The current landscape of AI is challenged by high inference costs, with processing tokens being up to 100 times more expensive than necessary. To address this, the industry must adopt a full-stack approach that combines smarter software, purpose-built hardware, and intelligent orchestration. The complexities of model size, data movement, and computational demands contribute significantly to the rising costs and impact the return on investment.
AI’s growth potential hinges on fixing these economic barriers to unlock adoption. History shows that as AI performance on GPUs accelerates according to Huang’s Law, infrastructure must evolve accordingly. New architectures, including advanced AI chips and networking solutions, are essential to facilitate rapid data flows and optimize processing capabilities, ensuring that systems no longer hinder the performance of powerful AI processors.
Ultimately, the goal is to achieve near-zero marginal costs in AI token generation. This transformation is crucial for creating sustainable business models within the technology sector, making it essential to close the gap between conventional laws of technology advancement. Achieving these objectives demands innovative approaches that leverage both hardware and software advancements in concert.
👉 Pročitaj original: CIO Magazine
