There’s a simple test for how serious the AI programme is in your enterprise: ask what the spend on GPU hours is. Not licences, not consultants — GPU hours. Training runs, fine-tunes, embedding jobs, inference serving: every one of them boils down to time on accelerated silicon, and the teams shipping AI fastest are the ones that treat that time as a utility to be drawn on, not an asset to be owned. That, in a nutshell, is the construct of GPU-as-a-Service. You don’t build a power plant to run a factory; you buy electricity. The whole notion of our AI Factory is built on the same logic for GPU compute: the latest-generation NVIDIA GPUs, delivered on demand from Indian data centers, billed for what you use.
What GPU-as-a-Service Actually Is
Strip the acronym and GPUaaS is a straightforward proposition. A provider stands up dense clusters of data-center GPUs, wraps them in networking, storage and orchestration, and rents capacity by the hour or by reservation. If you’ve worked on or used HPC setups, this will look very similar. Your team gets access to a cluster appropriately sized for the job, for the time duration needed, almost immediately, instead of having to release a purchase order that lands hardware two quarters later. This utility model of GPU computing is growing the way genuinely structural shifts do: from USD 8.21 billion in 2025 to a projected USD 26.62 billion by 2030, a 26.5% CAGR (MarketsandMarkets), and Gartner expects providers built for AI infrastructure to take 20% of a USD 267 billion AI cloud market by 2030 (Gartner). The demand side explains it. Model ambitions doubled faster than anyone’s procurement cycle.
Why AI and ML Workloads Are Different
General-purpose public cloud was designed for web applications: many small tasks, modest memory, CPUs everywhere. AI workloads invert all of it. For instance, training a large model is one enormous task spread across dozens or thousands of GPUs that must talk to each other constantly, which is why interconnect bandwidth matters as much as raw compute. Fine-tuning is bursty: intense for days, then nothing. Inference is the opposite, a steady around-the-clock hum whose economics live and die on how many tokens a GPU card can serve per second. Three profiles, three different hardware answers, and none of them well served by a fixed cluster bought once and amortised for five years while the silicon underneath goes through two generations.
Match the Silicon to the Workload
This is the part where ownership of GPUs fails to become a sustainable solution for ever-increasing AI initiatives across the enterprise. Enter GPUaaS, which can instantly provide the flexibility to choose the GPU SKUs for the duration you need, fueling your AI innovation. Accordingly, service providers are gearing up to service such a demand. For example, at Vyoma, we run three fleets, giving the flexibility to run workloads on the right GPU family:
- Frontier training — Blackwell. The NVIDIA B200 carries 192 GB of HBM3e at 8 TB/s with native FP4. At rack scale, the GB200 NVL72 binds 72 GPUs and 36 Grace CPUs into one liquid-cooled domain — 1.44 exaFLOPS of FP4 and a fabric that lets a trillion-parameter model train as if it lived on a single card.
- Sustained training and large-model inference — Hopper. The H200’s 141 GB of HBM3e at 4.8 TB/s runs the most mature software stack in AI. For teams productionising today, it is often the pragmatic centre of gravity.
- Inference, RAG and agentic work — RTX PRO 6000 Blackwell Server Edition. 96 GB of GDDR7, fifth-generation Tensor Cores, and MIG partitioning into up to four isolated instances per card, so four teams or four services share one GPU cleanly.
MIG deserves a sentence of its own, because it changes the unit economics of inference: instead of dedicating a whole card to a service that uses a fraction of it, you slice the card. Utilisation climbs, cost per token falls, and nobody queues.
The Economics: Own the Output, Not the Plant
Owning GPU infrastructure means paying at least three times. Once in capex, before a single token is generated. Again in the 6–12 month procurement queue while the market moves. And a third time in depreciation, as each new silicon generation resets the price-performance bar. A service inverts the risk: size a cluster for the run, release it when the run ends, and let the Cloud Calculator show the spend before you commit a rupee. For workloads that genuinely run flat-out all year, owned or colocated hardware can still pencil out, and we host that too. For everything else, the meter beats the mortgage.
How Enterprises Actually Consume It
In practice, GPUaaS settles into three patterns. Experimentation runs on-demand: a data science team grabs a handful of RTX or Hopper instances for a week of trials and hands them back. Training runs on reserved blocks: a fine-tune or a from-scratch run reserves a Blackwell cluster for a defined window, with the interconnect and storage already tuned for it. Production inference runs on MIG-sliced pools that hold their cost per token steady as traffic grows. Around all three, our public cloud carries the surrounding services and our managed services team runs the networking and operations, so the AI team’s surface area stays the model, not the plumbing. A programme can move through all three patterns in a quarter. The infrastructure follows it, rather than the other way round.
The Infrastructure Under the Service
A GPU service is only as good as the halls it runs in. Ours sit in Tier III facilities in Mumbai and Chennai, engineered for beyond 100 kW per rack with direct liquid cooling, rear-door heat exchangers and immersion for the densest systems — because a cluster that throttles overnight is a cluster you paid for twice. And since the compute sits on Indian soil in L&T-operated campuses, data residency and DPDP-aligned compliance come with the service. For banks training on payment data under RBI localisation rules, for hospitals under ABDM, for government programmes that cannot touch foreign jurisdictions, that is the difference between a vendor and an option.
Owning GPUs vs GPU-as-a-Service
|
Dimension |
Owning GPU Infrastructure |
GPU-as-a-Service (Vyoma) |
|
Upfront cost |
Heavy capex, months before first token |
None — pay for hours used |
|
Time to capacity |
6–12 month procurement, then build-out |
Provisioned as a service |
|
Utilisation risk |
Idle between runs, yours to absorb |
Release the cluster when the job ends |
|
Hardware refresh |
Depreciates as each generation lands |
Fleet stays current (Blackwell, Hopper, RTX) |
|
Scale ceiling |
Whatever you bought |
Rack-scale NVL72 domains on demand |
|
Ops & cooling |
Your problem at 100 kW a rack |
Tier III, liquid-cooled, managed |
|
Data location |
On premises |
Indian soil, DPDP-aligned |
The Backbone, Delivered
Every AI roadmap eventually meets the same constraint: enough of the right GPUs, at the right moment, at a cost the business can carry. GPU-as-a-Service is how that constraint stops being the story. Explore the AI Factory, price a cluster on the Cloud Calculator, or talk to our team about what your workloads need.
Sources: MarketsandMarkets (GPU-as-a-Service market, 2025–2030); Gartner (AI cloud market and specialised-provider share, 2030).