Vulcan Vector

MANAGED GPU INFRASTRUCTURE

Yggdrasil.

Deep roots.
Limitless research.

From bare metal to research-ready. We build and operate the infrastructure beneath your next breakthrough.

Your GPUs. Your research. Our infrastructure expertise.

Yggdrasil infrastructure treeThe tree rises from bare metal and host configuration through networking and shared storage to compute scheduling and research workloads. Access and observability operate across these layers. Storage systems are alternatives, and Slurm can run directly on hosts or through an integration with Kubernetes. WORKLOADS TrainingInference PyTorchvLLM · SGLang KubernetesSlurm COMPUTE & SCHEDULING YGGDRASIL SHARED DATA NFS · CephWEKA · VAST InfiniBand / RoCEDrivers · RDMA NCCL · GPU–NIC AFFINITY · RAIL-AWARE TUNING BARE METAL · BURN-IN · BIOS · IOMMU ACCESSTeleportauthentik · LDAPOBSERVABILITYPrometheusGrafana · Alerting
METAL → FABRIC → DATA → COMPUTE → WORKLOADS
ROOTED IN RELIABILITY
Built for the full cluster lifecycle
VALIDATE ✧ BUILD ✧ OPERATE
01 / THE PROBLEM

Your GPUs should
be doing research.
Not waiting for a fix.

You invested in compute.
The complexity came with it.

Unhealthy nodes. Fabric bottlenecks. Storage that can’t keep up. A distributed job that fails hours into a run. Expensive GPUs become expensive waiting time.

Researchers shouldn’t have to become infrastructure specialists to do their best work. That’s where Yggdrasil takes root.

Meet your infrastructure foundation
01

Hardware needs
more than a power-on.

02

Every layer affects
the next training run.

03

Research needs focus.
Infrastructure needs ownership.

02 / THE YGGDRASIL STACK

Every layer.
One foundation.

A cluster is more than a collection of tools. Yggdrasil connects the layers that make research possible—and operates the services that keep them dependable.

FROM THE ROOTS UP

01Bare metal & host baseline+

A repeatable foundation, from provisioning and firmware to Linux and CUDA compatibility. Burn-in and DCGM diagnostics validate the hardware; workload measurements guide CPU affinity, NUMA placement, PCIe topology, power profiles and IOMMU settings. Changes are tested against the actual hardware and isolation requirements.

Burn-in / DCGMBIOS / firmwareLinux / CUDANUMA / PCIeCPU affinity / TuneDIOMMU
02High-speed fabric+

InfiniBand or RoCE carries distributed traffic. Validate links and RDMA paths, then measure collectives with NCCL tests. Match GPU-to-NIC affinity and NCCL communication paths to the actual rail topology, then benchmark collective bandwidth and latency. For RoCE, tune congestion control and PFC/ECN to the fabric design. GPUDirect RDMA and UCX are options where the hardware and software path support them.

InfiniBand / RoCERDMA / UCXNCCL testsRail-aware tuningGPU–NIC affinityPFC / ECNGPUDirect RDMANetwork Operator
03Shared storage & data access+

Match the data path to dataset loading, checkpoints and model weights. NFS is an access protocol; CephFS, WEKA and VAST are storage choices. RBD supplies block volumes, while object storage can serve dataset and checkpoint workflows. Kubernetes mounts the selected backend through CSI. Evaluate GPUDirect Storage only on compatible paths.

NFSCephFS / RBDWEKA / VASTS3-compatible storageCSIGPUDirect Storage
04Orchestration & scheduling+

Kubernetes manages containers; conventional Slurm can schedule jobs directly on hosts. Configure partitions, accounting, QoS and fair-share around the chosen design. GPU Operator manages Kubernetes GPU components; Kueue adds workload admission, quotas and topology-aware placement for suitable batch workloads.

Kubernetes / SlurmGPU OperatorKueueQoS / fair-shareAccountingOptional SUNK

SUNK requires the appropriate licence and deployment validation. Kubernetes is not a prerequisite for conventional Slurm.

05Training & inference+

Validate distributed training with PyTorch, then design model serving around latency, throughput and memory budgets. Evaluate vLLM, SGLang or TensorRT-LLM for the model and hardware. NVIDIA Dynamo can coordinate distributed inference, including separate prefill and decode workers with NIXL transfers. These are deployment options, selected and benchmarked per workload.

PyTorchvLLM / SGLangTensorRT-LLMNVIDIA DynamoNIXL / KV cacheServing benchmarks

A conceptual architecture, tailored to your environment. Technologies shown are design options; integrations and acceptance tests are agreed for each deployment. Storage may be external or colocated; access and observability are shared operating services. Commercial licences and compute capacity are separate.

SECURITY, BUILT INTO OPERATIONS

Your research.
Our security operations.

From bare metal to agentic workloads, we own security operations across your managed infrastructure: monitoring, patching, access controls and incident response.

How we handle security
01 / REDUCE EXPOSURE

Harden. Patch. Verify.

Controlled access, scoped identities and validated updates across the managed stack.

02 / CONTAIN & RECOVER

A response with an owner.

24/7 critical-incident response, coordinated containment and recovery, with clear communication.

03 / AGENTIC WORKLOADS

Give agents boundaries.

Workload isolation, tool access boundaries and audit trails, designed with your application team.

Managed systems, response targets and responsibilities are defined in your service agreement.

03 / FROM METAL TO MOMENTUM
01—

Understand

Your hardware, workloads, team and research goals.

02—

Build & validate

A configured stack with an agreed acceptance baseline.

03—

Operate

Ongoing care, visibility and incident ownership.

04 / STRAIGHTFORWARD MANAGEMENT PRICING

Small overhead.
Strong foundations.

Management priced per GPU-hour.
No minimum charge.

UNDER 50 NODES

Build your foundation

C$0.05/ GPU-hour

Expert infrastructure operations for your growing GPU cluster.

Discuss your cluster

LARGER CLUSTERS

Go further

Let’s talk.

Custom pricing and a service scope built around your environment.

Contact sales

Rates in Canadian dollars. Node count determines your tier; the rate is per managed GPU-hour. GPU hardware, hosting and commercial software licences are separate. Deployment scope is agreed in your proposal.

Calculate your yearly management cost

ESTIMATE YOUR YEARLY MANAGEMENT COST

Your cluster.
Your numbers.

An illustrative yearly estimate using your managed capacity. Defaults assume 365 days × 24 hours. All figures in CAD.

Estimated yearly management feeC$112,128.00

256 GPUs × 8,760 hours/year × C$0.05 per GPU-hour

Pricing questions
Is this the cost of renting GPUs?+

No. These rates cover infrastructure management. You supply or lease the GPU capacity; hardware, hosting and commercial licences are separate.

How is the management fee calculated?+

Your node count selects the rate. The estimate multiplies nodes, GPUs per node, managed hours and that rate. Billing follows the managed capacity and hours agreed in your contract, independently of job utilisation. There is no minimum charge.

Do we need every technology shown in the tree?+

No. We select a supported design around your workloads. Storage systems are alternatives, and Slurm can run directly on hosts or through an integration with Kubernetes.

What happens when a critical incident occurs?+

Yggdrasil includes 24/7 critical-incident response through an on-call arrangement. Response targets, escalation channels and the scope of responsibility are defined in your service agreement.

05 / LET’S BUILD YOUR FOUNDATION

What could you do
with infrastructure
off your mind?

Tell us about your cluster. We’ll talk through what it takes to make it research-ready.

[email protected]

ALREADY WORKING WITH US?

Contact support

For critical incidents, use the escalation channel in your service agreement.

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starts with curious people.

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