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How Do You Get to Artificial General Intelligence? Think Lighter
Billions of dollars in hardware and exorbitant use costs are squashing AI innovation. LLMs need to get leaner and cheaper if progress is to be made.
OpenAI, Google, and xAI are locked in an arms race to train the most powerful large language model (LLM) in pursuit of artificial general intelligence, known as AGI, and their gladiatorial battle dominates the mindshare and revenue share of the fledgling Gen AI ecosystem. The LLMs trained by these huge GPU farms are usually also very expensive for inference, the process of entering a prompt and generating a response from large language models that is embedded in every app using AI. This lopsided ecosystem of ultra-rich tech moguls battling each other has enriched Nvidia while forcing application developers into a catch-22 of either using a low-cost and low-performance model bound to disappoint users, or face paying exorbitant inference costs and risk going bankrupt.
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