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CRN selected 25 companies for its 2026 AI 100 infrastructure and edge-computing category. The list spans processors, servers, storage, networking, data protection and edge management—not just AI chips. CRN calls the companies “hottest,” but the article does not disclose a scoring method or rank them against one another; the names are presented alphabetically. Treat the list as a market map, not a performance verdict or a recommendation to buy.
That distinction matters for technology budgets: the right choice depends on the workload, location, software compatibility and operating costs. A GPU supplier, backup provider and edge-orchestration vendor may all support AI, but they solve different problems.
What CRN’s infrastructure and edge list covers
CRN’s broader 2026 AI 100 is divided into cloud, cybersecurity, data and analytics, infrastructure and edge computing, and software. The infrastructure category covers systems that support AI from data centers to distributed sites. That includes centralized resources for training, fine-tuning and inference, as well as computing near the places where data is generated—such as factories, stores, branches and vehicles.
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Edge AI can reduce latency, limit data transfers or keep processing available when connectivity is unreliable. It also creates work: teams must secure, update, monitor and recover devices distributed across many locations. Hybrid AI combines cloud, data-center, private-cloud and edge resources rather than assuming one location suits every workload.
#1 Best Overall
Nimo AI NAS, Agentic Computer Mini PC and AI Server, AMD Ryzen 7 PRO 8845HS(up to 5.1 GHZ, beat i5-1235u) up to 132TB ZFS Hybrid Storage, Dual 10GbE for 24hr AI Agent
- [Local AI Inference & 70B Model Ready] Equipped with the AMD Ryzen 7 PRO 8845HS processor, NEXUS is engineered for heavy local AI workloads. With a full-size GPU bay, it runs 70B LLMs natively without an internet connection. Ideal for AI developers and tech enthusiasts who need private environment for coding and model testing.
- [132TB Mass Storage with ZFS Integrity] Features a hybrid storage architecture (3×NVMe + 4×3.5" HDD) supporting up to 132TB. Utilizing the enterprise-grade ZFS file system and ECC memory, it prevents data corruption and bit rot—a must-have for professional photographers and video editors safeguarding 4K/8K RAW footage.
- [OpenClaw-Driven Automation Workflow] The built-in OpenClaw execution layer allows complex automated tasks to be processed locally. Even when offline, your backup schedules and AI file organization continue seamlessly. Say goodbye to monthly cloud subscriptions and high latency.
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- [Open-Source ZimaOS for Total Privacy] Running on the fully open-source ZimaOS, NEXUS ensures your data stays physically on-premise with no backdoors. It acts as a "Digital Fortress" for privacy-conscious families and small businesses who demand absolute data sovereignty.
CRN reported Gartner estimates of $2.53 trillion in worldwide AI spending for 2026, including $1.37 trillion for AI infrastructure—more than 54 percent of the total in CRN’s presentation of the estimate. These are forecasts, not measured spending results. The figures help explain the category’s breadth; they do not establish that any particular vendor will deliver a return on investment.
The 25 companies, grouped by role
The following descriptions summarize the products and positioning CRN highlights. They do not establish independent performance, availability, pricing or suitability. Product names, configurations and corporate branding can change, so verify them with vendors before making a purchasing decision.
Rank #2
Accelerated compute and silicon
- AMD: Supplies CPUs, GPUs, accelerators and networking components, alongside software such as ROCm, Vitis AI and ZenDNN. It may merit evaluation where CPU-and-GPU options, software compatibility or alternatives to a single-vendor accelerator stack matter. Test the target models and frameworks directly; an alternative ecosystem is not automatically interchangeable with Nvidia’s.
- Intel: Offers Core Ultra processors with CPU, GPU and NPU components, Xeon processors, Gaudi accelerators and OpenVINO tools. Its broad x86 and edge presence can be relevant for enterprise and local AI deployments. Validate model, library and framework support for the intended workload rather than assuming every accelerator runs it equally well.
- Nvidia: Spans GPUs, data-center and embedded systems, networking, AI cloud services, server components and software. This breadth makes it a vertically integrated option across several infrastructure layers. CRN characterizes Nvidia as industry-leading, but its list does not compare performance, availability, power efficiency or total cost on a common basis.
