Cisco NVIDIA Tesla M60 Graphic Card - 16 GB

CiscoSKU: 5555400

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Sale price$8,489.28

Description

Cisco NVIDIA Tesla M60 Graphic Card - 16 GB

The Cisco NVIDIA Tesla M60 Graphic Card - 16 GB is a premier data-center GPU accelerator designed to meet the exacting needs of modern enterprises. Built to handle analytics at scale, scientific computing workloads, and demanding virtualization tasks, this dual-GPU card delivers reliable performance in dense server environments. With 16 GB of GPU memory, enterprise-grade ECC protection, and Cisco’s emphasis on manageability and lifecycle support, the M60 is purpose-built for data centers that require consistent, predictable acceleration for multi-tenant workloads, AI inference, and complex simulations. This is the kind of accelerator that helps organizations transform raw data into actionable insights, accelerate time-to-value for projects, and sustain performance as workloads evolve.

In today’s digital ecosystem, virtualization and remote work demand GPU-accelerated capabilities that can scale without sacrificing user experience. The Tesla M60 brings NVIDIA GRID virtualization to the table, enabling multiple virtual desktops and virtualized applications to share GPU resources with minimal latency and high frame fidelity. That means graphics-intensive applications—such as design tools, 3D modeling, data visualization, and large-scale data analytics—can run smoothly for dozens or even hundreds of concurrent users on a single server. Cisco’s integration adds confidence in deployment, compatibility with Cisco management ecosystems, and long-term support that helps organizations maintain stable, secure, and upgradable infrastructure over time. The combination of dual GPUs and 16 GB of memory delivers ample headroom for modern workloads, while the architecture is designed to maximize throughput and minimize bottlenecks in data-center racks.

Businesses seeking to modernize their data centers will find the M60 particularly compelling for scenarios that require a blend of compute, memory, and virtualization. From accelerating scientific simulations and deep learning inference pipelines to powering GPU-accelerated databases and analytics engines, this card is engineered to boost performance without sacrificing reliability. The Cisco branding underscores a commitment to enterprise-grade firmware, robust driver support, and seamless interoperability with Cisco servers, networking gear, and digital infrastructure management tools. The 16 GB memory pool supports large datasets and multi-tenant workloads with reduced need for data shuttling between CPU and GPU, helping to lower latency and increase throughput. And because the card is designed with enterprise needs in mind, administrators can expect features that support secure deployments, easier maintenance, and consistent behavior across environments as workloads intensify.

Whether you are building a private cloud, expanding a virtual desktop infrastructure, or running data-intensive HPC jobs, the Cisco NVIDIA Tesla M60 Graphic Card - 16 GB is designed to deliver consistent, scalable results. It brings together NVIDIA’s GPU acceleration capabilities and Cisco’s reliability, control, and support philosophy to help organizations meet performance targets while simplifying management in multi-user, multi-tenant ecosystems. The combination of dual GPUs and 16 GB of memory makes it possible to allocate GPU resources more efficiently, improve user experiences, and unlock new levels of throughput for analytics, AI workloads, and visualization tasks. With the M60 in your data center, you gain a flexible, future-ready platform that can adapt to changing workloads and evolving business requirements without sacrificing stability or security.

  • Dual-GPU architecture for high-throughput virtualization and analytics: The Tesla M60 combines two GPUs on a single PCIe card to deliver parallel compute resources that accelerate virtual desktop infrastructures, high-performance analytics, and multi-tenant workloads. This configuration enables scalable throughput for graphics-rich applications, large-scale simulations, and batch data processing, all within a compact, data-center-ready form factor. The dual-GPU setup helps distribute workloads efficiently, reducing contention and enabling smoother experiences for users and applications alike.
  • 16 GB of GPU memory with ECC protection: A generous 16 GB memory pool provides headroom for demanding models, expansive datasets, and multiple concurrent virtual desktops. ECC memory helps detect and correct bit errors, improving reliability and data integrity in long-running workloads and during peak operation windows. This memory capacity supports larger models, richer visualizations, and more complex analytics workloads without sacrificing performance or stability.
  • Optimized for NVIDIA GRID virtualization: The M60 is tuned for GPU-accelerated virtualization, delivering smooth, responsive experiences for VDI, remote work solutions, and cloud-based workflows. It supports multiple user sessions with predictable latency, high frame rates, and improved multimedia performance, enabling businesses to deploy pens-and-paper-less design review, data exploration, and collaborative analytics across distributed teams.
  • Enterprise-grade reliability and Cisco integration: Built to operate in dense data centers, this card includes Cisco-tested firmware, extended support lifecycles, and compatibility with Cisco management stacks. Expect easier deployment, centralized monitoring, and consistent firmware updates across your Cisco-powered infrastructure, which reduces operational risk and simplifies governance in large-scale deployments.
  • Efficient cooling and power design for dense racks: The M60 balances performance and thermals to fit into standard data-center rack densities. Its cooling and power envelope are engineered to minimize impact on overall data-center efficiency while delivering sustained GPU performance under heavy workloads. This efficiency is especially valuable in environments with strict power budgets or high-density server configurations.

