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AMD Helios is a rack-scale AI system, not a GPU you can buy as a standalone card. The closest comparison is NVIDIA’s integrated NVL72 rack; Intel Gaudi 3 is an alternative accelerator and cluster path, while cloud instances let teams rent access to accelerator infrastructure instead of buying and operating a rack. Those are different kinds of choices, and the available vendor announcements do not establish a neutral performance winner or comparable current prices.
What is the AMD Helios AI rack?
AMD describes Helios as a rack-scale platform for large AI training and inference. Its announced design combines 72 Instinct MI455X GPUs, 6th Gen EPYC server CPUs, Pensando networking, and ROCm software. AMD says the rack can scale into larger clusters and uses UALink over Ethernet for scale-up communication, with standards-based Ethernet for scale-out networking.
AMD’s Helios product page specifies up to 432 GB of HBM4 memory and 19.6 TB/s of memory bandwidth per MI455X GPU. These are AMD’s specifications, not results from an independent comparison. The company also describes an open rack design aligned with Meta’s Open Rack Wide standard, with serviceability features intended for rack operations.
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- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
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What are the alternatives to AMD Helios?
The right alternative depends on whether the requirement is an integrated rack, an accelerator platform to build into a cluster, or temporary access to hosted infrastructure. NVIDIA GB200 NVL72 is the most directly comparable rack-scale option in the cited material. Gaudi 3 is an accelerator and cluster alternative, not a documented 72-GPU rack equivalent. Cloud offerings change the purchasing model rather than provide a like-for-like hardware product.
| Option | What the buyer gets | Documented configuration or detail |
|---|---|---|
| AMD Helios | Rack-scale platform | 72 MI455X GPUs, 6th Gen EPYC CPUs, Pensando networking, and ROCm |
| NVIDIA GB200 NVL72 | Integrated rack-scale system | 72 Blackwell GPUs, 36 Grace CPUs, and a 72-GPU NVLink domain |
| Intel Gaudi 3 | Accelerator card and cluster path | Intel documents a PCIe accelerator card and a 32-node cluster reference design |
| CoreWeave GB200 NVL72 | Cloud access to an NVIDIA rack system | Instances announced February 4, 2025; NVIDIA said the infrastructure could scale to 110,000 GPUs |
| IBM Cloud Gaudi 3 | Cloud access to Gaudi 3 accelerators | Intel named Frankfurt, Washington, D.C., and Dallas when availability was announced May 1, 2025 |
The table compares what each route represents, not matched capacity, performance, availability, or cost. A rack, a card, a cluster reference design, and a cloud instance are not interchangeable purchasing units.
How does AMD Helios compare with NVIDIA NVL72?
Rack architecture and communication
NVIDIA describes GB200 NVL72 as a liquid-cooled rack with 72 Blackwell GPUs and 36 Grace CPUs connected as a 72-GPU NVLink domain. NVIDIA lists 130 TB/s of GPU communication bandwidth for GB200 NVL72. That figure describes NVIDIA’s stated NVLink bandwidth; it should not be compared directly with a different vendor’s memory-bandwidth figure.
AMD’s Helios announcement describes 72 MI455X GPUs per rack and a fabric design using UALink over Ethernet for scale-up, plus standards-based Ethernet for scale-out. The architecture descriptions establish that both are rack-scale systems, but they do not supply a matched, independent test of communication performance.
Keep generations and metrics separate
GB200 NVL72 and GB300 NVL72 are distinct generations. NVIDIA describes GB300 as using 72 Blackwell Ultra GPUs and 36 Grace CPUs, positioning it for inference and reasoning. Do not treat a GB200 figure as a GB300 specification, or compare headline numbers across GB200, GB300, and Helios without matching the metric and configuration.
