Loading
Loading
Elastic, high-performance GPU infrastructure engineered for the full AI lifecycle — from experimentation through training to production inference, with predictable performance and enterprise-grade isolation across NVIDIA H100, A100 and L40S architectures.
NOVACORE GPU Cloud delivers on-demand access to the latest NVIDIA GPU architectures deployed in purpose-built data centres with high-bandwidth interconnects, tiered storage and a security model designed for regulated workloads. Whether you are checkpointing a 70-billion-parameter model across hundreds of H100s, serving low-latency inference with L40S instances or iterating quickly on a fine-tuning pipeline, the platform provides the raw compute density, network fabric and operational tooling that production AI teams require — without the capital commitment or supply-chain friction of owning hardware.
Hopper architecture with 80 GB HBM3, Transformer Engine, FP8 support and 3.2 Tbps InfiniBand. Ideal for large-scale distributed training, long-context fine-tuning and high-throughput inference of frontier models. Delivers up to 9x faster training than A100 on transformer workloads.
Ampere architecture with 40 GB or 80 GB HBM2e, MIG partitioning for multi-workload isolation and mature ecosystem support. Well-suited for mid-scale training, batch inference and research workloads with proven cost efficiency. Supports up to seven isolated GPU instances per physical device.
Ada Lovelace architecture with 48 GB GDDR6, fourth-generation Tensor Cores and hardware-accelerated video decode. Optimised for inference serving, real-time applications, fine-tuning of small-to-medium models and visual AI pipelines. Delivers exceptional performance-per-watt for latency-sensitive production deployments.
Next-generation Hopper and Blackwell architectures with expanded HBM3e memory capacities up to 288 GB and significantly higher memory bandwidth. Available under early-access programmes for partners with demanding memory-bound workloads and frontier training requirements that exceed current-generation capacity.
Bespoke node specifications combining specific GPU counts, memory tiers, local NVMe capacity and network topology. Designed for teams whose workload profile does not fit standard instance types and who need a tailored environment with precisely matched compute, memory and I/O ratios.
Pre-provisioned clusters of 8 to 1,024+ GPUs interconnected over non-blocking InfiniBand fabrics with shared parallel storage. Suitable for teams running distributed training jobs that require tightly-coupled, low-latency communication across every rank. Rail-optimised topology ensures predictable all-reduce performance at scale.
Every GPU instance runs on a fabric engineered to eliminate bottlenecks between compute, memory, storage and network — the dimensions that define real-world AI throughput.
Provision GPU virtual machines or bare-metal nodes in minutes through the API, CLI or web console. Pay per GPU-hour with no upfront commitment. Ideal for experimentation, burst capacity, CI/CD pipelines and short-duration training runs where flexibility outweighs unit-cost optimisation.
Commit to a minimum GPU footprint over a monthly, quarterly or annual term in exchange for discounted rates and guaranteed availability. Suitable for teams with predictable baseline workloads and budgets that reward capacity planning with lower per-hour cost and assured resource access.
An entire GPU cluster provisioned for a single tenant with isolated control plane, custom network partitioning and dedicated storage backends. Designed for regulated industries, long-running training campaigns and organisations that require complete tenancy separation at every layer of the stack — from bare metal through to the API.
NOVACORE GPU Cloud pricing is built on transparent unit economics: GPU-hour rates by architecture tier, with volume discounts for reserved capacity and dedicated clusters. Storage, networking egress and support are priced separately so teams only pay for what they consume. Published rates follow once capacity and operational readiness are confirmed. Early-access partners receive preferential terms and direct input into the pricing framework.
Tell us about your models, training scale, inference throughput targets, data residency requirements and timeline. We map your needs to the right GPU architecture, cluster size and deployment model — ensuring the configuration matches the workload, not the other way around.
Receive a detailed proposal covering GPU type and count, network topology, storage configuration, security posture, estimated run rate and SLA commitments. Every proposal is tailored to the workload and includes transparent cost modelling at multiple utilisation scenarios.
Once terms are agreed, your environment is provisioned with API credentials, VPN or private-link access, storage backends and monitoring dashboards. Our engineering team provides onboarding support and workload optimisation guidance to get your first training run or inference endpoint online within hours.
Run your workloads with full observability into GPU utilisation, memory pressure, InfiniBand congestion and storage throughput. Scale capacity up or down through the console or API, with reserved and on-demand pools available to absorb demand spikes. Continuous cost analytics help you optimise spend against throughput.
Secure AI and high-performance computing for enterprises, governments and research.