- Qualcomm: Focuses on Snapdragon processors used across PCs, smartphones, vehicles, IoT and other devices, with an emphasis on low-power, device-side and distributed AI. It is more relevant to local inference than to centralized, large-scale model training. Check NPU capability, memory, thermal limits and support for the specific models and developer frameworks involved.
AI PCs, workstations and local AI
- Acer: CRN highlights Veriton GN100 AI Mini workstations using Nvidia’s Grace Blackwell GB10 Superchip, AI-ready PCs with Snapdragon X, Intel Core Ultra or AMD Ryzen processors, and Acer Intelligence Space software. Compact systems can suit development and smaller local workloads, but available configurations and capacity matter; confirm regional availability, memory and the intended model size.
- HP Inc.: Offers AI PCs with NPUs, AI workstations and AI-enabled printer features. Its place on this list is chiefly at the endpoint and workstation layer, not as a substitute for data-center compute. The printer features are peripheral to the main infrastructure question.
- Lenovo: Has a hybrid portfolio spanning ThinkSystem servers, ThinkEdge systems, software-defined storage, AI-ready servers and XClarity One management. That breadth can connect data-center and edge deployments. Distinguish Lenovo’s hardware and management tools from configurations that rely on third-party accelerators or software.
Servers and integrated AI systems
- Dell Technologies: Its Dell AI Factory positioning brings together AI-ready servers, PCs and workstations, storage, networking, software and cyber-resilience products. Buyers can assess it as an integrated procurement and lifecycle-management option, then compare a configured architecture with buying individual components. Ask what is included in the proposed design, support and services.
- Hewlett Packard Enterprise (HPE): CRN cites Nvidia AI Computing by HPE, HPE Private Cloud AI, and HPE’s compute, storage, networking and software portfolio. This is aimed at organizations seeking a private or hybrid deployment assembled as an integrated system. HPE’s statement that some use cases can be deployed in hours is a company claim, not a universal implementation timeline.
- Supermicro: Supplies a wide range of AI-ready servers for training, inference and edge use, including GPU-focused systems and storage offerings. Configuration breadth can be useful, but buyers should assess integration responsibilities, support arrangements, rack density, power and cooling—not just the accelerator configuration.
Storage and AI data platforms
- DDN: Focuses on high-performance storage for AI pipelines. CRN reports DDN’s claim that its platforms can achieve up to 99 percent GPU utilization. That is a vendor-reported figure, not a guarantee: actual accelerator utilization depends on the data pipeline, software, workload and system configuration.
- Everpure: Described by CRN as formerly known as Pure Storage, it focuses on AI data pipelines, training and inference acceleration, automation and data readiness. Verify current company and product names, contract details and support arrangements directly, as corporate branding can change.
- Hitachi Vantara: CRN highlights Hitachi iQ, AI-ready storage and AIOps. The vendor’s enterprise storage and analytics experience may be relevant to large data estates. Claims such as “best-in-class economics” should be treated as promotional unless supported by a comparison that matches your workload and costs.
- NetApp: Promotes an intelligent data platform, NetApp AI Data Engine and integrations that CRN says include Nvidia DGX SuperPOD-certified performance and scale. Buyers should examine how data access, governance and movement work across their existing on-premises, cloud and edge environments. Storage performance alone does not establish end-to-end model throughput.
- Vast Data: Positions its offering as an “AI Operating System,” bringing together storage, database and compute functions for training, inference and autonomous-agent workloads. That phrase is the company’s positioning, not a standardized product category. Assess which components the proposed deployment actually includes and whether the platform replaces or complements existing systems.
- Weka: CRN names NeuralMesh and describes data, compute and AI services spanning edge, core, hyperscale cloud and neocloud settings. Its performance-oriented data infrastructure is aimed at AI-heavy environments. A buyer should compare its architecture with conventional network-attached storage, parallel file systems and object storage for the intended workload.
Networking, security and application delivery
- Cisco Systems: Covers AI-optimized networking, networking silicon, security, observability, an AI-ready edge platform and Cisco IQ. Networks are critical to moving AI data and serving distributed workloads. Evaluate capacity for east-west traffic, security controls, observability and fit with existing infrastructure; a networking product is not an accelerator.
