Helios
AMD 'Helios' is a rack-scale AI platform built on Meta's 2025 OCP Open Rack Wide (ORW) specification, designed for gigawatt-scale data centers running frontier AI workloads.
Publisher review
AMD 'Helios' is a rack-scale AI platform built on Meta's 2025 OCP Open Rack Wide (ORW) specification, designed for gigawatt-scale data centers running frontier AI workloads. It targets hyperscalers, OEMs, ODMs, and enterprises needing an open, standards-based alternative to proprietary AI infrastructure. The platform integrates AMD Instinct MI450 Series GPUs, EPYC CPUs, and Pensando DPUs into a unified system that can be customized by partners. 'Helios' is not a product you buy off the shelf; it is a reference design that OEMs and hyperscalers adopt, extend, and deploy, reducing fragmentation in the AI hardware ecosystem.
At the core of a 'Helios' rack are 72 AMD Instinct MI450 Series GPUs, each with 432 GB of HBM4 memory and 19.6 TB/s of memory bandwidth. A fully configured rack delivers up to 1.4 exaFLOPS of FP8 performance and 2.9 exaFLOPS of FP4 performance, with 31 TB of total HBM4 memory and 1.4 PB/s of aggregate memory bandwidth. The system provides up to 260 TB/s of scale-up interconnect bandwidth (via UALink) and 43 TB/s of Ethernet-based scale-out bandwidth (via Ultra Ethernet Consortium standards). It uses quick-disconnect liquid cooling for sustained thermal performance and a double-wide layout to improve serviceability, allowing technicians to access components without removing adjacent hardware.
AMD positions 'Helios' directly against NVIDIA's Vera Rubin platform, claiming 50% more memory capacity than the competing system. The platform delivers up to 36× higher performance compared to previous AMD generations, according to AMD's internal benchmarks. While NVIDIA dominates the AI accelerator market with proprietary NVLink and InfiniBand fabrics, 'Helios' differentiates by supporting open scale-up (UALink) and scale-out (UEC) fabrics, giving hyperscalers more flexibility to mix vendors. However, AMD's software ecosystem (ROCm) still trails NVIDIA's CUDA in maturity and developer mindshare, which could slow adoption for enterprises with existing CUDA-optimized workflows.
The honest trade-offs: First, 'Helios' is a reference design, not a turnkey product — buyers must work with OEM/ODM partners to build and deploy it, adding integration complexity. Second, AMD's ROCm software stack, while improving, lacks the breadth of libraries and community support that CUDA offers, potentially increasing development time for custom models. Third, the platform's massive power and cooling requirements (gigawatt-scale) mean it is only practical for the largest data center operators, not small or mid-size enterprises. Fourth, AMD's track record on timely GPU deliveries has been inconsistent, and any supply constraints on MI450 chips could delay Helios deployments.
How it works
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Open Rack Wide form factor
Built on Meta's 2025 OCP ORW specification, enabling a double-wide layout for improved serviceability and standardized rack integration.
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AMD Instinct MI450 GPUs
Each GPU provides 432 GB of HBM4 memory and 19.6 TB/s bandwidth; 72 GPUs per rack deliver 31 TB total HBM4 and 1.4 PB/s aggregate.
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ExaFLOPS-class performance
A single rack achieves up to 1.4 exaFLOPS FP8 and 2.9 exaFLOPS FP4, enabling trillion-parameter training and large-scale inference.
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High-bandwidth interconnects
Supports 260 TB/s scale-up (UALink) and 43 TB/s scale-out (Ethernet/UEC) bandwidth for seamless multi-rack GPU communication.
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Quick-disconnect liquid cooling
Integrated liquid cooling with quick-disconnect fittings sustains thermal performance under continuous high-load AI workloads.
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EPYC CPU + Pensando DPU integration
Combines AMD EPYC processors for host compute and Pensando DPUs for advanced networking and data acceleration.
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OEM/ODM customization blueprint
Serves as an open reference design that partners can adopt, extend, and customize, accelerating time-to-market for new AI systems.
Strengths and trade-offs
Strengths
- Delivers up to 1.4 exaFLOPS FP8 and 2.9 exaFLOPS FP4 per rack, providing a 36× performance lift over previous AMD generations.
- Offers 50% more memory capacity (31 TB HBM4 per rack) than NVIDIA’s Vera Rubin system, critical for large model training.
- Supports open standards like UALink and Ultra Ethernet, reducing vendor lock-in compared to NVIDIA’s proprietary NVLink and InfiniBand.
- Integrates quick-disconnect liquid cooling and a double-wide ORW layout, improving serviceability and thermal efficiency at scale.
Trade-offs
- As a reference design, it requires OEM/ODM integration and is not available as a plug-and-play product, adding deployment complexity.
