On-demand GPU servers, reserved capacity, and dedicated clusters in Silicon Valley. Scoped around the workload.
Every deployment starts with the same question: what are you trying to run, and what does it need? From there we shape the GPU count, node design, storage, network, and commercial model.
How we think about infrastructureAccess GPU capacity for experiments, burst work, or a defined project without committing to a long reservation.
Reserve GPU capacity for a production launch, a training cycle, or a team that needs a stable environment.
A dedicated node or cluster for teams that need control over the deployment envelope and a closer technical handoff.
Reference configurations for common training and inference profiles. Availability is confirmed against your requested start date, quantity, and deployment model.
View all configurationsA proven platform for large-scale training, fine-tuning, and high-throughput inference.
More memory and bandwidth for larger models, longer context windows, and demanding inference.
Blackwell-class capacity for teams planning demanding training and inference workloads.
A capacity request is a technical conversation, not a checkout flow. We keep the sequence clear so you can make a commercial decision with the right information.
Model, GPU count, framework, data profile, target start date, and the outcome you need.
We map the requirement to a GPU configuration, node count, network, storage, and deployment model.
Commercial terms, deployment window, service scope, and operational responsibilities are put in writing.
Start with the right footprint, then expand when the workload or team demands more capacity.
Training and inference put different pressure on a deployment. We scope each one around the constraint that actually matters.
Pre-training, fine-tuning, and distributed runs where interconnect, memory, and sustained throughput set the pace.
Explore training capacity →Model serving where memory footprint, latency, concurrency, and cost per token shape the right deployment.
Explore inference capacity →No mystery inventory. No badge wall. A direct, documented path from a technical requirement to a deployment decision.
We confirm the hardware, deployment window, and service scope before asking you to commit.
GPU capacity, storage, network, and support are discussed together so the quote reflects the real deployment.
Legal entity, deployment terms, and operational responsibilities are available for company-level review.
Start with the footprint that fits today, with a path to add capacity as the workload evolves.
If your question is not here, include it in the capacity request. We will answer it directly.
Deployment timing depends on the GPU model, node count, network design, and start date. Share your requirement and we will confirm availability and a realistic deployment window before you commit.
Yes. The initial conversation can cover a single server, a multi-GPU node, or a larger reserved cluster. We will scope the smallest configuration that meets your workload and leave room to expand.
Capacity is delivered through Silicon Valley infrastructure partners. We do not publish a facility name or street address. LPC remains the customer-facing contracting entity for your capacity request and commercial commitments; facility-level power, bandwidth, and SLA obligations are delivered with those partners and documented in the applicable service terms.
The first release is a B2B capacity-request experience. You submit a workload profile and our team confirms the right configuration, commercial terms, and deployment path. Automated provisioning is a later phase.
The most useful details are the model or workload, GPU model and quantity, expected duration, target start date, networking needs, storage requirements, and whether you need on-demand, reserved, or dedicated capacity.
Share the workload, GPU count, timeline, and deployment model. We will come back with a clear capacity path.
Request capacity B2B only · Silicon Valley, California