While emerging technologies like artificial intelligence and blockchain have been capturing headlines, businesses must also keep an eye on evolving hardware trends. As companies pursue faster, smarter and more secure operations, hardware configuration is becoming a critical competitive differentiator.
In an era shaped by AI adoption, rising end-user expectations and tightening privacy regulations, IT leaders are reevaluating not only what hardware their organizations need, but also where it should reside and how it should be deployed. Below, members of Forbes Technology Council share key hardware strategies designed to deliver the flexibility, security and cost efficiency modern enterprises require.
1. AI-Embedded Hardware Security At The Edge
AI-embedded hardware security at the edge is becoming essential. By integrating intelligent processing directly into devices—servers, endpoints and storage—companies can achieve real-time, autonomous security; reduce latency; and protect privacy without cloud dependence. This hardware-native AI trend will be critical for secure, scalable operations in the near term. – Camellia Chan, Flexxon
2. Inference-Optimized Hardware
We’re seeing a shift toward inference-optimized hardware—systems designed specifically for running, not training, AI models. As model deployment scales, general-purpose GPUs waste energy and rack space. Purpose-built accelerators with high memory bandwidth utilization will be essential for cost-effective, real-time AI. – Thomas Sohmers, Positron AI
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3. Disaggregated Infrastructure
Disaggregated infrastructure is rising fast—separating compute, storage and memory lets companies scale AI workloads efficiently. Paired with smart NICs and GPUs, it’s the backbone for low-latency, high-throughput architectures in tomorrow’s data centers. – Sai Krishna Manohar Cheemakurthi, U.S. Bank
4. Field-Deployed Edge AI Accelerators
Edge AI accelerators are gaining traction—particularly in the insurance industry, where devices that can analyze claims locally are deployed in field adjusters’ kits. These devices slash cloud costs while preserving privacy. Key benefits include TOPS/watt efficiency, hardware-encrypted data pipelines, and precertification for IEC 62304 medical-grade reliability. The future is distributed intelligence. – Srinath Chandramohan, EY
5. Hybrid Cloud Infrastructure
Hybrid cloud—combining locally hosted servers and public cloud providers—is a trending configuration. This strategy can help businesses reduce costs while maintaining the flexibility to scale when needed. – Anto Joseph, Eigen Labs
6. Edge-Enabled Safety Infrastructure
As a public safety company, we’re seeing increasing demand for edge-enabled safety infrastructure—devices like smart panic buttons, mobile gateways and compact edge processors that can locally process video, audio or wellness data before syncing with cloud-based platforms. This reduces latency, enables real-time decision-making in emergencies, and enhances privacy by limiting data exposure. – Kevin Mullins, SaferMobility
7. AI-Enabled Edge Computing
A key trend is integrating edge computing with AI, enabling real-time data processing near the source. This reduces latency and bandwidth, which is crucial for IoT and smart systems. Advances in processors and AI chips facilitate local data analysis, enhancing decision-making and efficiency. Adopting this trend can help companies in data-driven industries cut costs, improve performance and stay competitive. – Gautam Nadkarni, Wipro
8. Localized Foundation Model Deployment
Enabling hardware to run localized foundational models is key. Current foundation models and large language models require significant infrastructure and pose security risks due to generalization and centralized data processing. New approaches deploy personalized models on small devices like watches or phones, enabling secure local processing and paving the way for personalized AI assistants. – Abhijeet Mukkawar, Siemens Digital Industries Software
9. On-Site Edge Computing In Factories And Telecom Sites
A growing trend is building edge computing setups equipped with GPUs or AI chips close to where data is generated. The advantages include reduced delays, because information is processed locally; bandwidth savings; and scalability. These reliable, flexible and modular hardware configurations allow factories and telecom sites to run AI-powered tasks on site, enabling faster responses, stronger data protection and more efficient workload management. – Maman Ibrahim, EugeneZonda Cyber Consulting Services
10. Data Center Layouts Built For AI
Local compute is making a comeback. Everyone chased the cloud—until inference costs punched them in the face. We used to fight over RAM; now it’s NVMe lanes, PCIe bandwidth and power delivery. Welcome to the AI hardware wars! But AI-native workloads demand rack design, not just chip choice. If your data center layout hasn’t changed since 2015, you’re not ready. – Mirror Tang, ZEROBASE
11. Energy-Optimized Hardware
We’re in the early stages of a shift toward energy-optimized hardware. Organizations are investing in renewable-powered data centers to meet ESG goals and reduce their carbon footprints. – Ohm Kundurthy, Santander Bank
12. Accelerated Compute AI Clusters
I’m seeing accelerated compute AI clusters doing double duty for both training and inference. The push toward agentic and multimodal AI requires significant processing power to solve complex problems and advance AI autonomy. – Steven Carlini, Schneider Electric
13. Modular Hardware Setups
One significant trend is the shift to modular hardware setups, such as servers and storage, which allow for scaling up or down as needed. This lets companies add power or space without a full rebuild, making it easier to keep up with changing needs and control costs. It’s a flexible approach that’s quickly becoming standard. – Ganesh Ariyur, Gainwell Technologies
14. Edge Computing With NPUs And Specialized Chips
One essential hardware trend is AI-accelerated edge computing. By processing data closer to its source with neural processing units and specialized chips, companies reduce latency, improve privacy and enable real-time decision-making. As AI becomes core to operations, edge intelligence will be critical for speed, scalability and resilience. – Rishit Lakhani, Nile
15. Heterogeneous Hardware Compatibility
Adopting heterogeneous hardware compatibility and mixed-hardware serving is essential. This enables the flexible use of diverse hardware types—GPUs, CPUs and ASICs—across generations and vendors, boosting capacity utilization and cutting costs. It supports scalable AI workloads by running models efficiently on mixed hardware fleets, increasing agility and sustainability. – Pooja Jain, Meta (Facebook)
16. Privacy-Driven Edge Computing
A growing hardware trend is edge computing—processing data closer to the user instead of relying entirely on the cloud. It’s becoming essential for real-time decision-making in privacy-sensitive environments. For example, in AdTech, edge setups enable brands to deliver faster, more compliant, personalized ads without sacrificing speed or data security. – Ivan Guzenko, SmartyAds Inc.
17. Hybrid CPU-GPU Architectures
A clear short-term trend is the adoption of hybrid CPU-GPU architectures optimized for AI and data analytics workloads. It’s important to understand that AI is no longer optional—it must be integrated into workflows. These architectures improve performance without requiring full infrastructure replacement, helping companies balance cost and efficiency. – David Barberá Costarrosa, Beeping Fulfilment
18. Chip-Level Security Integration
A key hardware trend is the integration of security at the silicon level—such as trusted platform modules, secure enclaves and hardware-based authentication. With rising cyberthreats and remote workforces, companies must adopt hardware that enforces zero-trust principles from the chip up to protect sensitive data and systems. – Raj Jhaveri, Greenlane™ Infrastructure
19. On-Device NPUs
One essential hardware trend is the adoption of neural processing units on personal devices. Newer PCs and devices come equipped with NPUs to handle AI workloads efficiently—on the device. As AI becomes integral to everyday workflows, devices without these chips risk falling behind in performance and capability. – Tarun Eldho Alias, Neem Inc.
20. Heterogeneous Compute
One key trend is the move to heterogeneous compute—combining CPUs, GPUs and AI accelerators—to handle growing machine learning workloads. Traditional CPUs can’t keep up with large models. Adopting specialized hardware like H100s, faster interconnects and memory-rich nodes is essential for faster training, cost efficiency and staying competitive in the AI era. – Karan Alang, Versa Networks Inc.
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