GPU as a Service
On-demand GPU accelerators for training and inference — from a single card to multi-GPU nodes with NVLink — billed only for active compute time. Sessions provision in minutes with pre-installed ML frameworks, so you never buy silicon that sits idle.
Overview
GPU demand is lumpy. A training run needs 64 cards this week and zero the next, and buying accelerators for the peak leaves expensive silicon idle for the rest of the quarter. GPU as a Service turns the accelerator from a capital decision into an operating expense: on-demand NVIDIA A100, H100, H200, and B200 GPUs provisioned in minutes, released when the job finishes, with no procurement cycle and no hardware to retire. Every node comes with NVLink or NVSwitch interconnects for multi-GPU workloads, GPUDirect RDMA for fast data movement, and pre-installed frameworks including PyTorch, TensorFlow, JAX, and the NVIDIA CUDA Toolkit. The model gets the compute it needs exactly when it needs it, and the budget stays variable.
Clevertek scopes every engagement to your environment — capacity, sites, compliance and support model — so you get a tailored plan rather than a fixed SKU. Pricing is quote-only, and our solutions architects will work through your requirements before any proposal.
Our approach
We provision and manage on-demand NVIDIA GPU infrastructure for AI training, fine-tuning, and inference across bare-metal and virtualised form factors. Every instance is backed by enterprise-class storage (NVMe burst tier with automated checkpoint offload to object storage), high-speed fabric connectivity (InfiniBand HDR or RoCE v2), and a curated software stack maintained by our platform team. Capacity spans single GPU cards through 8-way NVLink-connected nodes, with Slurm and Kubernetes schedulers managing job placement and queue fairness. Provisioning takes minutes through a self-service portal or API, and the accelerator releases automatically when the job ends — no idle-GPU charges, no manual teardown. We manage the full infrastructure layer so your team brings only the model, the data, and the code.
Why work with us
Zero CapEx, fully elastic
No GPU hardware to purchase, no server lead times, no depreciation schedule. Capacity scales up for a training run and releases afterwards — you pay for the compute, not the idle silicon.
Latest NVIDIA GPU generations
Access to NVIDIA A100 (80 GB), H100 (80 GB SXM), H200 (141 GB), and B200 Blackwell GPUs with NVLink and NVSwitch interconnects. New architectures added as they reach production availability.
Full-stack software readiness
Pre-installed CUDA 12.x, cuDNN, NCCL, PyTorch 2.x, TensorFlow 2.x, JAX, Hugging Face Transformers, vLLM, and Docker with GPU passthrough. No driver hunting or framework compilation at instance start.
Minutes-to-provision, no procurement friction
Self-service portal and API for GPU instance creation with pre-configured environment images. Provisioning completes in under 5 minutes without a purchase order or hardware approval chain.
Key benefits
What this solution delivers for your business.
Remove GPU procurement delays
A GPU server purchase cycles through procurement, order, delivery, racking, and configuration — weeks or months. GPUaaS provisions in minutes, so AI teams start working the same day the budget is approved.
Pay only for compute used
Per-second billing with automatic instance termination means no wasted GPU-hours. A weekend training run costs the run duration, not a months-long reservation you hoped would fill.
Access scarce GPU supply on demand
NVIDIA H100 and B200 supply remains constrained through 2026. GPUaaS pools reserve capacity across tenants, so you get the accelerator when you need it without competing for allocation.
Eliminate infrastructure management overhead
Drivers, firmware, NCCL configuration, storage provisioning, and cluster networking are managed by the platform. Your team focuses on model development, not GPU environment maintenance.
What's included
Part of this managed service.
On-demand NVIDIA GPU instances
Single-GPU to 8-way NVLink-connected nodes with NVIDIA A100, H100, H200, and B200 accelerators. Provisioned in minutes via self-service API or portal with automated teardown at job completion.
- NVIDIA A100 80 GB and H100 80 GB SXM
- NVIDIA H200 141 GB with faster memory bandwidth
- NVIDIA B200 Blackwell for next-gen workloads
- NVLink and NVSwitch multi-GPU topologies
Pre-configured software stack
Production-ready base images with CUDA 12.x, cuDNN, NCCL, PyTorch, TensorFlow, JAX, vLLM, Hugging Face, and Docker GPU runtime. Custom images supported via container registry integration.
- CUDA 12.x with cuDNN and NCCL
- PyTorch 2.x and TensorFlow 2.x pre-installed
- vLLM and TGI for inference serving
- Docker runtime with NVIDIA container toolkit
High-speed fabric interconnect
NVLink/NVSwitch for intra-node GPU communication at 900 GB/s, InfiniBand HDR (200 Gbps) or RoCE v2 (100 Gbps) for inter-node gradient synchronisation via GPUDirect RDMA.
- NVLink 4.0 at 900 GB/s per node
- InfiniBand HDR 200 Gbps fabric
- GPUDirect RDMA for zero-copy transfers
- NCCL-optimised topology detection
Elastic scheduling and auto-scaling
Slurm or Kubernetes job scheduling with GPU partitioning, fair-share queues, auto-scaling node pools, and spot-instance-like pre-emptible tiers for non-critical workloads.
- Slurm and Kubernetes GPU scheduling
- Fair-share queue with priority pre-emption
- Auto-scaling node pools on demand
- Pre-emptible tier for experimentation
Where it helps
Real-world scenarios where this solution delivers measurable outcomes.
Large language model fine-tuning
Fine-tune a 70B-parameter LLM on domain data using 8x H100 nodes with NVLink interconnect. Provision capacity for the fine-tuning run, evaluate, and release — no GPU idle time between experiments.
Batch inference at scale
Run nightly batch inference over millions of records using a pool of H200 GPUs with auto-scaling. The cluster scales up for the batch window and down to zero after, paying only for the inference hours consumed.
Questions buyers actually ask
How fast can I get a GPU instance?
Provisioning completes in under 5 minutes for pre-configured images. Custom images with additional libraries or drivers take longer for the first build; subsequent instances from the same image provision in minutes.
Do I need to manage NVIDIA drivers and CUDA?
No. The platform manages drivers, CUDA Toolkit, cuDNN, NCCL, and framework installations. You bring the model code and data; the accelerator environment is ready on provision.
Can I get reserved pricing for long-running workloads?
Yes. Reserved instances with 1-month, 3-month, or 12-month commitments offer significant discount over on-demand per-second pricing. Capacity is guaranteed for the reservation term.
What GPU models are available and where?
NVIDIA A100 (80 GB), H100 (80 GB SXM), H200 (141 GB), and B200 Blackwell. Available in Indian metros (Mumbai, Delhi, Bangalore, Hyderabad, Chennai, Pune) and global data centre locations. In-region capacity for data-sovereign workloads.
Ready to scope a solution?
Talk to a Clevertek solutions architect about your requirements — no obligation.