GPU infrastructure for research teams running ML experiments
Dedicated systems with fixed pricing and complete hardware transparency—no enterprise budgets or technical overhead required.
Research Needs vs. Reality
Research teams conducting machine learning experiments face an impossible situation:
Experiments that should take hours take months without GPU acceleration. But accessing that acceleration means navigating financial uncertainty, institutional red tape, or proprietary infrastructure that undermines research reproducibility.
You need GPUs, high RAM, and substantial storage. The compute requirements are clear.
What isn't clear: how to access that infrastructure without breaking your grant budget, waiting weeks in university computing queues, or sacrificing the hardware transparency that scientific publication requires.
The Infrastructure Barriers
Researchers Face
Financial Barriers
Usage-based cloud pricing creates unpredictable costs when experiment duration is unknown. Hidden data transfer and storage fees compound the uncertainty.
Hardware purchases require capital difficult to justify when research direction is uncertain.
Institutional Barriers
IT approval processes delay or block hardware acquisition. University computing centers create multi-week queue delays for shared resource access.
IT departments mandate Windows environments while most ML software requires Linux.
Reproducibility Barriers
Cloud providers don't provide hardware specifications. Published research cannot be validated when the compute environment is unknown.
Inconsistent GPU hardware across institutions prevents exact reproduction of results.
Operational Barriers
Enterprise infrastructure creates vendor lock-in with data transfer fees and proprietary configurations. Minimum order requirements block small teams.
No clear path exists to scale from cloud to owned infrastructure as your lab grows.
Our Solution
Pandoro provides dedicated consumer GPU systems at fixed monthly pricing with full hardware transparency.
Complete hardware specifications disclosed. No usage-based billing or hidden fees for data transfer, storage, or compute time. Systems run on clean Pacific Northwest renewable energy.
Dedicated Infrastructure Access
Secure access to dedicated systems running in our facility.
No IT approval processes, shared resource queues, or multi-week delays. Immediate access when you need to run experiments.
Complete Hardware Transparency
Full hardware specifications and system configuration disclosure enables reproducible research and publication-ready methodology documentation.
Consumer-grade GPU systems with known specifications that you can reference in your methods sections.
Predictable Research Budgets
Fixed monthly pricing eliminates cost uncertainty. Run as many experiments as needed without tracking usage or unexpected bills.
Budget planning that actually works for research timelines where experiment duration cannot be predicted.
Onsite Migration Path
Consumer-grade GPU systems enable easy transition to in-house infrastructure as your lab grows.
Purchase the same components for local deployment. No vendor lock-in, no workflow rewrites, no proprietary configurations.
Clean Energy Computing
Infrastructure powered by renewable energy from Washington state's electrical grid minimizes the environmental impact of compute-intensive research.
How Pandoro Compares
vs. Hyperscaler Cloud Providers
What they provide
Abstracted infrastructure with usage-based billing. No hardware specifications disclosed. Unpredictable costs. Unreliable result reproduction.
What we provide
Consumer-grade hardware with complete specifications and fixed monthly pricing. You can purchase the same components for onsite migration when ready.
vs. University Computing Centers
What they provide
Shared resources with multi-week queue delays and aging infrastructure. Access requires institutional approval and IT bureaucracy navigation.
What we provide
Dedicated modern consumer hardware with immediate access. No queues, no shared resource competition, no IT approval processes. Professional maintenance without institutional overhead.
vs. Hardware Purchase
What it requires
Large capital investment with IT management burden. Early-stage research cannot justify equipment costs when experimental direction remains uncertain.
What we provide
Professional maintenance and flexible access with lower capital requirements than ownership. Clear onsite migration path when your team scales.
Why This Approach Works
Hyperscalers sell infrastructure.
We provide access to computers.
You don't need deployment pipelines, auto-scaling groups, or infrastructure orchestration. You need to run ML experiments on reliable hardware with known specifications.
We provide exactly that—secure access to a dedicated computer running in our facility.
Fixed pricing solves the prediction problem.
AWS, Google Cloud, and Azure offer reserved instances requiring accurate future capacity predictions. You cannot predict experiment duration when exploring new methodologies.
Fixed monthly pricing eliminates prediction requirements entirely.
Research needs transparency, not abstraction.
Hyperscalers abstract hardware details behind instance types and virtualization layers. This simplifies infrastructure management but undermines research reproducibility.
We provide complete hardware specifications because scientific publication requires reproducible computational environments.
Adequate hardware beats bleeding-edge at premium costs.
You need adequate GPUs at reasonable prices, not H100 systems at enterprise costs. Consumer-grade RTX hardware handles most visual imaging ML workflows at a fraction of enterprise GPU costs.
Who We Support
Domain scientists
conducting visual imaging ML projects. Teams without ML engineering backgrounds who need hardware transparency for reproducible, publishable results.
Robotics teams
running ML experiments for computer vision, sensor fusion, and autonomous systems who need reliable compute without cloud unpredictability.
Common constraints across both
Small teams without capital for hardware purchases
Institutional IT barriers and shared resource queues
Hardware transparency for reproducible environments
Compute infrastructure that scales with project growth
Interested ?
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
Let's discuss your compute requirements.
We'd like to understand your research needs, timeline constraints, and budget considerations. Schedule a conversation to explore whether Pandoro can support your work.
Download complete specifications.
Full hardware details, pricing structure, and technical specifications are available in our information packet. Share with your team for evaluation and decision-making.
About Bread Board Foundry
Pandoro is developed by Bread Board Foundry.We build software that makes working with hardware easier for teams building meaningful, impactful projects.
© 2025 Bread Board Foundry