GPU infrastructure provisioned for compute-intensive workloads that require parallel processing at scale. Isolated, dedicated, and managed with the same operational standards as every other layer in our network.
GPU and accelerated compute is infrastructure built around specialised processing hardware for workloads where general-purpose CPUs are insufficient. Inference, training, rendering, transcoding, simulation, and large-scale numerical processing all demand hardware that is architecturally different from the CPU-based compute described on our other infrastructure pages.
This is not a GPU bolted onto a standard server. It is a purpose-built compute environment where the GPU is the primary resource, with CPU, memory, storage, and network configured to serve it rather than the other way around. Provisioning, scheduling, and lifecycle management are all GPU-aware, meaning the infrastructure understands VRAM capacity, GPU interconnect topology, and thermal constraints as first-class concerns.
Multiple GPUs allocated to a single instance with high-bandwidth interconnects between them. For workloads that must distribute processing across multiple devices, such as large model training, high-resolution rendering, or parallel simulation. GPU-to-GPU communication bandwidth and topology are specified at provisioning time.
One dedicated GPU per instance with supporting CPU, memory, and storage. For workloads that require hardware acceleration but operate within the capacity of a single device. The simplest deployment model and the right starting point for most inference and light processing workloads.
A single GPU shared across multiple smaller workloads with resource boundaries enforced at the infrastructure layer. For workloads that need hardware acceleration but do not require a full device. Each workload receives a guaranteed allocation of GPU compute and VRAM that cannot be consumed by adjacent workloads on the same device.
Multiple multi-GPU nodes coordinated as a single compute resource. For workloads that exceed the capacity of a single node and require distributed processing across a fleet of accelerated hardware. Cluster networking is optimised for the high-throughput, low-latency communication patterns that distributed GPU workloads demand.
GPU memory is allocated and tracked with the same rigour as system RAM on CPU-based infrastructure. Every workload has a defined VRAM allocation. Oversubscription is not permitted. A workload that requires 24GB of VRAM is guaranteed 24GB, not a share of a pool that may or may not be available at runtime.
In single-GPU and multi-GPU configurations, the devices assigned to your workload are exclusively yours. No other workload can access, share, or contend for them.
In fractional configurations, resource boundaries are enforced at the hardware level. Each workload compute and VRAM allocation is guaranteed and isolated. One workload cannot consume another allocation or observe its data.
GPU instances follow the same network isolation model as all other infrastructure in our network. Isolated traffic paths, policy enforcement at the infrastructure layer, and per-instance segmentation keep workloads separated.
All attached storage is encrypted at rest with per-instance keys. Model data, training data, and output data are cryptographically destroyed on deprovisioning.
Specify GPU type, quantity, interconnect requirements, supporting CPU and memory, region, and network configuration. The instance is built and enters active state.
Add GPU instances or nodes to your deployment using the same configuration and placement rules as existing capacity.
Pause a GPU instance while preserving its full state, including loaded models in VRAM where the hardware supports it.
The instance returns to active state. For workloads with persistent VRAM allocation, the model is already loaded and ready to serve.
The instance is terminated, all storage is cryptographically wiped, and GPU resources are released.
GPU hardware is available at selected locations across our network. Not every location carries every GPU type. Availability depends on hardware allocation and demand across the network.
Our team can advise on which locations carry the GPU configurations your workload requires, and new capacity is added as demand justifies it. Where a specific GPU type is not available at your preferred location, we will identify the nearest alternative and provide latency data so you can make an informed placement decision.
Orchestration, for GPU-aware scheduling, scaling, and lifecycle management across accelerated node fleets. Networking and connectivity, providing high-throughput, low-latency paths for data-intensive GPU workloads and distributed training. Monitoring and disaster recovery, providing GPU health telemetry, performance monitoring, and continuity planning. Platform and application servers, where CPU-based services feed data to or consume output from GPU workloads.
ML frameworks, training pipelines, or inference serving software. These run on the infrastructure. Think Huge provides the accelerated compute layer, not the application stack above it.
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