- Extreme Networks: Offers cloud-managed wired and wireless networking, security and analytics, along with Extreme Platform One, secure fabric and Extreme AI. Its AI features concern network operations and management; do not confuse AIOps with infrastructure that accelerates model training or inference.
- F5: Works in application delivery, security and performance management, including AI delivery, model-security positioning and multi-cloud orchestration. Its role is closer to managing and protecting application traffic than supplying compute. Clarify whether the requirement is model or API traffic management, an inference gateway, conventional load balancing or something else.
Data protection and resilience
- Cohesity: CRN highlights Cohesity Data Cloud, AI-driven data resilience and threat detection, and Cohesity Gaia for extracting value from historical unstructured data. Backup and recovery, data security, and using archived data for AI are connected but distinct jobs. Buyers should identify which of those requirements they need met.
- Veeam Software: CRN describes data mapping across the data estate and AI lifecycle, AI-pipeline security, a context-aware LLM firewall and automated data sanitization. Before evaluating such capabilities, establish what needs protection—datasets, models, prompts, vector indexes, configurations or applications—and define recovery requirements for each.
Hybrid, distributed and edge infrastructure
- Nutanix: CRN highlights a cloud operating model for AI agents, Nutanix Agentic AI and Nutanix Enterprise AI for controlled LLM-endpoint deployment. Buyers should determine whether they need the broader hyperconverged infrastructure stack, an AI endpoint-management capability, or both.
- Scale Computing: CRN says Scale Computing was acquired by Acumera, which adopted the Scale Computing brand for its broader edge-focused portfolio. Its SC//Platform is described as covering edge compute, networking, storage, security and decentralized processing with autonomous management. Confirm current ownership, branding, product roadmap and support terms before procurement.
- StorMagic: Offers SvHCI software, SvSAN virtual SAN and Edge Control fleet management. Its focus is hardware-flexible infrastructure and centralized management for edge sites. Validate supported hardware, behavior during WAN outages, minimum deployment requirements, patching and recovery procedures.
- Zededa: Its Edge Intelligence Platform is positioned to orchestrate infrastructure, inference and autonomous agents across heterogeneous hardware, with centralized control and hardware-based security. This is a fleet-management proposition rather than a single edge appliance. Verify supported hardware and runtimes, offline behavior, observability, security controls and rollback procedures.
How to shortlist vendors for an actual deployment
Start with the job to be done, not the word “AI” in a product name. A training cluster, private retrieval-augmented generation (RAG) service, branch inference fleet and AI workstation have different constraints. Use these questions to reduce the field:
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- Define the workload. Is it training, fine-tuning, inference, RAG, computer vision, analytics or agentic software? Record latency, throughput, concurrency, model size and availability needs.
- Locate the data and processing. Decide whether data can move to a public cloud or must remain on-premises or near its source. For edge locations, include offline periods and physical access in the design.
- Check software and hardware compatibility. Verify support for the intended model, framework, libraries, drivers, accelerator, memory and deployment environment—containers, virtual machines, Kubernetes or bare metal. A software ecosystem’s breadth does not guarantee support for every model operation.
- Identify the real bottleneck. Slow results may come from data ingestion, preprocessing, storage, networking, memory or orchestration rather than a lack of accelerators. Compare end-to-end results, not a single throughput or utilization claim.
- Plan operations and security. Account for identity and access, encryption, segmentation, monitoring, patching, audit logs, backup, recovery and remote hardware replacement. At the edge, include fleet-wide model-version control and a plan for intermittent connectivity.
- Compare the complete cost. Request a multi-year estimate that includes hardware, software, support, services, power, cooling, networking, storage, data transfer and operating labor. Ask about minimum deployment size, licensing, accelerator availability and exit or migration costs.
Which parts of the list deserve attention by scenario?
- Large-scale training or GPU-heavy pipelines: Compare accelerator and server designs from Nvidia, AMD, Intel, Dell, HPE, Lenovo and Supermicro, alongside storage options such as DDN, Weka, Vast Data and NetApp. Test the whole pipeline, including storage and network behavior.
