Product Overview and Investment Rationale
The hardware landscape of 2025 blends breakthroughs in CPUs, GPUs, memory, and storage with a growing emphasis on energy efficiency and intelligent, on-device processing. Investors should assess not only silicon performance but also software ecosystems, supply chain resilience, and the ability to scale across data centers, edge nodes, and embedded devices. Key areas include quantum computing advancements, AI integration in hardware, edge computing innovations, computational storage solutions, and sustainable manufacturing practices that reduce environmental impact. This H2 section outlines the market dynamics and investment rationale behind next-generation CPUs, GPUs, accelerators, and memory technologies, offering a roadmap for strategic capital allocation in 2025. By aligning product roadmaps with deployment trends and risk factors, stakeholders can balance short-term returns with long-term resilience.
Market trends shaping hardware demand in 2025
Market demand in 2025 is increasingly guided by efficiency, latency, and intelligent capability across edge and cloud workloads globally.
- Hybrid quantum accelerators are moving into research and production environments, enabling faster cryptographic processing, optimized material simulations, and specialized optimization tasks that are impractical for classical systems.
- AI-integrated CPUs, GPUs, and NP accelerators embed on-die neural engines, enabling real-time inference, autonomous control, adaptive power management, and workload-optimized performance without excessive cloud round trips.
- 5G networks and future beyond-5G standards are reshaping modem design, edge radios, and distributed storage, pushing for smaller form factors, higher integration, and secure, low-latency connectivity.
- Edge computing requires on-device AI, secure enclaves, and computational storage to minimize latency, reduce bandwidth costs, and enable resilient operation in remote or constrained environments.
- Nanotechnology enables finer lithography, advanced materials, and sustainable manufacturing, reducing waste, improving yield, and enabling longer lifecycles through durable components and modular upgrades.
Together, these dynamics shape funding priorities, supplier ecosystems, and the architecture choices that will define hardware platforms through 2025 and beyond.
Why invest in new CPUs and GPUs
Investing in the next generation of CPUs and GPUs is central to maintaining competitive advantage as workloads become more diverse and demanding. Modern data centers, edge deployments, and embedded devices require processors that fuse high raw throughput with intelligent on‑chip acceleration, enabling faster model training, accelerated inference, and more responsive real‑time systems. The trend toward AI integration in hardware, with dedicated neural processors, sparse compute engines, and programmable accelerators, makes it possible to run large language models and vision tasks closer to the data source while conserving bandwidth and reducing energy use. In addition, advances in memory hierarchies, higher memory bandwidth, and computational storage capabilities help maintain data locality, further boosting overall system efficiency. As vendors push toward hardware-software co-design, the value of a future-ready CPU/GPU platform grows beyond a simple clock speed race, encompassing AI features, security, and resilience.
Strategically, quarterly refresh cycles and regional supply diversification reduce risk from single-supplier bottlenecks and geopolitical tensions. Each generation brings better performance per watt, enabling higher throughput without proportionally increasing power budgets. Process node advances, improved memory channels, and better I/O subsystems create opportunities for more capable platforms that can support robotics, edge AI, and real-time analytics in manufacturing, healthcare, and automotive contexts. The long tail of workloads—computer vision in surveillance, natural language processing in customer service, and scientific simulations—benefits from specialized accelerators and unified software stacks. This alignment between hardware capability and software demand accelerates time-to-market for differentiated products, drives higher device density, and supports sustainable operating costs over deployment lifecycles.
Moreover, sustainability in hardware manufacturing is increasingly a core criterion for procurement. Fans, power supplies, and cooling can be redesigned for higher efficiency, while recyclable materials and modular upgrades extend product lifespans. With the rise of robotics in future hardware, dependable CPUs/GPUs must work in harmony with autonomous control systems, sensor fusion, and edge orchestration. Finally, quantum computing advancements remain a strategic horizon, informing roadmaps and risk-management plans, even as practical quantum workloads remain limited to specialized environments today.
The integration of computational storage solutions and nano-manufacturing techniques further influences ROI, but returns depend on execution, supply chain resilience, and software ecosystem maturity.
Risks and long-term ROI considerations
The ROI from CPU/GPU upgrades is strongest when deployments align with enterprise AI workloads, edge autonomy, and data-centric architectures. Early-adopter cycles bring premium pricing and faster time-to-value, but they also carry execution risk as software ecosystems mature. Aligning platforms with software demands, memory bandwidth, and accelerator availability is essential to achieving expected payback periods.
