A wider view of compute: professional GPUs, dedicated AI processors and cloud platforms. Choose the software and deployment model as carefully as the silicon.
A GPU is a programmable parallel processor used for graphics and compute. An AI accelerator may use a more specialized design. They can solve overlapping problems, but their software, deployment and purchasing models differ.
Arc Pro B60 offers 24 GB of graphics memory. It is a professional GPU option to evaluate for workstation graphics and local AI workloads, rather than a direct substitute for a rack-scale accelerator system.
Gaudi 3 is a dedicated AI accelerator, not an Arc graphics card. Intel specifies 128 GB HBM2e memory and 3.7 TB/s memory bandwidth, with Ethernet-based scale-out connectivity.
Cerebras announced CS-4 in August 2026. Its WSE-3T uses a wafer-scale design with 900,000 AI-optimized cores and 44 GB of on-wafer SRAM. This is a dedicated system architecture, not a PCIe GPU card.
Evaluate supported models, system or service access, data movement and deployment terms. Do not compare its core count directly with GPU cores.
Cerebras CS-4 announcementGoogle’s TPU family is designed for machine-learning workloads. TPU v6e, also called Trillium, has documented slice configurations and training workflows through Google Cloud.
Check framework support, region, quota, compilation behavior and the cost of data movement. A TPU cloud deployment has different operational requirements from owning GPU servers.
Google TPU v6e architectureAmazon EC2 Trn2 instances provide access to Trainium2 AI accelerators. The instance shape determines accelerator count, host memory, networking and local storage.
Review the AWS Neuron software path and run a representative training or inference job before sizing a commitment.
AWS Trn2 instance configurationsOur platform overview covers Blackwell Ultra B300/GB300, AMD Instinct MI355X, Vera Rubin and AMD Helios. It explains how server and rack-scale configurations change the infrastructure plan.
Explore NVIDIA & AMD platforms| Platform type | Useful starting point | What to verify |
|---|---|---|
| Professional GPU | Workstation graphics, local AI and development | Application support, graphics memory, drivers and chassis |
| Data center GPU | Server-based training, inference and compute | GPU topology, framework support, memory and network |
| Dedicated AI system | Supported models on a specialized architecture | Compiler/runtime, model coverage and deployment terms |
| Cloud accelerator | Managed access without installing local hardware | Region, quota, instance economics and data location |
Use your own model, precision, sequence length and concurrency when benchmarking. Record latency, throughput, memory use and total operating cost together.
Send us your workload, platform and site requirements.
Manufacturer references reviewed 13 September 2026. Examples are informational, not a statement of stock, resale authorization or guaranteed compatibility. Confirm the exact SKU and platform documentation with your supplier.
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