AI PCs and the channel shift: Hype to reality

AI is no longer confined to massive datacentres buzzing in the background. It is steadily finding its way into laptops, workstations and mini PCs. That transition is not accidental. It is structural.

At the AI Impact Summit, Vinay Shetty, Regional Director for Component Business for India & South Asia at ASUS, outlined how AI workloads are being distributed across CPU, GPU and NPU in today’s AI PCs. The approach is not about pushing everything to one component. It is about building an ecosystem where each layer has a role.

Heavy calculations still sit largely on AI servers. That is where large-scale processing happens. Yet the journey does not end there. Workloads are gradually shifting closer to the user: from servers to laptops, workstations and specialised hardware.

The gap is real. Industry experts estimate that current GPU datacentre capacity meets only about 10 per cent of actual requirements. That shortfall is forcing the ecosystem to think differently. If central infrastructure cannot scale fast enough, intelligence must move outward.

For traders and developers in particular, compatibility between server and local devices becomes critical. They need the power of central systems and the responsiveness of local computing. The value lies in the connection between the two, not in choosing one over the other.

Engineering AI PCs: Thermal design, power efficiency and performance balance

Bringing AI workloads into laptops is not just a software challenge. It is a hardware puzzle.

Thermals. Memory bandwidth. Power delivery. These are not minor concerns. They define whether an AI PC can perform reliably.

The response has been rooted in thermal management expertise. Temperature control is no longer about a single fan pushing heat out the back. It is about advanced PCB design and multiple layers of sophisticated heat dissipation systems. Airflow now happens in more than one direction. Venting systems are designed to push heat away through multiple sides, not just the rear panel.

There is also a matter of scale. AI power consumption at laptop scale is significantly lower than server scale. That alone reduces engineering constraints. A GPU in a datacentre can be as large as an entire PC. The thermal and power challenges in such environments are on a different level altogether.

In a laptop, the problem is still complex, but manageable. The objective is not to replicate a server inside a notebook. It is to balance performance and efficiency within a compact form factor.

AI mini PCs vs Laptops: Enterprise use cases and market segmentation

The conversation around AI devices often frames mini PCs and laptops as rivals. That framing may be misplaced.

Mini PCs and laptops serve different entities and use cases. Mini PCs are finding traction in enterprise, banking, BPOs, KPOs, education and healthcare. In these environments, fixed setups, standardised deployments and centralised management matter more than portability.

A recent government office deployment of 28 mini PCs for registration and transfer processes illustrates this direction. The focus was not on mobility. It was on structured, controlled computing.

Interestingly, the real competition for laptops is not mini PCs. It is smartphones. For many consumer workloads, smartphones have already taken over. Email, communication, basic productivity and even some creative tasks are done on handheld devices.

Rising RAM prices have been acknowledged as a factor influencing the broader hardware market. Yet this has not fundamentally altered the competitive landscape between mini PCs and laptops. The segmentation remains driven by use case rather than component cost alone.

On-device AI in India: Localisation, language and distributed computing

In India, the path to on-device AI will not be abrupt. Server-based AI is expected to remain dominant in the near term. Large language models continue to evolve, moving beyond simple chat interactions and improving in scoring and capability. Centralised processing still makes sense for heavy workloads.

However, India’s 1.4 billion population creates a different kind of opportunity, and now localisation is not optional. Language adaptation becomes essential.

AI that understands and responds effectively across multiple Indian languages is not a niche requirement. It is a prerequisite for scale. The shift towards on-device AI will be shaped not just by performance, but by how well it adapts to local needs.

This is where distributed computing gains importance. Edge devices connected to a broader AI infrastructure can serve specific, localised tasks while remaining part of a larger system.

Channel strategy for AI PCs: System Integrators, margins and enterprise sales

Technology transitions do not succeed on product strength alone. They thrive on ecosystem readiness. The enterprise push has been relatively recent, and the approach remains channel-driven. All enterprise sales flow through system integrators. There is no bypassing of partners.

This is not merely about distribution. It is about capability. Business operations and service delivery depend entirely on capable SIs. AI projects are complex. They require integration, customisation and efficient on-the-spot support.

Training has become central. Channel partners are being equipped to understand AI solutions, not just sell hardware. Events showcase successful deployments to inspire confidence and demonstrate practical outcomes.

One project, valued at approximately Rs 1,000 crore, highlights the scale now possible in AI-driven enterprise engagements. More importantly, partners in Bengaluru have reported improved margins in data centre services compared to traditional PC sales.

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