Depreciation and lifecycle considerations are significant; hardware can be rendered obsolete quickly by software requirements, new codecs, or API changes. ROI depends on balancing initial capex with ongoing operational savings from energy efficiency, higher throughput, and reduced cloud costs. Careful planning around refresh cadence, reuse of existing infrastructure, and the ability to repurpose components is critical to mitigating depreciation risk.
Supply chain instability and pricing volatility present additional hazards. Single-source dependencies, logistics delays, and geopolitical constraints can inflate total cost of ownership and stretch ROI horizons. Cybersecurity risks, especially as quantum-era threats loom, require ongoing investment in secure firmware, hardware-rooted encryption, and robust update mechanisms to protect deployed hardware and data.
To improve resilience, organizations should diversify supplier bases, validate software ecosystems early, and run strong total cost-of-ownership analyses that incorporate energy, maintenance, and upgrade paths. Clear use-case validation and staged deployment help ensure that ROI aligns with real-world performance over the product lifecycle.
Key Features and Technical Specifications
The hardware landscape in 2025 is defined by AI integration at the silicon level, new CPU and GPU architectures, and memory innovations that reduce latency and increase throughput. Advances in chiplets and RISC-V adoption create modular designs that scale from mobile to data center. On-die neural processing units and domain-specific accelerators are becoming common, accelerating AI workloads without overwhelming power budgets. The year also brings new memory fabrics and storage interfaces that speed data movement, including CXL and next gen NVMe. Sustainability and security considerations, including quantum-resistant measures and green manufacturing, are shaping product roadmaps across major vendors.
CPU architecture advances (NPU, chiplets, RISC-V adoption)
CPU architecture in 2025 shows a move toward heterogeneity where compute cores, on-die neural processing units, and accelerator tiles work in concert to optimize workload placement. Chiplet ecosystems are expanding, enabling vendors to mix high-performance compute tiles with IO and memory tiles tuned for specific markets, improving yields and time to market. RISC-V adoption is gaining traction in both embedded and data center contexts, offering customizable extensions that can be targeted for security, determinism, and energy efficiency. In desktop and workstation contexts, designers strive for high IPC, large caches, and robust memory bandwidth to support simulation, content creation, and AI refinement. Mobile and edge platforms emphasize leakage reduction and dynamic power management, leveraging modular stacks to balance performance with energy budgets. The overall result is a more diverse, interoperable landscape where silicon is designed for workload-specific optimization rather than a single universal path.
| Architecture | Core Model | Target Workloads | Typical Power (W) | Strength |
|---|---|---|---|---|
| Chiplet-based multi-die designs | Compute die + IO die | Desktop/Workstation AI, HPC | 120-260 | High throughput, scalable performance |
| NPU-augmented CPUs | Integrated neural processing units on die | AI inference, real-time analytics | 70-140 | Low-latency AI acceleration, energy efficiency |
| RISC-V adoption with open ISA cores | Open specifications, modular cores | Embedded to server workloads | 25-90 | Customization and security features |
| Monolithic high-frequency cores with ISA extensions | Single-die, high IPC | Gaming, latency-sensitive workloads | 95-250 | Max single-thread performance, latency |
In practice, this landscape maps directly to how software targets run on hardware, guiding compiler strategies and runtime optimizations. Desktop and edge configurations diverge in the same way as data center accelerators diverge for AI workloads, shaping procurement, cooling, and software ecosystems. Practically, these options influence how developers partition workloads between CPU cores and accelerators. As AI and ML workloads grow, expect more modular, interoperable platforms that pair compute tiles with near memory and fast interconnects.
High-performance desktop and workstation designs
Desktop and workstation platforms in 2025 push the envelope on IPC, memory bandwidth, and multi-die integration. High clocked compute dies paired with fast IO and memory tiles enable smooth handling of demanding workloads such as real-time simulation, professional rendering, and large-scale machine learning refinement. PCIe 5/6, fast interconnect fabrics, and memory technologies like HBM or high-density DDR packages feed data to accelerators with minimal latency. Thermal management remains a central constraint, driving advanced cooling solutions and precision power management to sustain turbo performance. Software tooling must evolve in tandem, with compilers and profilers learning to map heavy AI tasks to accelerators while preserving responsiveness for gaming and professional applications. Sustainability considerations are increasingly integrated into design choices, from materials to energy sourcing and end-of-life recycling, shaping consumer expectations and vendor roadmaps.
