AI Innovation Unveiled: 14 Vendor Partners Helping Shape The Future Of Enterprise AI At Nvidia GTC 2026

While Nvidia took center stage at Nvidia GTC 2026, its technology partners also grabbed the spotlight to showcase innovations in enterprise AI, emphasizing scalability, performance, and ecosystem integration in ways that leverage Nvidia’s latest technologies to address growing AI workload demands and redefine industry standards.

Artificial intelligence continues to drive innovation across every sector, and nowhere is this more evident than at Nvidia GTC 2026.

While the news from Nvidia, including bringing Groq LPUs, Vera CPUs, and Bluefield-4 DPUs into new data center racks or updating DGX Rubin NVL8 systems with Intel Xeon 6 CPUs, was front and center throughout the conference, just as important was what happened in Nvidia’s partner ecosystem.

Nvidia’s technology partners were out in force, unveiling their latest wares for working with Nvidia. From next-generation mobile workstations to advanced server architectures and cloud-based AI platforms to storage and data management, these products collectively illustrate how the world’s top technology vendors are redefining what’s possible in AI infrastructure, application, and deployment.

Several key trends emerge that not only highlight the priorities of these partner companies but also signal the direction in which the broader industry is headed. First of all is the emphasis on scalability and performance. Many of these products—from Lenovo’s ThinkPad P14s Gen 7 to Supermicro’s Vera Rubin platform systems—are built to deliver greater compute power, more efficient memory utilization, and improved throughput. This is in direct response to the growing complexity of AI workloads, which require unprecedented amounts of data processing and storage capacity.

Vendor partners are also leveraging cutting-edge processors, such as Nvidia’s Blackwell and Vera Rubin GPUs, and innovative memory technologies like CXL-based architectures, to ensure their solutions can handle these demands with speed and reliability.

These vendors are working other angles in the Nvidia ecosystems, showing flexibility in deployment and operations, increasing collaboration and ecosystem integration, and above all making AI more accessible and manageable for enterprises.

Enterprise AI is evolving fast. CRN looks at 14 companies that are looking to work with Nvidia to make it evolve faster.

Lenovo ThinkPad P14s Gen 7, ThinkSystem and ThinkEdge Servers

Lenovo used GTC to introduce what it called its lightest AI-ready mobile workstation, the ThinkPad P14s Gen 7. Built for professionals needing a protected and truly portable device, the 14-inch ThinkPad P14s Gen 7 pairs Intel Core Ultra Series 3 processors with Intel vPro and Nvidia RTX PRO Blackwell Generation Laptop GPU or AMD Ryzen AI PRO 400 Series processors with AMD Radeon GPUs. The company also introduced new ThinkSystem and ThinkEdge AI inferencing servers developed in partnership with Nvidia. These two new hybrid AI platforms include one powered by Nvidia RTX Blackwell GPUs for scale-out enterprise AI and multi-model inferencing, and one powered by Nvidia’s Blackwell Ultra system for AI model training, fine-tuning, and large-scale AI inference use cases.

Penguin Solutions MemoryAI KV Cache Server

Penguin Solutions called its new MemoryAI KV cache server the industry’s first production-ready KV cache server utilizing CXL memory to address the AI inferencing ‘memory wall’ challenge. Inference workloads are typically 30-percent compute-bound and 70-percent memory-bound, which Penguin said means insufficient memory for large context and high concurrency resulting in performance bottlenecks and GPU idle time. Penguin’s MemoryAI KV cache server integrates 3 TBs of DDR5 main memory and up to eight 1-TB CXL add-in cards for a total of up to 11 TBs of CXL-based memory for reduced latency, higher throughput, increased efficiency of GPU clusters, consistent achievement of stringent SLAs, and faster time-to-first-token.

HPE Private Cloud AI

Hewlett Packard Enterprise is expanding HPE Private Cloud AI, its turnkey enterprise AI factory co-engineered with Nvidia, to deliver what the company said is improved performance, scalability, and flexibility for enterprise inferencing. New network expansion racks enable HPE Private Cloud AI deployments to scale up to 128 GPUs for customers looking to run larger, more demanding AI workloads with the same consistent operational experience. To meet increasing demand for secure, fully isolated, or sovereign deployments, the large HPE Private Cloud AI system is now available in an air-gapped configuration to help ensure sensitive data is not exposed to external networks.

