Generative AI, led by Chat GPT and complemented by DeepStream and Stable Diffusion, is poised to revolutionise industries, especially in the context of IoT services, where data processing challenges are met by innovative edge solutions like GenRunner, offering a seamless transition from development to production while inspiring a growing community of developers and integrators.
In today’s tech landscape, Generative AI is capturing the attention of not only CIOs and CTOs but also non-technical leaders. Among its most compelling applications are text-to-text, video-to-text, and text-to-image, each of which has the potential to bestow superhuman capabilities upon knowledge workers and transform the world of content creation and description. In the midst of this AI revolution, one particular AI model, Chat GPT, achieved remarkable success, surpassing a hundred million users faster than the renowned social media platform TikTok. Alongside Chat GPT, DeepStream boasts a staggering 2.5 million AI developers, while Stable Diffusion has nurtured a community of 40 million content creators. It is evident that Generative AI is poised to make a monumental impact on the technology market. However, GPT is just the beginning.
Generative AI is set to play a pivotal role in every IoT service, especially in applications involving machine data, video-to-text, and speech-to-text. Yet, these use cases come with their own set of critical challenges. Consider the influx of data from cameras, industrial sensors, kiosks, and smart displays. Three major hurdles emerge: the need for high-speed processing of heavy data, ensuring the privacy and secure handling of sensitive video and audio data, and the soaring GPU costs required to power these AI-hungry applications. It is becoming increasingly apparent that edge hardware holds the key to solving these challenges, enabling real-time performance, data privatisation, pre-processing, and the efficient distribution of workloads, ultimately driving down GPU costs.
In response to these emerging needs, we introduce GenRunner, the cornerstone of private Generative AI applications at the edge. GenRunner empowers high-impact use cases such as video-to-text, private GPT, and speech-to-text, all within a compact single-board computer that can be easily scaled into clusters. Think of GenRunner as an integrated kit for Generative AI, equipped with frameworks, functional models, and a streamlined API, offering plug-and-play access to this transformative technology. GenRunner not only addresses edge AI horsepower requirements but also simplifies integration, reduces complexity and accelerates time-to-market. As you transition from development to production, our Director software serves as the mission control centre for cluster deployment and AI performance optimization, providing turnkey infrastructure for your applications.
Here’s a breakdown of the key components of GenRunner:
- GenRunner Node: This modular computing unit forms the heart of Gen AI at the edge.
- GAPI (GenRunner API): The software API gateway that facilitates seamless access to Generative AI capabilities.
- Director: Your mission control for cluster deployment and fine-tuning AI applications, ensuring optimal performance.
Over the past decade, two significant edge hardware devices have made their mark: Raspberry Pi, which ushered in the era of IoT-to-cloud connectivity, and Jetson Nano, which introduced complex AI topics to a new generation of 2.5 million developers. With GenRunner, we aspire to establish a reputation for embeddable Gen AI, inspiring those 2.5 million developers and the integrators they collaborate with to expand their horizons and unlock the superpowers inherent in Generative AI. The era of private Generative AI at the edge has arrived, and GenRunner is leading the way.
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