satisfy customers and equally empower internal teams. However, only 10% of companies worldwide are using generative AI at scale. The state of McKinsey’s AI in early 2024 investigation.
To continue to develop cutting-edge services and stay ahead of the competition, organizations need to deploy and scale high-performance generative AI models and workloads securely, efficiently, and cost-effectively.
Accelerating reinvention
Business leaders are beginning to realize the true value of generative AI as it takes hold across multiple industries. According to , organizations that embrace LLM and generative AI are 2.6 times more likely to see at least a 10% increase in revenue. Accenture.
However, by 2025, 30% of generative AI projects will be abandoned after proof of concept due to poor data quality, inadequate risk management, increased costs, or unclear business value. is. gartner. Much of the blame lies with the complexity of deploying generative AI capabilities at scale.
Deployment considerations
Not all generative AI services are created equal. Generative AI models are tuned to handle a variety of tasks. Most organizations require a variety of models to generate text, images, video, audio, and synthetic data. They often choose between two approaches to deploying their models.
1. Models are 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) that allow you to choose a robust model for building secure AI applications.
Self-hosted solutions require custom coding of APIs and further adjustments based on your existing infrastructure. And organizations that choose this approach must consider ongoing maintenance and updates of the underlying model.
Ensuring an optimal user experience with high throughput, low latency, and security is often difficult to achieve with existing self-hosted solutions. High throughput refers to the ability to process large amounts of data efficiently, and low latency refers to minimal delays in data transmission. and real-time interaction.
No matter which approach an organization takes, improving inference performance and keeping data secure is a complex, computationally intensive, and often time-consuming task.
project efficiency
Organizations face several barriers when implementing generative AI and LLM at scale. If not addressed quickly or efficiently, project progress and implementation schedules can be significantly delayed. The main considerations are:
Achieve low latency and high throughput. To ensure a good user experience, organizations must respond quickly to requests and maintain high token throughput to scale effectively.
Consistency. A secure, stable, and standardized inference platform is a priority for most developers who value easy-to-use solutions with consistent APIs.
Data security. Organizations must follow internal policies and industry regulations to protect corporate data, customer confidentiality, and personally identifiable information (PII).
Only by overcoming these challenges will organizations be able to leverage generative AI and LLM at scale.
Inference microservices
To stay ahead of the competition, developers need to find cost-effective ways to quickly, reliably, and securely deploy high-performance generative AI and LLM models. Key measures of cost efficiency are high throughput and low latency. Together, these impact 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 using APIs can be a game-changer. Use industry-standard APIs to provide instant access to comprehensive generative AI models, extend to open source and custom foundational models, and seamlessly integrate with existing infrastructure and cloud services. They help developers optimize model performance and achieve both high throughput and low latency while overcoming challenges associated with building AI applications.
Enterprise-grade support is also essential for companies running generative AI in production. Organizations save valuable time by getting continuous updates, dedicated feature branches, security patching, and rigorous validation processes.
Hippocratic AI is a leading healthcare startup focused on generative AI, deploying over 25 LLMs with over 70 billion parameters each using inference microservices to improve security and improve AI hallucinations. Create empathetic customer service agent avatars with reduced friction. The underlying AI model, which includes over 1 trillion parameters in total, enabled fluid, real-time discussions between patients and virtual agents.
create new possibilities
Generative AI is transforming the way organizations do business today. As this technology continues to grow, enterprises need the benefits of low latency and high throughput when deploying generative AI at scale.
Organizations that embrace inference microservices to address these challenges securely, efficiently, and economically will position themselves to lead their sector toward success.
Click here for details NVIDIA NIM inference microservices on AWS.
