The video reviews the Phi 4 Local AI LLM by Microsoft, highlighting its 14 billion parameters and various quantization options, but ultimately finds its performance lacking compared to competitors like GPT-4 and Llama 3.3, particularly in coding tasks and logical reasoning. The host expresses disappointment in the model’s accuracy and responsiveness, suggesting it may not be suitable for users seeking reliable AI assistance.
In the video, the host reviews the Phi 4 Local AI LLM, a new model released by Microsoft with 14 billion parameters. The host emphasizes that while claims of superiority over models like GPT-4 are common, the true measure of a model’s effectiveness comes from user-centric testing. The video aims to provide insights into the model’s performance and usability, particularly for those who may not be experts in AI but are interested in exploring local AI alternatives.
The host discusses the different quantization options available for the Phi 4 model, including Q4, Q8, and fp16, each requiring varying amounts of VRAM. The Q4 model is noted to be the most accessible for users with limited GPU resources, while the Q8 and fp16 models offer better performance but require more powerful hardware. The host plans to test the Q8 version of the model, which is expected to be a common choice for many users, and demonstrates the setup process using Open Web UI and Olama.
As the testing begins, the host evaluates the model’s performance through various prompts, starting with a coding request for a Flappy Bird clone in Python. The model struggles with this task, failing to produce a functional code without external assets. The host notes that the Phi 4 model’s performance is not on par with other models like Llama 3.3, highlighting inconsistencies in its responses and a tendency to dodge direct questions.
Throughout the review, the host poses a series of questions to the model, assessing its ability to handle logic, reasoning, and basic calculations. While some responses are satisfactory, many others are deemed failures due to inaccuracies or lack of clarity. For instance, the model incorrectly identifies the larger of two numbers and struggles with a fitness plan request, ultimately providing only basic information without depth.
In conclusion, the host expresses disappointment with the Phi 4 model’s overall performance, suggesting that it may not be suitable as a daily driver for users seeking reliable AI assistance. While the model has potential for specific use cases, it appears to fall short in accuracy and responsiveness compared to its competitors. The host encourages viewers to share their thoughts in the comments and invites them to subscribe for future content related to AI models and home networking setups.