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That ‘cheap’ open-source AI model is actually burning through your compute budget
New research reveals open-source AI models use up to 10 times more computing resources than closed alternatives, potentially negating cost advantages for enterprise deployments.
A comprehensive new study has revealed that open-source artificial intelligence models consume significantly more computing resources than their closed-source competitors when performing identical tasks, potentially undermining their cost advantages and reshaping how enterprises evaluate AI deployment strategies. “Open weight models use 1.5–4× more tokens than closed ones (up to 10× for simple knowledge questions), making them sometimes more expensive per query despite lower per‑token costs,” the researchers wrote in their report published Wednesday. Turning energy into a strategic advantage Architecting efficient inference for real throughput gains Unlocking competitive ROI with sustainable AI systems
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