Comparison of Large Language Models for Deployment Requirements
This work addresses the problem of navigating the complex LLM landscape for researchers and companies, but it is incremental as it compiles existing information without introducing new methods or benchmarks.
The paper tackles the challenge of selecting optimal large language models (LLMs) for deployment by providing a comparative list of foundational and domain-specific models, focusing on features like release year, licensing, and hardware requirements, with the list published on GitLab for continuous updates.
Large Language Models (LLMs), such as Generative Pre-trained Transformers (GPTs) are revolutionizing the generation of human-like text, producing contextually relevant and syntactically correct content. Despite challenges like biases and hallucinations, these Artificial Intelligence (AI) models excel in tasks, such as content creation, translation, and code generation. Fine-tuning and novel architectures, such as Mixture of Experts (MoE), address these issues. Over the past two years, numerous open-source foundational and fine-tuned models have been introduced, complicating the selection of the optimal LLM for researchers and companies regarding licensing and hardware requirements. To navigate the rapidly evolving LLM landscape and facilitate LLM selection, we present a comparative list of foundational and domain-specific models, focusing on features, such as release year, licensing, and hardware requirements. This list is published on GitLab and will be continuously updated.