LLM Parameters…
Original post published on my website at https://naresh.se/en/posts/202425/2025-08-28_llm_parameters/. Copying it here for wider exposure.
When we start learning about Large Language Models (LLMs), it is but natural to become quite interested in how the various parameters, training data size, context size, tokens, etc. affect the performance of the model. And how the existing models out there in the wild; both open and closed source; use the different parameters, what are their strengths and weaknesses, etc. It is also important to know and compare the training data sizes used in such models, so one can understand how many resources would a relative model need in order to be trained from scratch.
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I have tried to collect the information in the table below. The information will also help an AI practitioner to select the right model based on the use case. Please do add your comments and provide more information as required.
This collection of data is valid as of August 2025, but it is expected to change in the future. This is not an exhaustive list by any means, and I have missed many of the models. Newer models are being developed almost every day and the LLMs in general are improving at an exponential rate. It is though good to have the data at hand for comparison and to understand the different parameters and their impact on the performance and applicability of the model for a particular use case.
Please do provide your comments (both here and on my site)and let me know if you have any questions or if you would like to add more models to the list or if any information is incorrect.
Happy coding!