Datacenter and AI accelerators
In data centers, modular multi-die platforms and high bandwidth memory stacks underpin scalable AI workloads. Accelerators with tensor cores and DPU-like units sit close to CPUs and memory to shorten data paths, while interconnects such as NVLink and PCIe 5/6 enable rapid cross-device memory sharing. CXL memory pooling and coherent caches across modules improve utilization and reduce waste, supporting both training and inference at scale. Power density and cooling complexity are balanced with sophisticated dynamic power management and intelligent workload scheduling. Security features including hardware root of trust help protect multi-tenant environments, aligning with quantum-resistant security considerations as part of forward-looking governance. Hyperscale operators also pursue greener data centers, integrating renewables and waste reduction into procurement and operation.
Low-power mobile and edge chip designs
For mobile and edge devices, energy efficiency remains paramount. Designers emphasize leakage reduction, dynamic voltage and frequency scaling, and integrated AI blocks that handle on-device inference without leaving the device. Chiplet stacks and modular packaging enable devices to scale down capabilities for wearables, cameras, and autonomous sensors while preserving responsiveness. 5G connectivity and evolving edge workloads drive architectural choices that minimize latency and data movement to the cloud, enabling better privacy and faster decision making at the source. Thermal and form-factor constraints drive novel cooling, materials, and packaging solutions, while software ecosystems tighten integration with AI toolchains and real-time analytics frameworks. Green computing initiatives and environmentally conscious manufacturing practices are increasingly part of the mobile and edge hardware brief as networks expand and devices proliferate.
GPU evolution and specialized accelerators
GPU evolution continues to redefine performance envelopes as rasterization gives way to real-time AI inference, high-fidelity rendering, and large-scale scientific computation. Modern GPUs incorporate larger on-die caches, faster interconnects, and memory subsystems designed to minimize data movement between host CPUs and accelerators. Dedicated tensor cores and specialized matrix engines accelerate neural networks, enabling faster training and lower inference latency for both data center and edge deployments. Ray tracing, AI-assisted upscaling, and hardware-accelerated video processing have matured, guiding developers toward new rendering pipelines and efficient AI-driven workflows. In data centers, GPUs operate as multi-tenant accelerators that share interconnect fabrics and scale to meet diverse workloads, from climate modeling to NLP inference. AI integration extends to on-die blocks for pre and post processing, sensor fusion, and real-time analytics at the edge, enabling responsive devices and autonomous systems. As 5G networks expand, edge compute becomes increasingly capable, reducing cloud round trips and accelerating decision making. Toolchains and compilers are increasingly designed to map high level models to architecture primitives, optimizing memory traffic, fusion, and tiling strategies while balancing cybersecurity and data locality concerns. The broader agenda includes ethical AI in hardware, as well as environmental considerations tied to chip fabrication and energy use.
Memory and storage breakthroughs (HBM, CXL, NVMe)
Memory and storage breakthroughs are reshaping data movement and architectural strategy across all compute tiers. HBM and stacked memory technologies continue to raise bandwidth, reducing bottlenecks for GPUs and AI accelerators while enabling denser, more energy-efficient layouts. CXL 3.0 and beyond enable memory pooling and cache coherence across CPUs, GPUs, and accelerators, supporting flexible, tiered memory architectures that improve utilization in data centers and at the edge. NVMe over fabrics with optimized namespaces and QoS support scalable storage pools, enhancing data locality and throughput for hyperscale workloads and distributed edge deployments. Storage class memory and persistent memory options blur the line between RAM and storage, delivering near-DRAM speeds for analytics pipelines and large databases. Computational storage devices push processing closer to the media, reducing data movement and enabling on-device analytics for IoT, robotics, and industrial sensing. Alongside these advances, researchers are exploring bio-inspired and nano-scale memory concepts to push energy efficiency further while reducing silicon real estate requirements. Taken together, these trends enable end-to-end data locality and smarter data management across compute tiers.
