5 steps to ensure startups successfully deploy LLMs | TechCrunch

Lu Zhang, a prominent Silicon Valley investor, highlights the burgeoning era of large language models (LLMs) with the advent of ChatGPT and other models like Google’s LaMDA, BLOOM, Meta’s LLaMA, and Anthropic’s Claude. As businesses plan to deploy LLMs, particularly in specialized domains, they face the promise of competitive advantage but also significant challenges.

The deployment of LLMs is not without its issues. These models can sometimes produce incorrect information, a phenomenon known as “hallucination,” which can overshadow other critical concerns in the processes that generate these outputs. Moreover, the financial and computational costs of LLMs are substantial. The hardware, such as Nvidia’s H100 GPU, is expensive, with an estimated cost of $240 million in GPUs needed to train a model comparable to ChatGPT-3.5. Additionally, the power requirements are immense, with training a model consuming as much energy as 1,000 U.S. homes use in a year, and daily operations equivalent to the energy usage of 33,000 households.

These costs pose not only a financial burden but also a potential user experience issue, as running LLMs on portable devices could quickly deplete battery life, hindering consumer adoption. As startups look to integrate LLMs into their operations, they must navigate these challenges to leverage the technology effectively.
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