IBM Granite Benchmarking and Enterprise Readiness
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Mitch Lewis
The field of AI has experienced rapid innovation within the past few years, with generative AI and Large Language Models (LLMs) in particular garnering significant attention from enterprise organizations. The quick pace of innovation has led to a plethora of new and intriguing models available in the market, while the relatively new nature of the technology presents a challenge for enterprise organizations to evaluate their options.
LLMs can be evaluated by a wide range of metrics including logical reasoning abilities, math and coding capabilities, safety, and more. Different models may exceed in various areas due to different model sizes, architectures, and mixtures of training data. Enterprise organizations choosing LLMs should be aware how different models perform in the areas that are most beneficial to their intended use cases. In addition, factors such as size, security, and continuous development are core considerations that may impact the practicality of model usage in an enterprise environment.
This paper discusses an overview of LLM evaluation criteria and reviews the performance of IBM Granite models in several key areas. This paper additionally evaluates how IBM Granite models are positioned as competitive solutions for enterprise AI requirements.
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