- Private or hybrid enterprise AI: Compare integrated approaches from Dell, HPE, Lenovo and Nutanix, while assessing accelerator, data and management dependencies. The value of an integrated system is less assembly work; the trade-off can be reduced flexibility or greater reliance on one platform.
- AI PCs, developer workstations or local inference: Acer, HP Inc. and Lenovo cover endpoint and workstation options, while Qualcomm, Intel and AMD supply relevant processor platforms. Confirm the model’s memory and accelerator needs before assuming a local machine can serve it.
- Distributed sites and edge fleets: Consider Zededa, Scale Computing, StorMagic and Lenovo ThinkEdge for management and infrastructure, with Cisco or Extreme Networks for networking and Qualcomm-based devices for low-power local processing. The central buying question is how sites are provisioned, secured and recovered at scale.
- AI data resilience: Evaluate Cohesity and Veeam for backup, recovery and data-protection requirements. Separately assess whether high-performance storage or data governance is needed; resilience software does not by itself solve inference performance.
- AI application delivery and protection: F5, Cisco and networking/security platforms may matter when serving and protecting applications across environments. Pin down whether the need is traffic management, API or model protection, network security or compute capacity.
What this list does not tell a buyer
CRN’s article does not publish a scoring rubric, comparative benchmarks, prices, customer-satisfaction results or market-share thresholds. It also does not consistently distinguish generally available products from previews, announcements or broader product positioning. Inclusion does not mean every named capability is suitable for every buyer—or that vendors in different groups are direct competitors.
Rank #3
Hewlett Packard Enterprise ProLiant MicroServer Gen11 Tower Server, Intel Xeon 6325P Processor, 32GB Memory, 4TB HDD Storage, External 180W US Power Supply (HPE Smart Choice P86771-005)
- MODEL P86771-005: Ultra-compact HPE ProLiant MicroServer Gen11 featuring Intel Xeon 6325P 3.5GHz 4-core processor, ideal for SMB workloads and edge deployments
- FLEXIBLE MEMORY & STORAGE: Includes 32GB DDR5 UDIMM memory (expandable to 128GB) and 4 LFF-NHP drive bays. Features new MR408i-p controller support for enhanced storage performance
- READY TO RUN: Includes 1 x HPE 4TB SATA 6G Business Critical HDD, 180W external power adapter, and 1/1/1 year warranty for dependable plug-and-play server operation
- WHISPER-QUIET & SPACE-SAVING: Ultra-compact mini tower design fits easily in small office spaces; supports wall, flat, or vertical placement for deployment flexibility
- REMOTE MANAGEMENT READY: Includes HPE iLO6 with Silicon Root of Trust, TPM 2.0, and dedicated iLO-M.2 port kit for secure and efficient remote server administration
It does not establish that a high GPU-utilization figure will hold for a particular workload, that a deployment will be completed in a stated number of hours, or that an “AI platform” label means the same thing across companies. Nor does it provide the power, cooling, migration, support and staffing figures needed for a reliable total-cost comparison.
Before committing, ask vendors and channel partners for the exact configuration, software versions, release status, support scope, workload-specific validation, recovery behavior and a cost model. For a fair comparison, run representative data and models through the proposed full system, including the data path and operational tools.
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Rank #4
IPCHASSIS 2U Industrial Computer Case Rackmount Chassis Short Depth 13.38" Support ATX Motherboard Use Flex ATX PSU
- Versatile Motherboard Compatibility: 2U Industrial Computer Case supports multiple M/B sizes including CEB 12*10.5", ATX 12*9.6", Micro ATX, and Mini ITX
- Flexible Storage Configuration: Storage support includes 1 x 3.5" HDD bay plus 5 x 2.5" HDD bays for mixing traditional hard drives and solid state drives
- Front Panel Connectivity: Dual USB 3.0 ports on front I/O panel with USB 2.0 adapter included for quick and convenient access
- Space-Saving Short Depth Design: Compact rackmount chassis with short depth of 340mm (13.38") not including handle, suitable for space-constrained environments
- Flex ATX Power Supply Compatible: Designed to support Flex ATX PSU for efficient power management in compact server builds
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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