What's the point of potato-tier LLMs?

This Reddit post from r/LocalLLaMA questions the practical utility of smaller Large Language Models (LLMs) like 7B, 20B, and 30B parameter models. The author expresses frustration, finding these models inadequate for tasks like coding and slower than using APIs. They suggest that these models might primarily serve as benchmark tools for AI labs to compete on leaderboards, rather than offering tangible real-world applications. The post highlights a common concern among users exploring local LLMs: the trade-off between accessibility (running models on personal hardware) and performance (achieving useful results). The author's tone is skeptical, questioning the value proposition of these "potato-tier" models beyond the novelty of running AI locally.

• •Smaller LLMs may not be suitable for complex tasks like coding.
• •The performance of local LLMs can be significantly slower than using cloud-based APIs.
• •The primary use case for some smaller LLMs might be benchmarking and experimentation.