Performance Benchmarks, Compatibility, and Reliability
The year 2025 marks a turning point where hardware performance, cross-platform compatibility, and reliability converge to enable more capable and sustainable systems. Designers balance raw speed with energy efficiency, sustainability in hardware manufacturing, and faster time-to-market for AI on-device and edge solutions. Quantum computing advancements start to influence accelerator design and security strategies, even as traditional CPUs and GPUs continue to push performance ceilings. Edge computing innovations bring latency-sensitive workloads closer to data sources, unlocking new possibilities for robotics, IoT, and real-time analytics. The ecosystem expands to accommodate computational storage solutions, nanotechnology in hardware design, and stronger sustainability initiatives, while addressing the implications of 5G on hardware development and cybersecurity resilience.
Expected benchmark improvements and metrics to watch
Benchmark improvements in 2025 will be measured across multiple dimensions, reflecting a shift from single metric wins to a more holistic view of compute, memory, and data handling under real workloads. A core focus remains end-to-end throughput, with throughputs described in terms of raw FLOPS, AI-specific operations per second, and sustained, real-world latency under varied task mixes. Vendors will lean on MLPerf for AI throughput, while traditional suites like SPECint, SPECfp, and emerging memory benchmarks will accompany these results to illustrate cross-domain performance. In addition, engineers will emphasize energy efficiency by reporting performance per watt under both peak and sustained conditions, recognizing that data centers, edge nodes, and consumer devices all demand responsible power use.
Computational storage solutions will increasingly factor into the benching narrative, evaluating not only bandwidth and IOPS but also on-device processing capabilities within storage controllers that reduce data movement and lower latency for streaming analytics and machine learning pipelines. Benchmarking will also probe memory subsystem behavior with high bandwidth memory, stacked caches, and interconnect latency, since memory is often the bottleneck in large models and complex simulations. In graphics and compute workflows, benchmarks will assess how well systems maintain predictable performance during noisy neighbor scenarios, thermal throttling, and dynamic frequency scaling, which are common in real-world deployments.
5G and edge-enabled workloads will shape new benchmark scenarios by stressing latency budgets, edge-to-cloud offloading patterns, and communication overheads. Auditors will seek metrics that unify compute, memory, storage, and networking in a way that reflects what users actually experience—fast startup times for apps, responsive inference, and smooth multimedia rendering. Finally, researchers will explore how quantum computing advancements and associated accelerator stacks affect security, cryptography, and resilience, ensuring benchmarks account for future-proofing without overstating near-term gains.
Cross-platform compatibility and ecosystem support
Cross-platform compatibility and ecosystem support is a practical prerequisite for the next generation of hardware, ensuring that software, tools, and services can run efficiently across devices and architectures.
The following compatibility indicators highlight factors that influence how quickly new hardware can be adopted at scale, from compiler maturity to platform governance, and from ecosystem readiness to partner alignment.
- Compiler and runtime portability across x86, ARM, RISC-V, and dedicated accelerators, emphasizing ISA stability, binary compatibility, and consistent performance across heterogeneous systems.
- Memory subsystem benchmarks quantify sustained bandwidth and latency under mixed workloads, including AI inference, data analytics, and rendering, with a focus on cache efficiency and memory tiering.
- Interconnect performance and NUMA locality across multiple sockets, PCIe gen4/5, CCIX or CXL, and memory fabrics, shaping how the platform scales with adding accelerators and external memory resources.
- AI on-device tooling and compiler pipelines that deliver portable models with minimal quantization overhead, ensuring models run efficiently on diverse hardware while preserving accuracy and security.
- Software ecosystem maturity including robust debuggers, validated model libraries, and standardized validation suites for cryptography and multimedia workloads across vendors.
Observers will track these indicators to better forecast total cost of ownership, developer productivity, and the ability to secure and operationalize AI, IoT, and robotics workloads across diverse environments.
Reliability, thermal design, and lifecycle considerations
Reliability in 2025 hinges on more than defect-free silicon; it requires predictable behavior over time and under stress. Manufacturers will publish MTBF figures and accelerated life testing data, but real-world reliability depends on the accuracy of burn-in procedures, firmware update strategies, and fault-tolerance in memory and storage subsystems. Field data will be analyzed to forecast failure modes across diverse workloads, helping buyers plan maintenance windows and spare-part provisioning. Across devices from edge sensors to data center accelerators, manufacturers will pursue better error detection, ECC correctness, and resilience to transient faults. The software stack, including driver firmware, hypervisors, and orchestration layers, will be part of reliability assessments because a stable platform requires harmonious interactions between hardware and software.
