The post The Inference Paradox and How AI’s Real Value Is Being Wasted on Oversized GPUs appeared on BitcoinEthereumNews.com.

For years now, the AI sector’s entire infrastructure narrative has seemingly centered around a single fundamental misconception, i.e. inference and training are computational twins. However, that is not the case; training (of LLMs) alone demands thousands of GPUs running in lockstep, burning through electricity at an almost incomprehensible scale.  Inference processes, on the other hand, require orders of magnitude less compute than the iterative backpropagation of training. Yet the industry provisions for inference exactly as it does for the latter.  To put things into perspective, the consequences of this misalignment have quietly metastasized across the industry, with an NVIDIA H100 GPU currently costing up to $30,000 and drawing up to 700 watts (when load is deployed).  And while a typical hyperscaler provisions these chips to handle peak inference demand, the problem arises outside of those moments when these GPUs sit burning approximately. 100 watts of idle power, generating zero revenue. To put it simply, for a data center with, say, 10,000 GPUs, such high volume idle time can translate into roughly $350,000+ in daily stranded capital.   Hidden costs galore, but why? In addition to these infrastructural inefficiencies, when inference demand does spike actually (when 10,000 requests, for instance, are incurred simultaneously), an entirely different problem emerges because AI models need to load from storage into VRAM, consuming anywhere between 28 to 62 seconds before the first response reaches a user.  During this window, requests get queued en masse, and users experience a clear degradation in the outputs received (while the system, too, fails to deliver the responsiveness people expect from modern AI services).  Moreover, even compliance issues arise as a financial services firm operating across the European Union (EU) can face mandatory data residency requirements under the GDPR. Thus, building inference infrastructure to handle such burdens often means centralizing compute…

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Filed under: News - @ December 16, 2025 12:25 pm