GPU compute infrastructure for research teams running machine learning experiments.
Dedicated GPU systems with fixed monthly pricing and complete hardware transparency for domain scientists and robotics researchers who need predictable compute access without enterprise budgets or technical overhead.
We're looking for early research partners. If you run ML experiments and have struggled with compute access, we'd love to chat.
Contact: hello@breadboardfoundry.com or https://www.pandoro.today/
Researchers conducting machine learning experiments face systematic barriers to accessing the compute infrastructure they need:
- Financial barriers: Cannot justify capital investment for hardware in early research stages. Usage-based cloud pricing creates unpredictable costs when experiment duration is unknown.
- Institutional barriers: Most IT departments mandate Windows environments while ML software requires Linux. Hardware approval processes delay or block acquisition. Shared computing queues create multi-week waits.
- Reproducibility barriers: Cloud providers don't disclose hardware specifications. Inconsistent GPU hardware across institutions prevents exact reproduction of published results.
- Operational barriers: Enterprise infrastructure creates vendor lock-in. No clear path to scale from cloud to owned infrastructure as labs grow.
Without GPU acceleration, experiments that should take hours take weeks or months. But accessing that acceleration means navigating financial uncertainty, institutional red tape, or proprietary infrastructure that undermines research reproducibility.
Pandoro provides dedicated consumer GPU systems with fixed monthly pricing and full hardware transparency.
- Fixed monthly pricing. Run as many experiments as needed without tracking usage or unexpected bills. No capacity prediction required—researchers exploring new methodologies can't predict experiment duration anyway.
- Complete hardware transparency. Full specifications and system configuration disclosed. Scientific publication requires reproducible computational environments, which hyperscalers' abstracted infrastructure cannot provide.
- Direct infrastructure access. Secure access to dedicated systems running in our facility. No IT approval processes, shared resource queues, or multi-week delays.
- Professional-grade hardware. Professional NVIDIA systems with substantial VRAM for training workloads, not hyperscaler inference infrastructure priced for Fortune 500 budgets.
- Onsite migration path. Consumer-grade components enable transition to in-house infrastructure as your lab grows. Purchase the same hardware for local deployment. No vendor lock-in, no proprietary configurations, no workflow rewrites.
- Clean energy infrastructure. We power systems with Washington state's renewable hydroelectric grid—so research built for meaningful impact doesn’t have to depend on fossil fuels.
Researchers don't need deployment pipelines, auto-scaling groups, or infrastructure orchestration. They need to run machine learning experiments on reliable hardware with known specifications—access to computers, not infrastructure platforms.
Hyperscalers sell reserved instances requiring accurate future capacity predictions. Fixed monthly pricing eliminates prediction requirements entirely.
Cloud providers market sub-minute provisioning as primary value. Researchers working on month-long projects don't optimize for 60-second provisioning differences. They need reliable access over weeks or months.
We're focused on supporting:
Domain scientists conducting visual imaging ML projects—marine biology, climate research, environmental monitoring. Teams without ML engineering backgrounds who need hardware transparency for reproducible, publishable results.
Robotics teams running ML experiments for robotics applications.
Common constraints:
- Small teams without capital for hardware purchases
- Blocked by institutional IT barriers and shared resource queues
- Need hardware transparency for reproducible research environments
- Need compute that scales with project growth and clear onsite migration path
We're working with a small group of research teams to deploy initial infrastructure. Availability is limited as we scale.
We're looking for research partners who:
- Are currently running or planning ML experiments
- Face budget constraints, IT processes, or queue delays blocking compute access
- Need hardware transparency for reproducible, publishable research
- Require GPU acceleration but lack ML infrastructure expertise
If this resonates with your research needs, let's chat.
We’d love to learn about your compute requirements and discuss how Pandoro could support your work.
📧 Contact: hello@breadboardfoundry.com
We're particularly interested in understanding:
- Your current ML compute workflow and constraints
- Hardware specifications your research requires
- Budget and timeline considerations
- What would make this more useful than your current approach
Pandoro is developed by Bread Board Foundry. We build software that makes working with hardware easier for teams building meaningful, impactful projects.