Large language models (LLMs) like ChatGPT have revolutionized the way we approach coding and development. By leveraging vast datasets and advanced algorithms, these models can generate code snippets, functions, and even entire projects. However, there's a critical limitation that often goes unnoticed: LLMs can't validate or test the code they generate. This blog post will explore this limitation and provide a real-world example to illustrate the issue.
Read moreThe Rapid Evolution of GPT Models: Capabilities, Challenges, and Hybrid Solutions
GPT models, with their advanced conversational capabilities, face challenges in local deployment due to high hardware and memory requirements, with larger models needing over 40 GB of GPU memory. A hybrid cloud approach, combining local and cloud resources, offers an efficient solution by balancing workload management, cost, and scalability while adhering to data compliance standards.
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