Efficient thermal design is central to reliability; high-density CPUs and accelerators generate significant heat, necessitating advanced cooling options. Designers will explore improved heat sharing, vapor chambers, liquid cooling loops, and immersion cooling for servers and high-performance GPUs. Sensors distributed across a chassis or module will provide real-time feedback to dynamic cooling strategies, preventing hot spots and reducing thermal throttling events. The objective is to maintain performance headroom while minimizing noise, deposit, and energy usage. A robust thermal design also means predictable degradation curves, so user expectations remain aligned with actual device longevity.
Lifecycle planning includes predictability of component aging; memory cells, NAND flash wear, and DRAM refresh cycles influence end-of-life timing. Manufacturers and operators will rely on predictive maintenance analytics to schedule firmware updates, spare-part inventories, and hardware refresh cycles that optimize total cost of ownership. Green computing initiatives will push for easier recycling, reduced material waste, and better product containment at end of life. Certifications and traceability will be emphasized to ensure supply chain integrity and environmental compliance. Robotics in future hardware and edge devices will demand high reliability as automation becomes more pervasive; as such, product life cycles will be measured in both years of service and the software-supported horizon.
Offers, Financing, and Promotion Options
Buyers today expect clear, flexible options when investing in next generation hardware. This section highlights the current and forthcoming offers that blend financing, promotions, and asset lifecycle management. From enterprise procurement programs to consumer upgrade incentives, vendors are aligning incentives with rapid tech refresh cycles. You will see how financing models impact total cost of ownership, depreciation, and operational readiness across compute, storage, and AI accelerators. We also consider sustainability criteria and governance when evaluating promotions.
Enterprise procurement and financing models for 2025 hardware
2025 hardware cycles demand procurement strategies that balance performance requirements with cash flow discipline. Enterprises increasingly plan hardware refresh programs that couple compute, memory, storage, and accelerators with software upgrades, training, and security upgrades. Rather than single, large capital purchases, many organizations adopt modular bundles that scale with demand, enabling phased deployments, pilot programs, and interoperability with existing data centers and edge deployments. Market entrants offer adaptable financing options tied to consumption and utilization metrics, so finance teams can forecast expenses with greater precision while IT aligns capex with strategic initiatives such as AI workloads and data governance. This shift reframes procurement as a lifecycle program rather than a one off purchase.
Capital expenditure models remain viable for core data center upgrades, but operating expenditure, subscription, and hardware as a service models are gaining traction in both enterprise and hybrid environments. With HaaS, vendors package hardware, software, and services into predictable monthly payments that include maintenance, firmware updates, and asset disposal. This approach reduces upfront risk, accelerates time to value, and enables rapid scaling as workloads evolve. For public sector and regulated industries, financing packages often include strict audit trails and compliance features, ensuring traceability from budget planning to end of life disposition. Tax incentives and depreciation schedules also influence the attractiveness of different structures.
Leasing and financing terms can balance control and flexibility. Operating leases preserve lines of credit for strategic initiatives and convert capex into revenue like operating expenses, while capital leases enable gradual ownership with predictable residuals. Vendors frequently offer tailored terms based on expected utilization, asset type, and technology lifecycle, including options for buyouts at favorable rates or return rights if performance thresholds are not met. Interest rates, maintenance commitments, and upgrade allowances should be negotiated upfront, along with service level expectations for firmware and security updates. Forecasting tools help IT and finance model scenarios across refresh cycles and workload mix.
Financing choices extend beyond the original vendor. Banks, fintech lenders, and channel partners frequently provide pre approved lines of credit, vendor agnostic financing, or blended arrangements that combine leasing with managed services. For complex AI and HPC deployments, integration with cloud credits, data center consolidation, or co location services can further optimize cash flow and capital planning. Procurement teams should evaluate total cost of ownership over the full lifecycle, including initial deployment, software subscriptions, energy consumption, and end of life equipment recycling. Clear governance around approval thresholds, asset tagging, and performance metrics helps protect budgets while enabling innovation.
