For AI business leaders, 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 revolutionary workloads.
Need guidance? Get our latest white paper, Key Enterprise Considerations for Inference Deployment of Large Language Models.
In our latest 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.
There are a wide variety of workloads that are most dependent on inference. For example, applications that deliver real-time insights such as financial trading, anomaly detection, and especially LLMs.
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, co-produced with Alan Eagle, former Managing Director of Sales and Executive Communications for Google. It includes specific questions every business leader should ask when pivoting from training to deployment for inference.
Key Enterprise Considerations for Inference Deployment of LLMs
Alan Eagle is the author of How Google Works & Trillion Dollar Coach, an executive communications coach, and former Google Managing Director known for his work with Eric Schmidt, former CEO of Google.
With production-ready workloads and models, now you need inference.
Groq took a software-first approach to compiler and hardware design, recapturing software simplicity and automated AI/ML/HPC programming for anyone integrating Groq hardware solutions into their chip or scaled heterogeneous system, creating a synergistic software-hardware ecosystem.
At Groq we obsess over reduced developer complexity–getting AI workloads from functional to production-optimized as fast as possible.
Our easy-to-use software tool suite delivers optimal control over our hardware. Groq™ Compiler not only makes models machine-readable, it runs models efficiently and exactly the same way every single time while GroqFlow™ is an automatic toolflow for mapping machine learning workloads to GroqChip™.
While most AI companies are focused on the training phase, or learning from patterns within large sets of data, inference is about exploiting the discovered patterns by executing models to gain business insights.
Groq advances systems to deliver real-time AI for real-world advantages.