AMD AI GPUs

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IT teams planning private AI clusters need capacity that stays predictable as model size, memory demand and governance needs grow. AMD AI GPUs give data centres and controlled environments accelerator options for training, inference and fine-tuning, from rack-scale systems to add-in cards for existing servers.

They suit dense AI platforms, scientific computing and mixed AI/HPC estates where keeping data close to compute matters. The range supports faster model iteration, fewer infrastructure bottlenecks and better use of existing racks, while helping teams keep sensitive workloads under local control.

AMD AI GPU Quick Specs & Key Features

  • Rack-scale AI architecture: AMD Instinct MI455X systems are built for rack-scale AI factories, keeping accelerator capability aligned across the rack so frontier training, inference and fine-tuning can scale predictably with lower interconnect overhead.
  • High-memory accelerator fabric: AMD Instinct MI325X, MI300X, MI350X and MI355X platforms pair high-bandwidth memory with tight accelerator connectivity, so large models and datasets stay closer to compute and enterprises avoid repeated data movement that slows delivery.
  • Mixed-precision compute: AMD Instinct MI430X and MI300A combine precision-heavy HPC with AI acceleration, so scientific modelling, simulation and machine-learning workloads can share one infrastructure and reduce platform sprawl.
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  • Liquid-cooling readiness: AMD Instinct MI355X is designed for direct-liquid-cooled systems, giving dense AI clusters a higher sustained performance envelope and helping data-centre operators manage heat without sacrificing throughput.
  • Air-cooled deployment: AMD Instinct MI350X and MI325X suit standard data-centre platforms, allowing organisations to add large-model AI capacity without moving to specialised cooling or changing established server operations.
  • PCIe server integration: AMD Instinct MI350P brings modern AI acceleration into conventional enterprise servers, making incremental deployment simpler for regulated, departmental and on-premises workloads while keeping operational overhead lower.

Find your ideal AMD AI GPU

Full technical specifications are available on each product page.

Model Popularity GPU Architecture FP32 Performance (TFLOPS) BF16 / FP16 Performance (TFLOPS) FP8 / INT8 Performance GPU Memory (GB) GPU Memory Type Memory Bandwidth (TB/s) Form Factor
AMD Instinct MI300A APU AMD Instinct MI300A APU — — — — — — — — — View
AMD Instinct MI300X AI Accelerator AMD Instinct MI300X AI Accelerator — — — — — — — — — View
AMD Instinct MI325X AI Accelerator AMD Instinct MI325X AI Accelerator — — — — — — — — — View
AMD Instinct MI350P AI Accelerator AMD Instinct MI350P AI Accelerator — — — — — — — — — View
AMD Instinct MI350X AI Accelerator AMD Instinct MI350X AI Accelerator — — — — — — — — — View
AMD Instinct MI355X AI Accelerator AMD Instinct MI355X AI Accelerator — — — — — — — — — View
AMD Instinct MI430X AI Accelerator AMD Instinct MI430X AI Accelerator — — — — — — — — — View
AMD Instinct MI455X AI Accelerator AMD Instinct MI455X AI Accelerator — — — — — — — — — View
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AMD AI GPU Deployment Scenarios and Industries

Data Centres

Data centre teams need to run dense AI clusters with high power draw, cooling demands and heavy data movement between accelerators. AMD AI GPUs support large-scale training and inference in rack-scale environments, helping operators fit more useful work into each rack.

Media & Entertainment

Studios need to train and run generative video, VFX and multimodal models while keeping production schedules on track. AMD AI GPUs provide the memory and throughput needed for large creative workloads, helping reduce render queues and processing delays.

Professional Services

Consultancies and engineering firms often need local AI for client data without building specialist infrastructure. AMD AI GPUs can be added to familiar enterprise servers, giving teams a practical way to run private AI, analysis and model development in-house.

Healthcare

Healthcare organisations need to process imaging, genomics and clinical AI workloads on controlled infrastructure where privacy and reliability matter. AMD AI GPUs give teams the memory and compute headroom to handle data-heavy models while keeping sensitive information on site.

Finance

Finance teams use AI for fraud detection, risk analysis and customer services, but need predictable performance and tight control over sensitive data. AMD AI GPUs help private models run faster and support more demanding workloads within governed infrastructure.

AMD AI GPU Management and Licensing Options

Why Work With Steel City Consulting

We’re trusted by IT teams in enterprise environments, data centres and complex multi-vendor estates. Our consultants help you select, integrate and support AMD AI GPUs, with practical guidance across workload fit, platform compatibility, infrastructure readiness, deployment and lifecycle planning.

  • Official multi-vendor partner Pricing, licensing and upgrade routes across leading infrastructure technology vendors.
  • Decades of IT expertise Hands-on consultancy across networking, compute, storage and security.
  • UK-wide support network Certified engineers and technicians for on-site projects, SLAs and break/fix cover.

AMD AI GPUs

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Tell us about your AI, HPC or inference workloads, current compute environment and growth plans. We’ll help you assess GPU fit, review platform and infrastructure requirements, and identify the most suitable deployment route.

AMD AI GPU Procurement & Vendor Support

We help you compare AMD AI GPUs against your workloads, infrastructure and deployment requirements, balancing performance needs, platform fit, scalability, support and long-term growth.

Right-sized GPU selection

We help you match AMD accelerator options to your AI, HPC and inference workloads, so the selected platform fits current performance requirements and future growth plans.

Vendor support & service planning

We help you define suitable warranties, support coverage and professional services around your compute environment, operating model and long-term support requirements.

Compatibility & infrastructure planning

We help you assess server platforms, networking, storage, power and cooling requirements so the wider GPU environment is ready for the workloads it needs to support.

Deployment & lifecycle planning

We help you plan implementation, capacity expansion, ongoing support and future accelerator upgrades as workload and infrastructure requirements evolve.

Need help selecting an AMD AI GPU?

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Designing & Supporting AMD AI GPU 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.

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AMD AI GPU FAQ

How should organisations choose between AMD Instinct MI455X, MI430X, MI350 and MI300 Series GPUs?

Organisations should choose between AMD Instinct GPU generations by matching each platform to workload type, deployment scale, infrastructure design and expected performance requirements.

MI455X suits the largest frontier AI deployments, while MI430X is more focused on scientific HPC and sovereign computing. MI350X and MI355X target newer high-performance AI and HPC platforms, while MI325X and MI300X remain relevant for established generative AI and accelerated-computing environments.

When is AMD Instinct MI350P more practical than MI350X or MI355X?

AMD Instinct MI350P is more practical when organisations want newer-generation AI acceleration within mainstream PCIe servers rather than deploying a more specialised accelerator platform.

MI350P offers greater flexibility for existing enterprise server environments and gradual AI adoption. MI350X and MI355X are better suited where higher-density AI or HPC deployments justify purpose-built infrastructure and a more specialised deployment model.

How do AMD Instinct GPUs compare with NVIDIA GPUs for AI and HPC workloads?

AMD Instinct and NVIDIA GPUs can both support demanding AI and HPC workloads, but the best fit depends heavily on software ecosystem, compatibility and existing infrastructure.

AMD Instinct uses the ROCm software platform, while NVIDIA centres on CUDA. AMD may suit organisations seeking an open software stack or an alternative accelerator ecosystem, while NVIDIA may be more practical where applications, development workflows and internal skills already depend heavily on CUDA.

Need a different solution?

If these options aren’t the right fit for your environment, we provide a wide portfolio of product series and solutions that may better suit your infrastructure. Explore below, or speak to our team and we’ll help you find the right match.

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