Across every sector, organizations are using generative AI for use cases such as chatbots, document analysis, and more. Accenture reports that those adopting large language models (LLMs) and generative AI are 2.6 times more likely to increase revenue by 10 percent or more.
However, according to a Gartner report, through 2025, 30 percent of generative AI projects will be abandoned after proof of concept (POC) due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. To overcome the complexity of deploying large-scale generative AI projects, organizations need a solution that will optimize inference and ensure success.
Read this solution brief to:
- Identify obstacles that deter the deployment of generative AI
- Learn how to optimize infrastructure for maximum efficiency
- Uncover the possibilities of scalable inferencing
Discover how NVIDIA NIM inference microservices on AWS empowers developers to optimize and scale the deployment of generative AI.
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