In the video, Matt advocates for the use of small AI models, emphasizing their accessibility and cost-effectiveness for small businesses compared to larger models that require significant resources and financial investment. He encourages a shift in focus towards optimizing smaller models, which can deliver practical results without the complexities and prohibitive costs associated with massive models.
In the video, Matt discusses the advantages of small models in the context of artificial intelligence and machine learning. He emphasizes that small models are more accessible for small businesses, which can benefit from locally hosted and finely tuned models without incurring exorbitant costs associated with larger models hosted on platforms like AWS or Google Cloud. This accessibility allows smaller enterprises to leverage AI technology without the financial burden that comes with using massive models.
Matt expresses his enthusiasm for small models, highlighting the excitement within the community, particularly on platforms like Discord. He notes that many users are captivated by the prospect of working with large models, such as those with 45 billion parameters. However, he points out that the practicalities of using such large models can be daunting, especially for individuals or small businesses that may not have the necessary resources to manage them effectively.
He shares his personal experience, mentioning that even with decent resources at home, handling a model with 405 billion parameters is unrealistic. The complexity and resource demands of such large models can be overwhelming, making them impractical for everyday use. This leads him to question the rationale behind the excitement for these massive models when smaller, more manageable options are available.
Matt also touches on the financial implications of using large models, noting that the costs associated with hosting and operating them can be prohibitive. He argues that for many users, the benefits of small models far outweigh the allure of larger ones, as they provide a more feasible solution for practical applications. This perspective encourages a shift in focus towards optimizing smaller models that can deliver effective results without the associated costs.
In conclusion, Matt advocates for the adoption of small models, highlighting their practicality and cost-effectiveness for small businesses and individual users. He believes that the excitement surrounding large models should not overshadow the potential of smaller, finely tuned models that can be more easily managed and utilized. By promoting the use of small models, he aims to empower a broader audience to engage with AI technology without the barriers posed by larger, more complex systems.
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