Rising memory and GPU costs worrying retailers

The global AI boom is reshaping semiconductor markets resulting in sharp rise in memory and GPU costs. This trend has the potential to materially impact the cost of Visual AI solutions across retail. At SAI Group, our architecture and partners help retailers sustain performance while keeping deployment costs within acceptable limits (see SAI Group Hardware Requirement for more information).

Memory and GPU cost challenges for Retail AI

In 2026, semiconductor memory markets are experiencing an AI-driven supercycle — a structural shortage that’s disrupting the economics of hardware critical to AI infrastructure.

According to industry analysis:

• AI data centres are consuming a dominant share of memory supply, with high-bandwidth DRAM and flash increasingly prioritized for cloud AI servers over legacy channels.
• Price spikes in DRAM and NAND memory have reached unprecedented levels, with some trackers reporting 50%+ quarter-on-quarter increases, and forecasts showing this trend persisting through 2027 due to constrained capacity growth.
• The shift toward specialized HBM memory for AI accelerators is both structural and long-term, with new fab capacity unlikely to meaningfully ease pricing until late 2027 or beyond.

Visual AI solutions, which provide loss prevention analytics and self-checkout scorecards rely on powerful GPUs and memory-rich servers for training and inference. As memory and GPU costs rise, the total cost of deploying on-premise or cloud-assisted AI infrastructure can increase significantly.

For retail leaders planning to scale Visual AI, these macro hardware pressures can translate into:

• Higher upfront investment
• Longer provisioning timelines
• Increased total cost of ownership (TCO)

Why SAI Group’s architecture is better positioned for cost volatility

At SAI Group, we have built our visual AI platform to help retailers avoid the worst of these cost impacts while still realizing advanced intelligence:

1. Optimized compute architecture that minimises waste: Rather than driving cost with monolithic GPU-heavy deployments, SAI Group uses an architecture that:
   • Efficiently leverages edge processing and hybrid execution
   • Reduces dependency on oversized memory allocations
   • Matches compute where it matters most — at the edge, near the retail operation.

   This means lower per-store infrastructure costs and less sensitivity to global memory price swings.

2. Scalable deployment with cost-predictable partners: We partner with hardware and integration leaders who:
   • Help secure favourable pricing, guaranteed capacity, and lead-time commitments
   • Provide infrastructure options that fit retailer needs and budgets
   • Support procurement, forecasting, and rollout planning

   In a tight memory market, these partnerships are not just tactical — they’re strategic. Forecasting and disciplined planning are key to navigating memory constraints and locking in cost stability.

3. Cloud and hybrid options provide retailers flexibility: Recognising that not all retailers want on-prem hardware capex, SAI enables:
   • Cloud-assisted inference for variable workloads
   • Hybrid edge/cloud pipelines that balance latency, cost, and performance

   This flexibility makes it easy to scale Visual AI without locking retailers into expensive hardware refresh cycles that could be undermined by memory price inflation.

4. Built for retail, not data centres: Unlike general-purpose AI stacks that assume abundant memory and GPU capacity, SAI Group’s platform is purpose-built for retail workloads — from loss prevention to checkout accuracy:
   • Lower memory footprint per model
   • Targeted inference paths for common retail scenarios
   • Fine-tuned performance that maximises ROI on compute spend.

Retail CXOs and Loss Prevention leaders need to act now

Given the evolving hardware landscape, retail CXOs and Loss Prevention leaders need to do the following:

1. Embed hardware cost risk into your AI planning: Memory and GPU volatility should be part of your annual budgeting and architecture evaluation.
2. Choose AI platforms with efficient, predictable performance: Platforms that align budget with outcomes — rather than raw compute consumption — will deliver more value with less cost risk.
3. Plan with partners who manage supply constraints: Hardware procurement and forecasting expertise will be a differentiator in 2026–2028.

At SAI Group, we have engineered a solution that helps retailers capture the power of Visual AI without being hostage to commodity price swings. Our architecture, integration partners, and flexible deployment options help reduce sensitivity to memory and GPU cost inflation — so you can focus on outcomes, not hardware overheads.

Final thoughts

The memory and GPU price environment isn’t a short-term blip; it’s a market transformation driven by AI itself. Retailers who lock in scalable, efficient Visual AI now – with platforms like the one offered by the SAI Group – will be better positioned to accelerate loss prevention, improve customer experience, and drive operational excellence without skyrocketing costs.

About SAI

As a leader in computer vision technology, SAI Group delivers cutting-edge, multi-modal AI solutions into retail environments. Using a unique platform approach, its technology uses existing camera systems to target losses, increase store safety, and underpin operational efficiencies.

All solutions are built from the ground up to ensure the highest levels of security and data protection, respecting the privacy expectations of the public and operating to stringent ethical standards while delivering substantial value to our clients. Globally, SAI monitors millions of transactions per day, protecting the revenues from tens of millions of product sales and hundreds of millions of customer interactions. Its models also accurately identify anti-social behaviour, aggression and violence, helping to de-escalate situations with real-time interfaces to security officers and operations centres.