Technical Details of Cisco NVIDIA Tesla M60 Graphic Card - 16 GB

  • GPU Architecture: Dual NVIDIA Tesla GPUs on a single PCIe card, engineered for HPC, analytics, visualization, and virtualization workloads. This architecture enables effective parallel processing and efficient workload distribution across two GPUs.
  • Total Video Memory: 16 GB of GPU memory (GDDR5) with ECC protection, providing ample headroom for large datasets and multi-tenant workloads while enabling greater fault tolerance for mission-critical tasks.
  • Memory Configuration: 16 GB ECC memory (typically divided across the two GPUs), designed to maintain data integrity in long-running computations and multi-user environments.
  • Interface: PCI Express (PCIe) interface for compatibility with standard server motherboards and Cisco data-center platforms, enabling straightforward integration into existing infrastructure.
  • Form Factor: PCIe add-in card suitable for installation in standard 1U–2U server chassis and Cisco-supported servers, designed for dense data-center deployments.
  • Virtualization Support: NVIDIA GRID vGPU capable, enabling GPU-accelerated virtualization across multiple users and sessions, ideal for VDI, CAD/design workloads, and collaborative analytics.
  • Power and Cooling: Enterprise-grade design optimized for data-center power budgets and thermal envelopes, with considerations for reliability, quiet operation in dense racks, and long-term maintainability.

How to install Cisco NVIDIA Tesla M60 Graphic Card - 16 GB

  • Power down the server, unplug the power cord, and discharge any residual static electricity by touching a grounded metal surface.
  • Open the server chassis and locate an available PCIe x16 slot that meets the card’s form-factor requirements. Remove the slot cover if needed.
  • Align the Cisco NVIDIA Tesla M60 with the PCIe slot and firmly seat the card into the slot, ensuring the external connectors align with the rear I/O opening.
  • Secure the card with the appropriate screw to fix it to the chassis and connect any required auxiliary power connectors as specified by the server hardware and the card’s installation guide.
  • Install the recommended drivers and management utilities. Start the server, boot into the operating system, and install NVIDIA drivers compatible with the Tesla M60 and Cisco’s GPU management tools if applicable. Verify visibility with system utilities (for example, nvidia-smi) and ensure the GPUs are online and ready for use.

Frequently asked questions

  • Q: What workloads is the Cisco NVIDIA Tesla M60 Graphic Card - 16 GB best suited for?
    A: It is designed to accelerate data-center workloads, including analytics, HPC, AI inference, and GPU-accelerated virtualization (NVIDIA GRID) for VDI and multi-user environments. It shines in scenarios that demand high parallel compute, large memory footprints, and responsive virtualized graphics.
  • Q: How much GPU memory does it include?
    A: The card provides 16 GB of GPU memory with ECC protection, designed to support large datasets, multi-tenant workloads, and persistent workloads in data centers.
  • Q: Is it compatible with Cisco UCS and Cisco management tools?
    A: Yes. This card is built to integrate with Cisco data-center ecosystems, with firmware, drivers, and management tooling that align with Cisco hardware, ensuring smoother deployment and centralized lifecycle management.
  • Q: Do I need special drivers or software?
    A: You should install NVIDIA drivers compatible with the Tesla M60, along with any Cisco GPU management software recommended for your environment. After installation, restart the system and verify GPU status using standard management utilities.
  • Q: How should I install and configure the card for best performance?
    A: Follow the installation steps outlined above in the “How to install” section, ensure firmware and driver compatibility with your server, consider enabling vGPU profiles if using NVIDIA GRID, and monitor performance via your Cisco management stack and NVIDIA tools to optimize resource allocation.

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