Private Cloud
Compute Infrastructure
Virtual Machines
Metal Instances
Cloud GPU
Dedicated compute capacity under your governance—virtual machines for elastic workloads, bare-metal for latency-sensitive or compliance-bound applications, and GPU infrastructure for training, inference, and high-performance analytics. All engineered with isolation boundaries, predictable performance envelopes, and hardware-level control your public-cloud tenancy cannot guarantee.
Whether the workload is a stateful database cluster that refuses to share NUMA nodes, an ML training pipeline that saturates NVLink, or a fleet of lightweight VMs behind an internal platform team—compute starts with the right instance shape and ends with the operational discipline to keep it healthy.
Reality check
Shared tenancy pattern
Noisy neighbors steal cycles. Instance families change without notice. GPU availability is a lottery during training season. Compliance auditors ask where the hardware is and nobody can answer precisely.
Private compute delivery
Dedicated silicon, measured performance envelopes, GPU scheduling tied to pipeline SLAs, and audit-ready hardware provenance from rack to workload.
The physical and logical foundation underneath every VM, metal instance, and GPU allocation—server selection, rack architecture, network fabric, power and cooling envelopes, and lifecycle operations that keep compute predictable across years, not just benchmarks.
CPU families, memory tiers, and storage buses chosen against workload profiles—not catalog defaults. Qualification includes thermal, firmware, and driver compatibility for your hypervisor or container stack.
Compute density, power distribution, and east-west fabric bandwidth designed together—so scaling does not hit a cabling or cooling wall at 60% utilization.
Procurement, burn-in, production entry, warranty tracking, and decommission—each stage with documented acceptance and handoffs.
Compute capability
The most common unit of private cloud compute—VMs sized from lightweight dev containers to multi-socket database engines, all under your hypervisor, your policies, and your scheduling rules.
General purpose, memory-optimized, compute-optimized, and storage-dense profiles mapped to workload archetypes—not T-shirt sizes that hide reality.
CPU and memory affinity configured deliberately for latency-sensitive applications—databases, real-time processing, and financial engines that notice jitter.
Maintenance-window flexibility without application downtime—validated per workload class, not assumed from hypervisor defaults.
Ratios set by workload tier—production gets guarantees, dev-test gets density. Both documented and measured, not discovered during incidents.
Golden images, patching cadence, and template governance so new VMs inherit security posture from day one—not after the first vulnerability scan.
CPU, memory, storage, and network consumption tracked per tenant or project—evidence for internal billing and capacity reviews.
Compute capability
Bare-metal servers allocated as single-tenant instances—no hypervisor overhead, full hardware access, and performance characteristics that do not share a socket with anyone else's workload.
Workloads that need direct device access, deterministic latency, or compliance regimes that prohibit shared hardware. High-frequency trading engines, some database tiers, license-bound software that counts physical cores, and bare-metal Kubernetes nodes for GPU or DPDK networking.
Metal instances provisioned through automation—PXE boot, firmware alignment, BIOS configuration, OS deploy, and network plumbing—not a technician with a USB stick.
When a metal instance is returned, secure wipe, firmware reset, and requalification happen before the next tenant sees it—audit-grade evidence included.
Purpose-built GPU clusters for training, inference, rendering, and HPC—designed around power density, high-bandwidth interconnects, and scheduling that treats GPU hours as a first-class resource, not a sidecar to CPU allocation.
NVLink, InfiniBand, or RoCE fabrics sized to training parallelism needs. Power and cooling engineered for sustained GPU load—not derated after 20 minutes.
Job queues, preemption policies, and priority tiers so training pipelines and inference services share GPU resources without stepping on each other.
CUDA, ROCm, driver versions, and ML framework compatibility tracked per cluster—upgrades tested before production training runs depend on them.
Compute capability
GPU instances available on demand within your private cloud—allocated to workloads that need accelerated compute without managing the cluster infrastructure directly.
Multi-GPU and multi-node training with high-bandwidth interconnects, shared storage, and checkpointing so long jobs survive node failures without restarting from epoch zero.
Low-latency GPU allocation for model serving—autoscaling, batching, and model versioning infrastructure that keeps prediction endpoints healthy under variable load.
GPU compute for ray tracing, CFD, molecular dynamics, or video encoding pipelines that are bursty but need deterministic throughput when active.
MIG, time-slicing, or vGPU partitioning for workloads that need acceleration but not an entire device—density without wasting silicon.
GPU nodes co-located with storage tiers so training data does not cross WAN links—locality engineered, not accidental.
GPU-hours tracked per team, project, or experiment—evidence for FinOps reviews and capacity planning that goes beyond "we need more GPUs."
Engagement
Applications, performance profiles, compliance constraints, growth forecast, and GPU pipeline inventory.
Instance families, metal candidates, GPU cluster topology, network fabric, and storage adjacency—documented.
Hardware procurement, rack integration, burn-in, hypervisor or container deploy, and acceptance testing.
Workload migration, performance baselining, failover drills, and operational handoffs with runbooks.
Capacity reviews, refresh planning, GPU utilization tuning, and quarterly architecture checkpoints.
Why intSignal
Dedicated silicon with measured baselines—not noisy-neighbor variance hidden behind averaged metrics.
Tenant boundaries enforced at the physical layer when compliance or risk posture demands it.
Procurement to disposal under one operational model—asset records auditors and finance can both reference.
Clusters sized to pipeline reality, scheduled by priority, and tracked per team—not lottery-based availability.
FAQ
Yes. Most private cloud deployments combine all three—VMs for general workloads, metal for performance-critical tiers, and GPU nodes for ML or rendering. Network fabric and storage tiers are designed to serve all instance types coherently.
VMware vSphere, KVM/OpenStack, and Nutanix AHV for VMs. NVIDIA A100/H100/B200 and AMD Instinct for GPU. Specifics depend on workload requirements and licensing—we qualify against your stack, not a generic matrix.
Scheduling policies define priority tiers, preemption rules, and quotas per team or project. Utilization is metered so capacity reviews are evidence-based—not political.
End to end—procurement coordination, burn-in, production entry, warranty and RMA, refresh forecasting, and secure decommission with auditable records.
With automation in place, metal instances provision in minutes via PXE, firmware alignment, and declarative OS deploy. Lead time depends on whether spare inventory is pre-staged or requires procurement.
Share applications, performance requirements, compliance constraints, and GPU pipeline plans—we will respond with architecture, sizing, and sequencing—not a generic catalog.