Cisco UCS AI & GPU Servers

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IT teams that need to run AI and accelerated applications without fragmenting management can use Cisco UCS AI & GPU Servers as a controlled platform for flexible, dense GPU compute. They support a range of workloads, from inference and fine-tuning to visualisation, model development and RAG, while keeping capacity aligned to the job rather than forcing every project onto the largest system.

In shared data centres, controlled research environments and regulated infrastructure, these servers help teams keep performance predictable, control power and space use, and refresh systems without creating server sprawl. The flexible rack option suits variable workloads, while the denser system supports larger models and higher accelerator demand where scale matters.

Cisco UCS AI & GPU Server Quick Specs & Key Features

  • Configurable GPU capacity: The C845A M8 supports two to eight PCIe GPUs, letting teams match accelerator count to inference, fine-tuning or visual computing demand so they avoid overcommitting to a large HGX-style node and reduce refresh risk.
  • Dense accelerator layout: The C885A M8 packs eight GPUs into a high-density rack server, giving data-heavy AI training, RAG and high-volume inference the accelerator scale needed for larger models and improved workload fit.
  • PCIe platform flexibility: The PCIe-based design supports a wider range of GPU and system configurations, which helps platform teams standardise on one server pattern for multiple AI use cases and simplify deployment.
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  • Enterprise AI workload support: Both models are designed for AI inference, fine-tuning, model development and accelerated application work, so organisations can host mixed accelerated workloads on a single platform and lower operational overhead.
  • Repeatable platform management: Cisco UCS rack integration gives infrastructure teams a consistent way to host, scale and refresh GPU servers across data centres or edge sites, improving lifecycle control and availability.
  • Workload-aligned model choice: The C845A M8 suits configurability-led projects while the C885A M8 suits maximum-density use cases, so buyers can choose the server that best matches power, space and performance priorities without mismatched capacity.
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Find your ideal Cisco UCS AI & GPU Server

Full technical specifications are available on each product page.

Model Popularity Deployment Primary Use Case Form Factor CPU Vendor Processor Platform Maximum CPUs Supported Maximum Memory Capacity Memory Slots Maximum GPUs Supported
Cisco UCS C845A M8 AI & GPU Server Cisco UCS C845A M8 AI & GPU Server ★ ★ ★ HPC / AI AI / GPU Compute 4U AMD EPYC 5th Gen 2 4 TB 32 8 View
Cisco UCS C885A M8 AI & GPU Server Cisco UCS C885A M8 AI & GPU Server ★ ★ ★ HPC / AI AI / GPU Compute 8U AMD EPYC 5th Gen 2 4 TB 24 8 View

Cisco UCS AI & GPU Server Deployment Scenarios and Industries

Data Centres

Data centre teams need GPU capacity for AI inference, fine-tuning, visualisation and accelerated application work without overcommitting to the largest AI platforms. Cisco UCS AI & GPU Servers provide flexible and dense compute options that help teams balance workload scale, space, power and lifecycle management.

Manufacturing

Manufacturers use GPU servers for plant analytics, digital twins, ERP support and edge applications that rely on operational data. Cisco UCS AI & GPU Servers give production teams a managed platform for accelerated workloads while avoiding fragmented infrastructure across sites.

Healthcare

Healthcare organisations need reliable compute for imaging, clinical platforms, records and analytics, with clear security boundaries and manageable operations. Cisco UCS AI & GPU Servers support these workloads with scalable accelerated infrastructure that can be planned around constrained IT resources.

Finance

Finance teams need predictable GPU capacity for risk analysis, trading support, payment systems and audit-sensitive applications. Cisco UCS AI & GPU Servers provide controlled accelerated compute for regulated environments where performance, oversight and operational consistency matter.

Software Development

Software development teams need scalable GPU resources for build farms, CI/CD, test platforms, private-cloud labs and AI-assisted development. Cisco UCS AI & GPU Servers make it easier to reprovision accelerated capacity as projects change, without constant hardware reshuffling.

Cisco UCS AI & GPU Server Management and Licensing Options

Manage Distributed UCS Infrastructure with Cisco Intersight

Cisco Intersight provides cloud-based monitoring, configuration and automation for Cisco UCS infrastructure across data centre and edge environments. It centralises system health, inventory, policies and firmware lifecycle management, helping teams operate distributed compute estates more consistently.

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Standardise UCS Domains with Cisco UCS Manager

Cisco UCS Manager provides embedded management for UCS compute, fabric and connectivity resources. Its policy-based approach supports consistent provisioning, inventory and configuration across rack and blade infrastructure, reducing manual administration and configuration differences within the UCS domain.

Explore Cisco UCS Manager

Size, Connect and Modernise Your UCS Environment

As a certified UK Cisco partner, Steel City Consulting assesses, designs, deploys and supports Cisco UCS environments. We help with workload sizing, platform selection, fabric and storage connectivity, migration, firmware planning, performance optimisation, maintenance and phased infrastructure refreshes.

Book a Cisco UCS Infrastructure Review

Designing & Supporting Cisco UCS AI & GPU Server Solutions

Backed by decades of expertise in the IT sector, our specialists support every stage of your deployment — from initial selection through to long-term lifecycle management.

  • Architecture & Platform Selection: We assess your workloads, performance requirements, capacity, deployment location, and growth plans before recommending suitable Cisco UCS infrastructure. This can include rack servers, blade servers, AI and GPU servers, edge servers, hyperconverged infrastructure, and composable infrastructure.
  • Fabric & Connectivity Design: We design UCS fabric and connectivity around your compute environment, network architecture, and storage requirements. Connectivity, resilience, and available capacity are planned together, helping the infrastructure support current workloads while providing a clear route for future expansion.
  • Configuration, Deployment & Migration: Our engineers size and configure Cisco UCS infrastructure around the workloads it will support before managing installation, integration, and migration. This helps avoid under-specced systems, unnecessary expenditure on excess capacity, and deployments that overlook essential network or storage dependencies.
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  • Management & Automation: We help teams use Cisco Intersight and Cisco UCS Manager to standardise configurations, monitor system health, and simplify ongoing administration. Existing environments can also be assessed for policy inconsistencies, firmware gaps, and opportunities to reduce repetitive management tasks.
  • Optimisation, Support & Lifecycle Planning: Steel City Consulting provides ongoing technical support, firmware and management guidance, capacity reviews, and lifecycle planning across Cisco UCS environments. As workload requirements evolve or infrastructure approaches end of support, we help plan upgrades and refreshes in manageable stages.
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Cisco UCS AI & GPU Server FAQ

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