In the fast-paced, ever-evolving world of technology, artificial intelligence (AI) remains a significant driving force behind innovation and efficiency across various industries. In recent years, businesses, regardless of their sector, have increasingly embraced AI to stay competitive and improve their operations. Notably, leading tech companies such as Microsoft, Oracle, Perplexity AI, Snap, and many others have turned to NVIDIA’s AI inference platform to enhance their AI capabilities. This platform is a comprehensive solution that integrates cutting-edge hardware, software, and systems to deliver high-performance, low-latency AI inference, helping companies reduce costs and improve user experiences.
The advances in NVIDIA’s inference software optimization and its Hopper platform have revolutionized the way industries serve the latest generative AI models. These improvements not only enhance user experiences but also optimize the total cost of ownership. A key feature of the Hopper platform is its ability to deliver up to 15 times more energy efficiency for inference workloads compared to its predecessors. This represents a significant leap forward in reducing the energy footprint of AI operations.
Understanding AI Inference
AI inference is a complex process that involves making predictions or generating outcomes based on pre-trained AI models. It’s a crucial aspect of deploying AI in real-world applications. However, achieving the right balance between throughput—how much data can be processed—and user experience is often challenging. The ultimate aim is straightforward: generate more tokens at a lower cost. Tokens are essentially words or segments of text that AI models process, and in many AI services, there is a cost associated with generating a large number of tokens. Hence, optimizing AI inference can lead to substantial cost savings and energy efficiency.
Full-stack software optimization is critical in improving AI inference performance. By refining every layer of the software stack, from the hardware to the application level, companies can achieve enhanced performance and lower operational costs.
Cost-Effective User Throughput
A common challenge for businesses is finding the right balance between performance and cost when dealing with AI inference workloads. While some tasks may be manageable with standard models, others require more customized solutions. NVIDIA’s technologies simplify model deployment by optimizing both cost and performance. This allows businesses to tailor their AI models to specific needs while maintaining flexibility and customizability.
NVIDIA offers several inference solutions, including NVIDIA NIM microservices, NVIDIA Triton Inference Server, and the NVIDIA TensorRT library:
- NVIDIA NIM Inference Microservices: These are prepackaged and performance-optimized solutions that allow rapid deployment of AI models on various infrastructures, such as cloud environments, data centers, edge devices, or workstations.
- NVIDIA Triton Inference Server: This open-source project enables users to package and serve any AI model, regardless of the framework it was trained on, making it highly versatile.
- NVIDIA TensorRT: This deep learning inference library is designed for high performance, delivering low-latency and high-throughput inference for production-level applications.
These components are part of the NVIDIA AI Enterprise software platform, which is available on major cloud marketplaces. This platform provides enterprise-grade support, stability, manageability, and security, making it a reliable choice for businesses looking to leverage AI.
Cloud-Based LLM Inference
For companies looking to deploy large language models (LLMs) in the cloud, NVIDIA has collaborated with major cloud service providers to ensure seamless integration of its inference platform. This means that businesses can deploy NVIDIA’s solutions in cloud environments with minimal effort. Some of the cloud-native services that integrate with NVIDIA NIM include Amazon SageMaker AI, Google Cloud’s Vertex AI, Microsoft Azure’s AI Foundry, and Oracle Cloud Infrastructure’s data science tools.
For instance, deploying NVIDIA Triton on Oracle’s OCI Data Science platform is as simple as activating a command line switch, which then launches an NVIDIA Triton inference endpoint. Similarly, Azure Machine Learning allows for no-code deployment through its studio or full-code deployment via its CLI. AWS and Google Cloud also offer intuitive deployment options for NVIDIA’s inference solutions.
The NVIDIA AI inference platform is designed to adapt to the evolving needs of users within a cloud-based infrastructure, making it a versatile choice for companies looking to enhance their AI capabilities.
Real-World Applications of NVIDIA’s AI Inference Platform
The impact of NVIDIA’s AI inference platform is being felt across various industries, transforming operations and driving innovation.
