AI Inference
Groq is an AI infrastructure company and the creator of the LPU™ Inference Engine, a hardware and software platform that delivers exceptional compute speed, quality, and energy efficiency. Using this AI inference technology, Groq is delivering the world’s fastest Large Language Model (LLM) performance.
A winning inference strategy will be the difference between success and failure when it comes to deployment. Real-time, highly accurate insights, at a price point supportive of business needs at scale, are critical to evolving markets.
Get your inference strategy right, and your enterprise will achieve a generational leap in the ROI of AI solutions, for LLMs and other GenAI workloads.
Get the Latest Groq AI Inference Insights
Learn more with our latest GroqThoughts on AI inference and see our entire library of resources here.
Inference: Where AI Training Ends & Business Begins
Inference Speed Is the Key to Unleashing AI’s Potential
Bringing Speed to Mission with the LPU™ Inference Engine
In this white paper, we share the four fundamental considerations AI business leaders should look at when evaluating their Large Language Model (LLM) inference strategy: Pace, Predictability, Performance, and Accuracy.
Training, as a development phase, is very expensive. Inference is where AI workloads start to earn their keep. And that’s the challenge for business leaders developing an AI strategy–moving from training to inference.
We wrote this paper for the first steps of that journey. It includes specific questions every business leader should ask when pivoting from training to deployment for inference.
Why does AI inference speed matter?
Plain and simple, when determining an inference strategy for a given application, business and technology leaders need to ensure it can achieve the necessary quality and scale while still maintaining a fast enough pace.
In this white paper, dig deeper into two key metrics to consider when measuring the speed of an AI workload, the two biggest factors contributing to a model’s quality, and how to measure scalability that matters.
We also include a number of questions business and technology leaders can ask of their teams as they prepare their AI inference strategy.
In this overview of Groq for the Public Sector, we highlight how the LPU Inference Engine runs GenAI applications at 10X better speed and precision, with 10X better energy efficiency. This performance is foundational for real-time AI solutions that help bring speed to mission.
Learn how the Groq architecture is fundamentally different from that of many current GPU-based solutions. You can also read about several sample use cases for real-time AI inference for the US government agencies and Federal systems integrators when deploying LLMs and other GenAI applications.