Multi-Instance GPUs Emerge as the Backbone of GPU-as-a-Servi…
Analytics India Magazine (Smruti S)
Multi-Instance GPU (MIG) technology, a hardware feature that allows graphics processors to be split into smaller, isolated compute instances, is fast becoming the foundation of GPU-as-a-Service (GPUaaS).
Indian cloud providers NeevCloud and NxtGen say the approach has transformed pricing, utilisation, and accessibility of high-end GPUs, allowing them to serve enterprises, startups, and researchers at a scale and efficiency that was previously out of reach.
From Whole GPUs to Fractional Slices
For years, renting GPUs for artificial intelligence (AI) workloads meant reserving entire cards, whether or not the workload consumed all available compute and memory. That resulted in low utilisation and high costs, especially as GPUs such as Nvidia’s H100 or AMD’s Instinct MI325X carry steep price tags. MIG addresses that by slicing GPUs into smaller hardware-isolated instances.
“MIG has completely reshaped the pricing model,” said Vijayakumar Arumuga Nadar, head of engineering and product-AI, NeevCloud. “Instead of requiring customers to rent entire H100 or A100 GPUs at the baseline rate, we can offer fractional GPU access starting from as low as 5–10% of that cost. With MIG, we can split GPUs into smaller profiles like 1g.5gb, 2g.10gb, or 3g.20gb, making GPU power accessible to startups and SMEs.”
NxtGen has deployed the same strategy with larger, newer GPUs. “MIG has reshaped our GPU-as-a-service pricing,” AS Rajagopalan, CTO of NxtGen said. “We slice high-end GPUs like the NVIDIA H200 (141 GB split into 7×20 GB) and AMD Instinct MI325X (256 GB split into 8×32 GB) into secure, hardware-isolated instances.
This allows NxtGen to bill per-instance or at a fractional level, while driving datacentre utilisation consistently above 80%. Enterprises begin with smaller footprints, experiment affordably, and then scale to full cards as their AI strategies mature.
Profitability and Utilisation Boost
The effect on profitability has been immediate. “We’ve increased GPU utilisation from 15–30% to 60–85%, since multiple customers can now share the same hardware. A single H100 GPU that used to serve one customer at the baseline rate can now generate 130–190% of that revenue across multiple MIG instances, a 30–90% increase,” Nadar said.
NxtGen reported similar gains. “MIG has significantly improved ROI. A single H200 or MI325X card now serves 7–8 tenants simultaneously in hardware-isolated slices. Utilisation has moved from ~40–50% to above 80%, accelerating payback on GPU investments. In a market where GPUs are scarce and expensive, this higher density directly translates into profitability,” noted Rajagopalan.
Opening the Door to SMEs and Startups
Both providers point out that fractionalisation has expanded the customer base far beyond large enterprises. NeevCloud acknowledged that for MSMEs, MIG makes AI adoption financially realistic. Instead of paying the full baseline rate for a GPU, they can start with fractional pricing at 5–15% of that cost, a 70–85% cost reduction. The pay-as-you-scale model lets them begin small and expand as workloads grow, eliminating upfront investment barriers.
Similarly, NxtGen is now serving freelancers, startups, fintechs, design studios, and mid-tier universities with fractional GPU instances. “This inclusivity has created new revenue streams without proportional hardware investment and positioned our GPU-as-a-service as the most accessible and cost-effective option in the market”, Rajagopalan said.
The model is particularly relevant for research and prototyping, where workloads may not need massive compute. Nadar said MIG has enabled it to target educational and research institutions, who can rent resources at 5–15% of the baseline cost, making AI research more accessible, and “developers and researchers who can use fractional GPUs for prototyping without the high costs of full GPU instances.”
Managing Multi-Tenant GPUs
But running multiple GPU tenants on the same hardware requires rigorous monitoring to guarantee performance. NeevCloud said it relies on real-time resource monitoring, dynamic provisioning through Kubernetes and GPU Operator, fault isolation and health checks so if one instance underperforms, it doesn’t affect others, and granular usage tracking for accurate billing.
NxtGen runs its operations with similar discipline. “We operate MIG with the same rigor as multi-tenant cloud workloads. We enforce strict boundary isolation, track latency and throughput in real-time, and monitor utilisation to eliminate contention or idle slices. These controls are fully integrated into our OpenShift stack with Kubernetes-native observability, and our in-house benchmark-driven monitoring ensures consistent performance across concurrent tenants,” Rajagopalan said.
Looking Ahead: Next-Generation GPUs
NeevCloud expects the technology to become even more central as next-generation GPUs reach the market. “MIG will only become more powerful with next-gen GPUs. We expect more instances per GPU (10–14 vs. today’s 7)”, Nadar said.
Nadar said next-generation MIG will bring AI-specific optimisations, allowing each slice to tap dedicated acceleration hardware for more consistent performance. He added that edge integration will grow as 5G expands and AI shifts closer to data sources, while hybrid cloud-edge deployments will enable seamless workload movement across infrastructure. “Sustainability gains will also be key, with next-gen MIG focusing on energy efficiency,” he said.
Rajagopalan highlighted that while some workloads will still demand the largest GPUs for training models with hundreds of billions of parameters, “most real-world applications, especially enterprise inference use-cases will be 3B parameters or less, which do not require mainstream GPUs such as H200s, B200s and MI325X. Multi Instance GPUs ensure that large GPUs are not underutilised for small workloads. Running multiple instances optimises the cost of the GPU.”
As AI adoption accelerates across industries, the ability to carve up scarce and expensive GPUs into smaller, isolated slices is transforming not just economics but also access. For NeevCloud and NxtGen, MIG is no longer just a technical feature; it has become the economic engine driving the GPU-as-a-Service market.
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