AceCloud on AI infrastructure: GPUs, sovereign Cloud & Agentic AI

As artificial intelligence adoption accelerates across Indian enterprises, the conversation is rapidly shifting from experimentation to execution. From GPU shortages and spiralling infrastructure costs to data sovereignty and energy efficiency, organisations and their channel partners are being forced to make far more nuanced decisions about AI infrastructure.

In an interaction with Vinay Chhabra, Co-founder and Managing Director of AceCloud (Real Time Data Services) the focus is clear: AI success will not come from chasing the biggest GPUs or the newest architectures, but from choosing the right infrastructure for the right workload, and preparing for a future shaped by agentic AI and inference-led demand.

Right-Sizing GPUs: Performance Without Overpaying

One of the most pressing challenges for AI adoption today is balancing performance, availability, and cost amid ongoing GPU constraints. The industry has fallen into a trap of assuming that newer or larger GPUs are always better.

“You don’t need a crane to lift a pen. Similarly, you don’t need the biggest and the best GPUs; you need the right GPU for the right workload,” he explains.

AceCloud works closely with partners and customers to map workloads to appropriate GPU classes. For example, L4 GPUs, despite being lower in VRAM, are highly effective for media streaming because of their encoding and decoding capabilities. “For SLMs, small language models, we have customers running three to four models on the same 24GB GPU,” he notes.

Legacy GPUs also continue to play a role. “A100 is still relevant because many customers have existing code and don’t want to change it,” he says, adding that even smaller GPUs like A2 remain meaningful for inference workloads where models are compact and predictable.

Beyond hardware selection, optimisation techniques such as GPU slicing further reduce cost. “An A100 with 80GB VRAM can be sliced into smaller units, maybe 10GB each. That’s another way of optimising cost,” he adds.

What This Means for Channel Partners

From a channel perspective, AceCloud positions optimisation as a relationship enabler. “When a channel partner helps a customer optimise cost without compromising outcomes, it creates stickiness." 

Rather than simply reselling capacity, partners are encouraged to engage in workload assessment, advising customers on GPU selection, slicing strategies, and software optimisation using newer CUDA libraries. This consultative approach further strengthens long-term customer trust.

Sovereign Cloud vs Hyperscalers: Where the Trade-Offs Lie

Data sovereignty has now become a board-level concern in India, especially for regulated sectors. AceCloud acknowledges that sovereign cloud environments bring both advantages and limitations.

“From an inferencing perspective, there are not many challenges because inferencing doesn’t require large GPU clusters,” he explains. However, large-scale AI training remains difficult. “Not all sovereign clouds will have very big GPU clusters. That’s a limitation.”

This reality reinforces a larger trend. “By 2030, around 90% of GPU workloads will be inferencing. Training will be only about 10%,” a prediction to be looked upon. Since inference workloads are continuous rather than burst-based, they offer more predictable revenue for both cloud providers and partners.

“Inference is steady business. Once you put a GPU on inference, it runs continuously. That’s where channel partners should focus,” he advises.

Latency, Locality, and the Psychology of Data Location

While AceCloud operates cloud regions in Noida and Mumbai, Chhabra downplays latency as a major concern for most Indian enterprise workloads. “Between Mumbai and Delhi, latency is around 20 milliseconds. For most workloads, that’s not a problem.”

Ultra-low latency use cases, such as drones, robotics, or autonomous systems, typically rely on onboard AI engines anyway. “In India, I don’t see many real use cases where ultra-low latency is required. Often, it’s more psychological comfort for customers knowing data is in-region,” he observes.

Energy Efficiency: Optimisation Across the Stack

As AI infrastructure scales, power availability and efficiency are emerging as critical constraints. This outlines how efficiency improvements are happening at every layer.

At the hardware level, smaller fabrication nodes are reducing consumption. GPU architectures now support lower-precision computation, allowing workloads to run with fewer bits and less memory. “Lower accuracy where possible means less memory, less power, and better efficiency,” he explains.

At the model level, innovations such as small language models and mixture-of-experts architectures are significantly reducing resource requirements. “Instead of activating hundreds of billions of parameters, only a small subset is active at any given time,” he says.

From the cloud provider’s side, selecting the right GPU and slicing capacity are key levers. “Energy efficiency is not one thing; it’s being introduced across every layer,” he stated.

Agentic AI: From Advice to Execution

Perhaps the most transformative shift highlighted is the rise of agentic AI. “With generative AI, we got intelligence but not execution. Agentic AI brings the execution layer,” he explains.

Using customer support as an example, the conversation describes how AI agents can now read tickets, understand context and urgency, analyse historical resolutions, draft empathetic responses, and even execute fixes autonomously. “Earlier, a support agent would take hours. Now, it can happen in minutes, or even seconds.”

He outlines three operating models: human-in-the-loop, semi-autonomous, and fully autonomous. In the latter, agents not only diagnose issues but also interact with infrastructure directly to resolve them. “The system reads the ticket, finds the solution, executes it, and reports back,” he says.

AceCloud is already deploying agentic AI internally. “We’re implementing it within our own organisation first, and then we’ll offer it to customers as a hosted workload,” Chhabra confirms.

Workforce Impact: Evolution, Not Elimination

On concerns about AI replacing jobs, long-term view states, “Every major technological shift, industrialisation, and computerisation, created fear. But it also created new kinds of jobs,” he reflects.

His advice is pragmatic. “AI will not replace you. The person who knows AI will replace you,” he says, urging professionals to adapt rather than resist. “AI is a wave. Learn to ride it.”

Conclusion: Pragmatism Over Hype in AI Infrastructure

AceCloud’s perspective offers a grounded counterpoint to AI hype. Instead of chasing headline-grabbing hardware or one-size-fits-all architectures, the focus is on workload-aware design, inference-led growth, and execution-oriented AI systems.

For enterprises and channel partners alike, the message is clear: the future of AI infrastructure lies not in excess, but in efficiency, choosing the right GPU, embracing agentic execution, and building sustainable, sovereign-ready AI stacks that deliver real outcomes.

Read More:

Focus on Hybrid Cloud Integration and AI powered IT Automation

Union Budget 2026–27: IT industry expectations on infrastructure, AI and digital growth

IT channel ecosystem in Punjab: PACT and FAIITA seek offline revival