AMD’s launch announcement claims Helios has 15% more peak FP4 performance, 50% more HBM capacity, and 6% more HBM bandwidth than its named competing solution, NVIDIA Vera Rubin NVL72. AMD attributes the comparisons to published specifications and AMD Performance Labs calculations or modeling; some results use a Kimi K2 Thinking workload, and system manufacturers may vary configurations. These are AMD’s claims under stated assumptions, not independent benchmark results or a general verdict over NVIDIA’s GB200 and GB300 systems.
Is Intel Gaudi 3 a Helios alternative?
Yes, if “alternative” means a different accelerator architecture and a route to building or renting AI compute. Intel documents Gaudi 3 as a PCIe accelerator card and provides a 32-node cluster reference design. It emphasizes standard Ethernet, open software, framework and model support, and OEM routes that include Dell, HPE, and Supermicro.
That makes Gaudi 3 relevant to organizations evaluating accelerator cards or clusters, but the cited Intel material does not establish a direct equivalent to Helios’s integrated 72-GPU rack. A buyer comparing the two should first define the system boundary: a Gaudi 3 card, a cluster built from those cards, and a complete rack have different integration and operations requirements.
Intel’s product material includes comparative performance statements. Treat those as Intel’s claims unless they are confirmed by a suitably matched independent evaluation. Intel also cited a Signal65-commissioned benchmark study in its May 2025 IBM Cloud announcement; any use of those comparisons should identify the study and the workloads rather than imply a universal result.
Can you use AI server GPUs in the cloud instead of buying a rack?
Cloud access can avoid purchasing and operating a rack, but it does not make hosted capacity equivalent to owning a particular rack. Availability, regions, capacity, instance configuration, support terms, and pricing need to be checked with the provider for the intended workload.
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NVIDIA GB200 NVL72 through CoreWeave
NVIDIA reported on February 4, 2025, that CoreWeave had launched GB200 NVL72 cloud instances. NVIDIA said the announced infrastructure could scale to 110,000 GPUs. That is a provider-and-vendor statement about announced scale, not a guarantee of current capacity or an indication that a buyer can reserve that number of GPUs.
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Intel announced on May 1, 2025, that IBM Cloud was the first cloud service provider to make Gaudi 3 available and named Frankfurt, Washington, D.C., and Dallas. Those are the regions stated in the announcement; current inventory, pricing, and region availability have not been established here. Confirm them directly before planning a deployment.
AMD cloud context
In June 2025, AMD described MI350 rack infrastructure rolling out with Oracle Cloud Infrastructure and previewed Helios as a future platform using MI400-series GPUs, Venice CPUs, and Vulcano NICs. AMD’s later Helios launch announcement describes a different, subsequently announced design based on MI455X GPUs, 6th Gen EPYC CPUs, and Pensando networking. The preview is historical context, not the final specification to use for the launched platform.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose between the platforms
Before requesting quotes or capacity, write down the workload and the system boundary you actually need. A useful evaluation should compare the same model, precision, software stack, topology, power assumptions, and system configuration; otherwise, a headline benchmark may measure a different problem.
- Workload: Specify training, fine-tuning, high-throughput inference, or low-latency reasoning, along with model size and expected concurrency.
- Software fit: Check framework and model support, migration effort, and the team’s familiarity with ROCm, NVIDIA software, or Intel’s software stack.
- Memory and communication: Compare accelerator HBM capacity and bandwidth separately from CPU-attached memory. Examine the scale-up fabric and the scale-out network in the context of the intended topology.
- Operations: For an owned rack, confirm facility power, liquid-cooling readiness, rack integration, serviceability, and support responsibilities. For a cluster, establish who supplies and supports the complete system.
- Access and economics: Compare an owned rack or OEM system with rented cloud capacity using current quotes, region and capacity confirmation, support terms, and the cost of a matched workload.
The cited announcements do not establish comparable street pricing or an independent same-workload benchmark across Helios, NVIDIA NVL72, and Gaudi 3. A sound buying decision therefore requires current vendor or provider quotes and an evaluation using the buyer’s own workload, rather than a universal ranking based on vendor claims.
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