Supermicro DCBBS System Portfolio Powered by the Nvidia Vera Rubin Platform

Supermicro’s new portfolio of AI infrastructure systems, built on Nvidia’s Vera Rubin platform, is designed to accelerate the deployment of large-scale AI factories. The systems use Supermicro’s modular Data Center Building Block Solutions (DCBBS) architecture with advanced liquid cooling to simplify deployment and improve efficiency. Offerings include rack-scale Vera Rubin NVL72 and HGX Rubin NVL8 GPU platforms, flexible Vera CPU servers, and a new context memory storage platform powered by BlueField-4. Together, they improve performance and efficiency for AI training and inference, targeting up to 10X better throughput per watt and lower token costs compared with previous-generation systems, the company said.

Microsoft Foundry

Microsoft used Nvidia GTC 2026 to unveil new offerings for Microsoft Foundry, Azure AI Infrastructure, and Physical AI. The company’s next-generation Foundry Agent Service equips developers with new APIs to more efficiently build, deploy, and operate production-ready AI agents. Nvidia Nemotron models are also available through Microsoft Foundry, receiving the same governance and management features available across Foundry’s 11,000+ model catalog. The company also showed Azure AI infrastructure optimized for inference-heavy, reasoning-based workloads, including what it called the first hyperscale cloud to power next-generation Nvidia Vera Rubin NVL72 systems. It also unveiled a partnership with Nvidia for Physical AI integration of Microsoft Fabric with Nvidia Omniverse.

Google Cloud Fractional G4 VMs

Google Cloud used Nvidia GTC 2024 to show a multi-faceted expansion of its strategic relationship with Nvidia. One key area of expansion is the new fractional G4 VMs (virtual machines) which allows customers to leverage smaller increments of its existing G4 VMs. The fractional G4 VMs use the same hardware (pictured) as the normal G4 VMs, including Nvidia RTX Pro 6000 hardware, but are sized to provide a smaller entry point for AI graphics workloads. Also new at GTC is an integration of GKE Inference Gateway and Nvidia Dynamo to provide an open-source control plane across the application layer and the hardware, support for Vera Rubin-based systems in the second half of 2026, expanded support for Vertex AI Training Clusters on Nvidia, and the launch of a Google Public Sector and Nvidia co-branded AI startup accelerator program.

Vertiv OneCore Rubin DSX

The Vertiv OneCore Rubin DSX from Vertiv is a scalable, simulation-ready AI factory infrastructure offering developed in collaboration with Nvidia. Designed to support Nvidia’s Vera Rubin DSX blueprint and Omniverse DSX simulations, it integrates power, cooling, controls, and lifecycle services into repeatable, validated building blocks. These standardized 12.5-MW infrastructure blocks help simplify scaling, reduce design complexity, and accelerate time to operational readiness. By combining Vertiv’s converged physical infrastructure with Nvidia’s digital twin modeling, OneCore Rubin DSX lets customers virtually validate designs, optimize performance, improve coordination, and reduce integration risk, which Vertiv said supports AI deployments from small clusters to gigawatt-scale factories with efficiency and reliability.

MSI XpertStation WS300 On NVIDIA DGX Station Architecture

MSI used Nvidia GTC 2026 to launch its XpertStation WS300 on NVIDIA DGX Station Architecture, a next-generation deskside AI supercomputer built to support the accelerating demands of large language models (LLMs), generative AI, and advanced data science workflows. Powered by NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip and supporting up to 748 GBs of large coherent memory and dual 400GbE networking, the platform extends advanced AI infrastructure capabilities into a compact deskside deployment model that is now available to order.

