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How AI Companies Are Measuring Inference Costs per Query

How AI Companies Are Measuring Inference Costs per Query

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Episode 80 of AI Business with Fexingo dives into the economics of inference—the real cost of running an AI model every time it responds. Lucas and Luna break down why inference cost per query has become the key metric for AI companies, from OpenAI to startups deploying small language models. They discuss the surprising numbers: how a single GPT-4 class query can cost a fraction of a cent at scale, and why companies like NVIDIA and AMD are seeing their stock wobble as the market rethinks 'GPU demand equals revenue.' The hosts also explore how inference optimization—like quantization, speculative decoding, and model distillation—is reshaping hardware spend and cloud contracts. With concrete examples and a nod to recent market data (ARM down 18% in five days, SMCI down 13%), this episode connects the engineering trenches to the balance sheet. If you're building or funding AI, this is the metric you need to track. #InferenceCost #AIEconomics #GPU #NVIDIA #AMD #ARM #SMCI #CloudCompute #ModelOptimization #Quantization #SpeculativeDecoding #Distillation #LLM #TechBusiness #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #AI Keep every episode free: buymeacoffee.com/fexingo
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