SF Compute outlines a programmable GPU compute market
In a Latent Space interview recorded at AI Engineer New York, SF Compute CEO Evan Conrad described a market that settles GPU-hour contracts with usable compute rather than cash against an index. He said the company now supports programmatic control of areas including networking and memory, dynamic subdivision of InfiniBand fabrics, and different pricing for interruptible batch workloads versus live inference; SF Compute also reported several hundred million dollars in volume and new clusters in development.
The interview presents an infrastructure approach to trading and operating AI compute, although SF Compute’s market model and reported milestones have not been independently verified.
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:
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 spoke with Evan about commoditizing compute. At AI Engineer New York, we went deeper: what does it take to physically deliver a GPU contract, and what has to be true before a market in compute can become liquid?