Need AI infrastructure?
Access NVIDIA and AMD Instinct GPU capacity — dedicated or reserved — for training, inference, fine-tuning and HPC.
Explore AI InfrastructureAI infrastructure + managed operations
AI infrastructure. Operated for production.
GPU capacity for AI training, inference, fine-tuning and HPC — and managed operations for the infrastructure around it, from GPU and fabric health to observability and service management.
- NVIDIA and AMD Instinct GPU infrastructure
- Dedicated and reserved clusters
- Multi-vendor infrastructure operations
- Service management and vendor coordination
Two ways to work with Varni
Already operating AI infrastructure?
Let Varni operate it, across GPU and server health, fabric, storage, orchestration, cloud and observability — with service management and vendor coordination.
Now taking enquiries
Explore Managed ServicesMore than GPUs.
AI infrastructure is a system, not a component. Production workloads depend on compute, fabric, storage, orchestration — and the operations that hold them together.
| Layer | What it covers | Provided through |
|---|---|---|
| GPU compute | NVIDIA and AMD Instinct accelerators: capacity on request and sourcing against specific requirements. | AI Infrastructure |
| Dedicated clusters | Single-tenant and reserved clusters for an agreed term. | AI Infrastructure |
| Fabric and storage | InfiniBand, high-performance Ethernet and AI storage platforms, operated for the throughput workloads need. | Managed Services |
| Orchestration | Kubernetes and Slurm operated as the layer that turns hardware into schedulable GPU capacity. | Managed Services |
| Observability | Telemetry and alerting across GPU, fabric, storage and application health. | Managed Services |
| Operations | Incident, problem and change management, root-cause analysis and vendor coordination. | Managed Services |
From requirement to day-two operations
One path from the first conversation to an environment that is run and improved.
Scope
The workload translated into GPU, cluster and operations requirements: model, quantity, term and timeline.
Provide
GPU capacity, dedicated or reserved, confirmed against the requirement.
Operate
Where you need it, Varni Managed Services runs the environment within an agreed scope.
Improve
Capacity, recurring issues and change priorities reviewed as the environment grows.
Keep AI infrastructure available, observable and under control.
Varni Managed Services brings infrastructure operations, monitoring and service management into one operating model — coordinating incidents across compute, network, storage and hardware vendors, so your teams can stay focused on AI workloads and product outcomes.
Now taking enquiries
Explore Managed ServicesCurrent capacity
| State | GPU | Details | Action | |
|---|---|---|---|---|
| On request | NVIDIAB300 | Quantity and availability confirmed on request | Request capacity: NVIDIA B300 | |
| On request | NVIDIAB200 | Quantity and availability confirmed on request | Request capacity: NVIDIA B200 | |
| Sourcing | NVIDIAH100 | Sourcing against customer requirements | Request capacity: NVIDIA H100 | |
| Sourcing | NVIDIAH200 | Sourcing against customer requirements | Request capacity: NVIDIA H200 | |
| Sourcing | Multiple vendorsAdditional GPU Capacity | MI355X · MI350X · MI325X · MI300X · Other Sourcing against customer requirements | Tell us what you need | |
- On request
NVIDIA B300
Quantity and availability confirmed on request
Request capacity: NVIDIA B300 - On request
NVIDIA B200
Quantity and availability confirmed on request
Request capacity: NVIDIA B200 - Sourcing
NVIDIA H100
Sourcing against customer requirements
Request capacity: NVIDIA H100 - Sourcing
NVIDIA H200
Sourcing against customer requirements
Request capacity: NVIDIA H200 - Sourcing
Additional GPU Capacity
MI355X · MI350X · MI325X · MI300X · Other
Sourcing against customer requirements
Tell us what you need
Capacity is quoted in nodes where applicable. GPU configuration is confirmed per requirement. Capacity status last updated: 12 October 2026.
Workloads
AI training
Distributed infrastructure for large-scale model training and pre-training.
AI inference
Infrastructure for serving models in production at the throughput and latency you need.
Fine-tuning
Dedicated environments for adapting foundation models to your data and domain.
HPC
Accelerated compute for simulation, scientific computing and engineering workloads.
Production AI
Infrastructure for business-critical AI applications and services.
Why Varni
Infrastructure and operations
One partner, from access to GPU capacity to the operation of the environment around it.
Operated as one system
Compute, fabric, storage and orchestration treated as one production platform, not separate silos.
Multi-vendor coordination
One operating model across hardware, networking, storage and software suppliers.
Service governance
Incident, problem and change discipline, with regular service reviews.
Flexible engagement
Infrastructure only, managed operations only, or both together.
Building or operating AI infrastructure at scale?
Let’s design the right combination of capacity and operations.