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This article details SF Compute's evolution from a simple GPU subleasing platform to a provider of physically settled, programmable compute clusters that manage the full stack from hardware to firmware. By separating control planes and enabling liquid markets for GPU hours, the technology addresses critical infrastructure challenges in scaling AI workloads without requiring massive upfront capital expenditures.
Sources coveringLatent Space, Cisco Blogs, Quantum Zeitgeist
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The Programmable Data Center — Evan Conrad, SF Compute

Latent Space40 min read

From GPU trading to physical settlement, independent markets, and programmable compute

This was recorded on the sidelines of the new Compute Trading track at AIE NYC. Catch us next at AIE CODE, our last flagship for the top agentic engineers in the world. In the early days of cloud, former guest Martin Casado made something so disruptive that Cisco was threatened enough to pay $600m for it, and VMware outbid for $1.3B: An M&A lesson: Cisco offered $600m for Nicira. @bhorowitz told @martin_casado: “The high offer is a signal that something in the environment has changed. A high offer from an acquirer means you should not take it!” “Don’t just sell to Cisco. Run a process.” Martin stopped … 3:42 PM · Sep 20, 2026 · 37.1K Views That concept was software-defined networking, which virtualized the routing network by separating the control plane from the data plane. That revolution is now coming for GPUs - where previously running your own supercompute cluster required $50-100m upfront to get commitments and do the entire stack from hardware to firmware to kernels, SF Compute is now enabling you to not only buy or sell GPU hours now and in the future, but also to programmatically control everything from your network to memory. We last…

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