dispatch from Crucible Compute an under discussed reason for the H100 smile curve (from @ComputeDesk) deploying workloads on new hardware and new firmware is HARD and there is a shortage of low level kernel and compiler engineers to figure it out, nor can mid stage startups afford large compute orchestration teams to manage heterogenous infrastructure since it’s intermittent and expensive work more efficient to run on H100s where you’ve already figured out how to make things work v burn expensive GPU time trying to refactor your workloads and solve firmware issues as hardware heterogeneity continues to increase across chips, networking, and more, huge opportunity to build abstraction layer(s) for how workloads get packaged and run on all types of hardware today there are lots of companies addressing one or a few abstractions (@SpectralCom for CUDA compilers, for example) but we expect this market to grow exponentially by necessity if you’re building abstraction layers for both workload runtime, token pricing, workload timing arbitrage, or more, we’d love to meet you and learn more, potentially integrate you into our compute deployment as we learn by building our own little margin optimized token factory

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