Delta Electronics (Americas) 800 VDC In-Row 660kW Power Racks

The new 800 VDC In-Row 660 kW Power Racks from Fremont, Calif.-based Delta Electronics (Americas) are designed as complete, validated systems, combining six 110 kW power shelves with embedded 80 kW battery backup units for each shelf, delivering 480 kW of total backup capacity. The racks are supported by newly developed 18.5 kW AC/DC power supply units that achieve up to 98-percent efficiency, while aluminum capacitor-based energy storage helps stabilize GPU workloads against high-frequency dynamic distortion. Delta said the new racks help minimize schedule risks and on-site coordination caused by the integration of switchgear, UPS, battery systems, and CDUs which arrive from multiple vendors.

Anaconda AI Catalyst with Nvidia Nemotron Models

Anaconda’s enterprise AI development suite AI Catalyst now includes Nvidia Nemotron 2 and Nvidia Nemotron 3 models with quantizations. Nemotron is Nvidia’s family of open-source foundation models designed to build efficient, accurate, and specialized agentic AI systems. Nemotron models in AI Catalyst come with enterprise-grade governance features such as AI bills of materials, vulnerability scanning and compliance documentation, CUDA compatibility validation, and reproducibility controls, extending Anaconda’s governance framework beyond Python packages to foundation models. Anaconda said this gives security, compliance, and infrastructure teams a unified system for managing both.

SUSE integration with Nvidia Jetson

SUSE unveiled integration with Nvidia Jetson, providing customers the security and stability of an enterprise-hardened Linux operating system with the world’s most powerful AI hardware and the confidence to move from the lab to production. Built on the foundation of Nvidia JetPack, the seamless experience supports mission-critical AI workloads on Nvidia Jetson Orin and, in the future, Jetson Thor platforms with combined global support and security certifications of both SUSE and Nvidia. Together, SUSE brings lifecycle governance, enterprise compliance, and Linux Foundation sovereign control to the Nvidia Edge AI ecosystem, operationalizing Edge AI for regulated enterprise environments.

Flex 800 VDC Power Rack For Nvidia AI Infrastructure

Flex used Nvidia GTC 2026 to introduce a new reference design for the Nvidia Omniverse DSX Blueprint which the Austin, Texas-based company said will help accelerate giga-scale AI factory deployment while supporting migration from traditional AC environments to 800 VDC power architectures and reducing onsite complexities. The 800 VDC Power Rack for Nvidia AI infrastructure, which features a disaggregated architecture and Flex’s power shelf for the Nvidia Rubin Ultra platform helps maximize space for compute and enable higher HPU density for greater performance, the company said. It also includes advanced liquid cooling features, including secondary fluid networks and cooling distribution units that enable efficient heat removal, along with integrated, high-density IT racks and critical power infrastructure across high-capacity power feeds.

Dell Pro Max With GB300 Support For Nvidia NemoClaw

Packing datacenter performance into a deskside supercomputer, Dell Pro Max with GB300 desktop from Dell Technologies supports Nvidia NemoClaw, providing compute power, memory capacity, and always-on reliability that agentic AI workflows and autonomous agent development require. Dell claims to be the first OEM to ship a desktop with Nvidia GB300, delivering 20 petaflops of FP4 performance and 748 GBs of coherent memory. Dell Pro Max with GB300 allows enterprises to build and run autonomous, self-evolving agents locally. With Nvidia NemoClaw, they gain the security guardrails and policy enforcement needed to safely deploy autonomous agents in production environments.

Salesforce, Nvidia Partner On Agentic AI Via Nemotron

Salesforce, which posits that most AI agents are still stuck in isolation and disconnected from the governed data and workflows that large enterprises actually run on, used Nvidia GTC 2026 to show how it and Nvidia are bringing high-performance, cost-efficient AI agents directly into the flow of work via Slack, Agentforce, and Nvidia Nemotron models. Nvidia Nemotron-3 Nano is now available in Agentforce, where the model’s 1-million token context window and architecture lets agents reason across long customer histories and multi-step workflows at a fraction of the traditional compute cost. Also new is the Slack ‘command center’, a Slackbot that receives user requests to trigger Agentforce workflows, reasons over Data 360 context, invokes Nemotron-powered processing, and orchestrates agent actions across enterprise systems.