High-performance AI compute, on tap.
Latest-generation GPU capacity for training, fine-tuning, and inference — available hourly, by reservation, or as a dedicated cluster. With private networking, shared filesystems, and the integration to your ML platform you'd expect from a partner, not a marketplace.
- Latest-gen NVIDIA GPUs
- Bare-metal & containerized
- Hourly, reserved, or dedicated
- Shared high-throughput storage
- Private VPC networking
- ML platform integration
The capabilities you get with us.
A GPU platform engineered for training, fine-tuning, and serving — not a generic VM marketplace with accelerators bolted on.
Latest-generation GPUs
NVIDIA H100, H200, and B200-class accelerators in supported regions. Verified configurations for distributed training (NVLink, NVSwitch, InfiniBand).
Bare-metal or containerized
Bare-metal for maximum throughput and predictability; Kubernetes (with GPU operators and topology-aware scheduling) when you want platform abstractions.
High-throughput shared storage
Parallel filesystems (Lustre, WekaFS, GPFS) tuned for training I/O patterns. Snapshot and tier-down for cost-effective dataset and checkpoint management.
Private VPC networking
Isolated networking with low-latency interconnect for multi-node training, private peering to your cloud, and clear egress economics.
Flexible commercial models
Hourly for experimentation, reservations for predictable workloads, and dedicated clusters for sustained capacity. Transparent pricing, no per-API surprises.
Security & isolation
Tenant isolation at the network and storage layers, encrypted at rest and in transit, and the audit trail your security team needs to clear procurement.
What we're typically asked to solve.
Foundation model training
Multi-node, multi-week training runs. We pre-stage capacity, validate the cluster, and stand by during the run — so failures become a 10-minute restart, not a weekend of debugging.
Fine-tuning at scale
Recurring fine-tuning workloads for customer-specific or task-specific models. Reservation-based capacity gives you predictable cost; orchestration handles the queue.
Inference serving
Latency-sensitive inference workloads with auto-scaling and model-aware routing. We integrate with vLLM, TGI, Triton, and your favorite serving stack.
Burst capacity for existing fleet
You have on-prem GPU capacity that's at the limit. We add elastic, secure burst capacity for spikes — without you re-architecting the orchestration layer.
A clear, repeatable engagement model.
No black boxes. Every engagement starts with discovery, runs through a defined plan, and ends with operating ownership clearly assigned.
Sizing
Workload profile: model size, dataset size, batch dynamics, target time-to-train or QPS. Output: GPU type, count, topology, storage tier, and a cost envelope.
Provisioning
Region selection, network/storage standup, cluster validation runs, and integration with your auth, monitoring, and data pipelines.
Run
Monitored capacity with named on-call support during critical runs. Issue triage at the hardware, network, and orchestration layers.
Optimize
Post-run analysis: throughput, MFU, cost per token, idle time. We feed insights back into sizing and orchestration so the next run is better.
- Which GPUs do you have available?
- NVIDIA H100 and H200 are core inventory, with B200-class capacity in select regions. We can also source previous-gen A100 capacity where it makes economic sense for inference workloads. Specific availability by region is shared in our first technical call under NDA.
- Can you support multi-node training (8+ GPUs across nodes)?
- Yes — multi-node is a core use case. We deploy clusters with NVLink/NVSwitch within node and InfiniBand or RoCE between nodes, validated end-to-end before you start your training run. Topology is documented and matched to your framework's expectations (PyTorch FSDP, DeepSpeed, Megatron).
- How does pricing work?
- Three commercial models: hourly (best for experiments, no commitment), reserved (1, 3, 6, or 12-month, with material discounts), and dedicated cluster (fixed monthly, full visibility). All-in pricing — no per-API fees or surprise egress costs. Bring us a workload profile, we'll come back with a quote.
- Can you integrate with our existing ML platform?
- Yes — integrations with Kubeflow, Ray, MLflow, Weights & Biases, and most common platforms. We can also provide a turnkey training/serving stack if you'd rather not run one. Your call.
- How fast can we get capacity?
- Smaller capacity (1–8 GPUs) typically same-day to next-day in most regions. Multi-node clusters depend on size and region — usually within 1–4 weeks for sub-128 GPU clusters, longer for hundred-plus-GPU dedicated commits. Reservation contracts can lock in future capacity months in advance.
Ready to talk specifics?
Tell us about your workload, your timeline, and what's in your way. We'll come back with a plan, not a sales deck.
Start the conversation