Lifecycle planning is anchored in a predictable refresh cadence aligned with technology roadmaps and workload demands. Enterprises benefit from pre approved templates, RFP packages, and standardized vendor agreements that speed procurement while maintaining control over risk, security, and compliance. Asset tracking, depreciation schedules, and automated notifications for warranty expirations enable proactive budgeting and maintenance. Financing can be synchronized with software renewals, allowing bundles that reduce total monthly spend while ensuring access to the latest CPUs, GPUs, and accelerators. In the era of edge computing, procurement must also account for remote sites, remote management, and consistent security postures across distributed infrastructure.
Consumer upgrade programs, trade-ins, and promotions
Consumer upgrade programs help individuals stay current with rapidly advancing hardware while managing budgets and trade in values. Below are common program formats you are likely to encounter in major consumer channels and open market ecosystems.
- Trade in based upgrades allow customers to refresh devices at regular intervals, applying the value of their existing hardware toward the purchase of newer models and reducing net upfront costs.
- Promotional bundles combine discounted devices with extended warranties, software subscriptions, or accessory credits, creating predictable monthly expenses while delivering a fuller technology stack.
- Flexible financing plans offer low or zero percent interest periods, accelerating upgrades for families and businesses alike and enabling faster adoption of AI enabled consumer devices.
- Member loyalty programs reward continued purchases with points, store credit, or device protection, encouraging prolonged engagement and higher average order value over multiple generations.
- Trade in programs often include appraisals based on device condition, storage capacity, and market demand, ensuring fair value and a transparent path to lower cost replacements.
These formats vary by region and retailer, so compare terms across vendors and consider the total value beyond sticker price.
Always check warranty coverage, data migration support, and environmental impact when choosing a program.
Total cost of ownership and long-term support plans
Total cost of ownership is a comprehensive lens through which hardware decisions should be evaluated, extending far beyond initial purchase price. When IT leaders model TCO, they incorporate energy consumption, cooling requirements, maintenance, spare parts, software licenses, and staff time for deployment and administration. Runtime efficiency of CPUs, GPUs, memory, and accelerators directly affects energy costs over the device’s life, making energy management an ongoing discipline. Peripherals, adapters, and interconnects contribute to both upfront and recurring expenses, while virtualization, containerization, and orchestration tools influence utilization and performance density. A disciplined TCO exercise also captures end of life and resale value, which can recover funds from retired equipment.
Long term support plans are essential to maximizing hardware value, especially for mission critical workloads. Vendors offer varying tiers of warranty coverage, service level agreements, and proactive maintenance windows that align with business calendars. On site coverage can reduce downtime for servers and storage devices, while remote diagnostics enable faster fault isolation and firmware rollouts. In edge deployments, support programs must address remote locations, limited connectivity, and secure data handling. Organizations should negotiate response times, spare part availability, and upgrade paths for firmware and security patches, ensuring that protection keeps pace with evolving threat models and software dependencies.
Warranty and service by the month models can be attractive for budgeting, but long term planning benefits from total guarantees that include spare parts, software updates, and access to compatibility test libraries. When evaluating warranties, IT teams should request coverage for both hardware and included firmware components, clarify renewal costs, and verify whether third party components are supported. Consider including escalation processes and dedicated technical resources for high priority systems. A strong support plan should translate into measurable outcomes, such as reduced mean time to repair, faster recovery from outages, and predictable upgrade windows that minimize production risk.
Longevity expectations depend on workload characteristics, thermal design, and the pace of software advances. Enterprise servers and accelerators can sustain five to seven year lifecycles with periodic upgrades to firmware and drivers, provided that cooling remains effective and power delivery is stable. HPC and AI systems may require more frequent refreshes as drivers and models evolve, while edge devices can extend life through modular components and field replaceable units. A clear roadmap for interim refreshes helps budgeting and capacity planning, ensuring that compute capacity aligns with forecast demand and data growth trajectories.
Transparency in procurement and lifecycle management reduces surprises and improves stakeholder alignment. Documentation of baseline configurations, warranty terms, and upgrade paths ensures that procurement teams and finance can anticipate budgets and depreciation schedules. Regular audits of asset usage, energy efficiency, and end of life disposition support sustainability goals while preserving performance headroom for future workloads. By pairing TCO rigor with robust support and environmental stewardship, organizations can keep hardware investments lean, scalable, and capable of delivering consistent performance across multi cloud and edge architectures.