Reach compatible
demand.
Connect offered capacity with compatible workloads. A shared integration is designed to make each new buyer easier to serve.
Your architecture.
More opportunity.
We’re building a compute marketplace and integration layer for GPUs and custom AI accelerators. Help buyers evaluate your hardware on supported workloads, and offer capacity on your terms.
Proposed provider profile
Performance,
in context.
- Workload fit
- Model · runtime · topology
- Useful performance
- Throughput · latency · completion
- Efficiency
- Cost per result · measured energy*
- Your offer
- Capacity · window · minimum rate
Make better compute
effortless to choose and use.
Help buyers choose on workload fit, delivered performance, and the terms that work for you.
Reach compatible
Demonstrate
performance.
Show how your memory, interconnects, software, and operations affect real work. Give buyers evidence to weigh alongside the hourly rate.
Control what
you offer.
Choose capacity, availability windows, and minimum rates. Your scheduler confirms allocation, respecting existing commitments.
- H100$16 (illustrative rise)/ group·hr
8 GPUs · NVLink · 4h window
- H200$24 (illustrative fall)/ group·hr
8 GPUs · NVLink · 8h window
- Custom AI accelerators
Native runtimes · Workload qualification
- B200$40 (illustrative rise)/ group·hr
8 GPUs · NVLink · 4h window
- A100$12 (illustrative fall)/ group·hr
8 GPUs · NVLink · 12h window
- Your hardwareYour terms
Make your compute advantage visible
Prices and movements are illustrative, not live quotes. GPU asks cover the full group. Custom accelerator integrations are in development. Workload qualification and provider confirmation are required.
Connect your capacity.
Or your architecture.
Neoclouds &
compute operators
Turn chosen capacity windows into opportunities for new work. Make cluster quality visible through workload measurements: step time, throughput, startup, and delivery history as evidence becomes available.
Bring your capacityTeams with
spare reservations
Put spare capacity to work. We’re building a way to connect your unused reservation windows with teams that need compute, where your provider and contract permit subleasing.
Discuss spare capacityCustom chip makers &
hardware teams
Work with your architecture’s native compiler and runtime to configure, optimize, and validate supported workloads. Demonstrate performance and efficiency under stated conditions, with a path from evaluation to use through an authorized operator.
Explore a hardware pilotRuntime teams &
developer communities
Bring workload expertise, optimized kernels, or reproducible evaluation methods. Help turn technical improvements into evidence that buyers and providers can use.
Discuss collaborationPerformance,
with context.
We’re building workload profiles that keep results tied to the model, software, and conditions that produced them.
Define the workload.
Agree on the model, input, quality target, and supported runtime.
Measure the result.
Measure useful performance and cost per result, with included costs stated. Energy comparisons need measured power and a defined system boundary.
Share the evidence.
Use the evidence to help compatible buyers evaluate the offered capacity.
Participants control which results are shared with prospective buyers.
Start with
one workload.
Bring an authorized capacity pool or access to a supported hardware system. Together, define a workload, the evidence that matters, and what a useful first result would look like.
You bringCapacity or hardware access, runtime knowledge, and the terms for participation.
We work towardA reusable integration, a repeatable evaluation, and a route to compatible demand.
Provider integrations, bidding, execution, reservation subleasing, and performance profiles are in development. Each hardware backend and workload needs qualification.
Let’s build a pilot.
Tell us about your hardware or spare capacity, the workloads it supports, and the opportunity you want to explore.