💰 GPU FinOps for Enterprise AI Viability and ROI

An extensive analysis of GPU FinOps, a new financial discipline necessary for managing the distinct and volatile economics of enterprise Artificial Intelligence, which is driven by expensive and scarce Graphics Processing Units (GPUs). It contrasts this new field with traditional Cloud FinOps, explaining that AI workloads involve non-deterministic consumption patterns and complex trade-offs between hardware selection (e.g., NVIDIA H100 vs. A100) and cost efficiency. The report identifies significant financial waste stemming from technical factors like under-utilization of reserved capacity, "Zombie Clusters," and poor bin packing, while also detailing strategies like quantization and FlashAttention that can reduce costs dramatically. Furthermore, the analysis covers the market fragmentation that allows for multi-cloud arbitrage and examines the critical "Build vs. Buy" decision, noting that while many AI pilot programs fail due to financial viability, rigorous FinOps is key to achieving sustainable ROI.