Unleashing Generative AI's Full Potential: NVIDIA's Revolution

Generative AI is just the latest technology inspiring a new wave of innovation in the digital world. As technology has progressed from microprocessors to multi-core chips that continue to increase in size and capabilities, it becomes clear that the next frontier is driven by human ingenuity in applying new algorithms. NVIDIA is emerging as a key player in this space, as it develops the next generation of algorithms that harness generative AI applications to perform unprecedented inference and real-time super-resolution. In this essay, I’ll explain how NVIDIA’s latest advancements in inference microservices will accelerate the deployment of AI applications for developers and enterprises alike.

NVIDIA: Elevating Developer Productivity with NIM

But next May, at the Computex trade show in Taipei, NVIDIA’s CEO, Jensen Huang, did just that. NVIDIA’s Inference Microservices – or NIM, for short – which just went into general release in July, is one of those quantum leaps forward that changes the keyboard forever. It improves model containers so that developers can deploy AI applications that run almost anywhere and run much faster than ever before, from clouds to workstations. The process that used to take weeks now takes minutes.

The Power of NVIDIA's Generative AI Applications

Among the most visible signs are that the apps become far more sophisticated and, in some cases, nested, with multiple generative AI models working together. NVIDIA NIM abstracts the generative AI complexity to provide an application developer with a standard approach to infuse generative AI in a straightforward manner. Depending on the capability of the underlying accelerator, it doesn’t just make it faster; it makes it vastly faster. For example, if an application developer were to run Meta Llama 3-8B using NVIDIA NIM, they could expect a three-fold increase in generative AI tokens. Now, the same resources can enable three times more to be produced.

Nearly 200 Partners Embrace NVIDIA NIM

That robust foundation was not lost on over 200 technology partners, including industry giants such as Cadence, Cloudera, and many others that are integrating NIM into their offerings to accelerate the delivery of generative AI into new use cases. From digital human avatars and code assistants to those developing new forms of AI, the number of applications is as expansive as the technology itself. The agreement with Hugging Face to provide NIM for Meta Llama 3 models cements this evolving universe.

Deploying AI Applications Seamlessly with NIM

NVIDIA’s ecosystem empowers businesses to quietly unlock AI apps with deep neural networks through the NVIDIA AI Enterprise software stack, while – as of next month – NVIDIA’s newly launched NVIDIA Developer Program will grant members access to NIM, ushering in renewed opportunities for research, development and test on clouds, data centres and HPC systems of choice.

Over 40 Microservices to Choose From

Again, the essence of this technique is to pre-fabricate containers for GPU-accelerated inference, making it possible for NIM to host more than 40 NVIDIA and community models as ready-to-use services. NVIDIA’s success in healthcare is a notorious case in point, as well as Infervision’s in lung cancer and IBM Watson’s in oncology. But the scope goes well beyond healthcare; the potential impacts in digitised biology, machine discovery, and the integration of AI into other industries are potentially transformational.

The Global Embrace of NVIDIA NIM

From platform providers to AI application companies, the global tech world is embracing NIM. Several major AI tools and MLOps partners have baked NIM into their platforms, burdening developers with less of the bulk in order to enable them to build and deploy tailored generative AI applications more easily than ever before.

NVIDIA: A Catalyst for Generative AI

NVIDIA’s commitment to raise the bar on what’s possible increases each year, whether it’s blazing trails with the NVIDIA-certified systems programme, raising the networking bar with NVIDIA Spectrum-X AI Ethernet platform, or launching certifications for edge AI with NVIDIA IGX systems.


Underlying NVIDIA’s success is an inexhaustible ability to keep innovating. The company’s roots in GPU development have led it to be a technology giant operating in a variety of fields from gaming and graphics all the way to professional visualisation, datacentres and now AI. NVIDIA’s groundbreaking work in AI is not only shaping the future of computing, but changing its fundamental processes, and the way we will interact with it in the digital world to come. The future is now.

To conclude: NVIDIA isn’t just making our jump to AI-powered apps easier, it’s making it better, digitally speaking. Its commitment to making the deployment of AI not just possible but also across all platforms, efficient and scaleable is a blueprint for where the industry must go. If generative AI apps can be built using NVIDIA’s inference microservices, the future looks very bright.

Jun 03, 2024
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