Serving 400 Million Search Queries Monthly With Perplexity AI
Perplexity AI, a search engine powered by AI, processes over 435 million queries monthly. Each query involves multiple AI inference requests, necessitating efficient and cost-effective solutions. By utilizing NVIDIA H100 GPUs, Triton Inference Server, and TensorRT-LLM, Perplexity AI supports over 20 different AI models, facilitating tasks like search, summarization, and question-answering. This approach has enabled the company to reduce costs while maintaining high accuracy and low latency.
Reducing Response Times With Recurrent Drafter (ReDrafter)
NVIDIA has integrated ReDrafter, an open-source approach for speculative decoding, into its TensorRT-LLM offering. ReDrafter employs smaller draft modules to predict tokens in parallel, which are then validated by the main model. This technique significantly reduces response times, particularly during periods of low traffic, enhancing the efficiency of LLMs.
Transforming Agreement Management With Docusign
Docusign, a leader in digital agreement management, collaborated with NVIDIA to enhance its Intelligent Agreement Management platform. With NVIDIA Triton, Docusign was able to unify its inference platform across all frameworks, accelerating time to market and turning agreement data into actionable insights. This underscores the positive impact of scalable AI infrastructure on customer experiences and operational efficiency.
Enhancing Customer Care in Telco With Amdocs
Amdocs, a prominent provider of software and services for communications and media providers, developed amAIz, a generative AI platform for telcos. By leveraging NVIDIA DGX Cloud and AI Enterprise software, Amdocs reduced token consumption for deployed use cases, offering the same level of accuracy at a lower cost. This collaboration also reduced query latency, ensuring near real-time responses, thereby improving user experiences.
Revolutionizing Retail With AI on Snap
Snap’s Screenshop feature, integrated into Snapchat, uses AI to help users find fashion items in photos. NVIDIA Triton played a crucial role in enabling Screenshop’s pipeline, which processes images using multiple AI frameworks. By consolidating its pipeline onto a single inference serving platform, Snap reduced development time and costs, providing a seamless user experience.
Financial Freedom Powered by AI With Wealthsimple
Wealthsimple, a Canadian investment platform, redefined its machine learning approach using NVIDIA’s AI inference platform. By standardizing its infrastructure, Wealthsimple reduced model delivery time from months to minutes, ensuring seamless predictions for over 145 million transactions annually. This transformation highlights the transformative power of robust AI infrastructure in financial services.
Elevating Creative Workflows With Let’s Enhance
Let’s Enhance, an AI startup, optimized its workflows using the Stable Diffusion XL model on the NVIDIA AI inference platform. This integration allowed the company to create stunning visual assets for e-commerce and marketing campaigns with minimal engineering involvement, freeing up resources for research and development.
Accelerating Cloud-Based Vision AI With OCI
Oracle Cloud Infrastructure integrated NVIDIA Triton to power its Vision AI service, enhancing prediction throughput and reducing latency. This integration has improved customer experiences across various applications, such as toll billing automation and invoice recognition.
Real-Time Contextualized Intelligence and Search Efficiency With Microsoft
Microsoft’s Azure platform offers a wide selection of virtual machines powered by NVIDIA AI. These systems enhance AI inference in Microsoft 365 Copilot, enabling real-time contextualized intelligence. Additionally, Microsoft Bing integrated NVIDIA inference solutions to improve the performance of its Deep Search feature, optimizing web results and enhancing user experiences.
Unlocking the Full Potential of AI Inference With Hardware Innovation
NVIDIA’s GPUs are at the forefront of AI advancements, offering high efficiency and performance for AI models. The NVIDIA Blackwell architecture has significantly reduced the energy used per token generation, while the NVIDIA Grace Hopper Superchip delivers substantial performance improvements across industries. These innovations enable companies to run state-of-the-art LLMs in real time, addressing challenges like scalability and latency.
Future AI Inference Innovations
The future of AI inference promises significant advancements in both performance and cost-effectiveness. The combination of NVIDIA software, novel techniques, and advanced hardware will enable data centers to handle increasingly complex workloads, driving progress in industries such as healthcare and finance. This will lead to more accurate predictions, faster decision-making, and better user experiences.
For those interested in staying updated on NVIDIA’s latest AI inference performance results and innovations, more information is available on their official website.
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