- AMD’s ROCm software ecosystem lags behind NVIDIA’s CUDA in library breadth and developer community support, potentially slowing adoption.
- The platform targets gigawatt-scale data centers only, making it impractical for small-to-medium enterprises or edge deployments.
- AMD has historically faced GPU supply constraints; any production delays on MI450 chips could push back Helios availability.
Pricing context
Not publicly disclosed; pricing is negotiated per deployment with OEM/ODM partners and depends on GPU count, cooling, and networking configuration.
Getting started with Helios
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Evaluate Helios reference design
Review the Helios OCP ORW specification and AMD's documentation to understand the rack-scale architecture, GPU count, cooling requirements, and power needs. Confirm that your data center can support gigawatt-scale deployment before proceeding.
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Partner with an OEM/ODM
Contact an authorized OEM or ODM partner to discuss customization and integration of the Helios design. Provide your performance targets, GPU count, and networking preferences so the partner can create a tailored deployment plan.
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Configure the rack hardware
Work with your partner to specify the number of AMD Instinct MI450 GPU modules, EPYC CPUs, Pensando DPUs, and the liquid cooling system. Choose the UALink and Ultra Ethernet interconnect configurations to match your workload scale.
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Set up the software stack
Install AMD ROCm on the host systems and configure the GPU drivers, libraries, and container runtime. Verify that your AI framework (e.g., PyTorch, TensorFlow) is compatible with ROCm and adjust any CUDA-specific code paths.
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Deploy and validate a workload
Run a representative training or inference job on a single rack to validate performance, memory bandwidth, and thermal stability. Monitor GPU utilization and interconnect throughput, then scale to multiple racks as needed.
Frequently Asked Questions
What is AMD Helios and who is it for?
AMD Helios is a rack-scale AI platform built on Meta's 2025 OCP Open Rack Wide specification. It targets hyperscalers, OEMs, ODMs, and enterprises needing an open, standards-based alternative to proprietary AI infrastructure for gigawatt-scale data centers running frontier AI workloads.
What are the key specs of the AMD Helios rack?
A Helios rack contains 72 AMD Instinct MI450 GPUs with 432 GB HBM4 each, delivering 31 TB total memory and 1.4 PB/s aggregate bandwidth. It achieves up to 1.4 exaFLOPS FP8 and 2.9 exaFLOPS FP4 performance, with 260 TB/s UALink scale-up and 43 TB/s Ethernet scale-out bandwidth.
How does AMD Helios compare to NVIDIA Vera Rubin?
AMD positions Helios directly against NVIDIA's Vera Rubin platform, claiming 50% more memory capacity. Helios supports open standards like UALink and Ultra Ethernet, reducing vendor lock-in compared to NVIDIA's proprietary NVLink and InfiniBand fabrics, offering hyperscalers more flexibility to mix vendors.
Is AMD Helios a product you can buy directly?
No, Helios is a reference design, not a turnkey product. Buyers must work with OEM or ODM partners to adopt, extend, and deploy it. This adds integration complexity compared to off-the-shelf systems, but allows customization for specific data center needs.
What are the main challenges of deploying AMD Helios?
Helios requires gigawatt-scale data centers, making it impractical for small or mid-size enterprises. AMD's ROCm software ecosystem trails NVIDIA's CUDA in maturity and developer support, potentially increasing development time. Additionally, AMD's historical GPU supply constraints could delay deployments.
What cooling and form factor does AMD Helios use?
Helios uses quick-disconnect liquid cooling for sustained thermal performance under continuous high-load AI workloads. It features a double-wide layout based on Meta's Open Rack Wide specification, improving serviceability by allowing technicians to access components without removing adjacent hardware.
Alternatives
How Helios compares
Direct head-to-head against 2 competitors. Picked by 7wData.
Helios
- Pricing
- Not publicly disclosed; pricing is negotiated per deployment with OEM/ODM partners and depends on GPU count, cooling, and networking configuration.
- Target
- AMD 'Helios' is a rack-scale AI platform built on Meta's 2025 OCP Open Rack Wide (ORW) specification, designed for gigawatt-scale data centers running frontier AI
- Strength
- Delivers up to 1.4 exaFLOPS FP8 and 2.9 exaFLOPS FP4 per rack, providing a 36× performance lift over previous AMD generations.
- Watch for
- As a reference design, it requires OEM/ODM integration and is not available as a plug-and-play product, adding deployment complexity.
Whoop
- Pricing
- $30/month or $239/year subscription required
- Target
- Athletes and fitness enthusiasts seeking 24/7 strain, recovery, and sleep tracking
- Deployment
- Wearable band with app
- Strength
- Industry-leading recovery and strain analytics with bicep band option
- Watch for
- Subscription fee escalates over time; no screen or on-device data
Amazfit Helio Band
- Pricing
- $99 one-time, no subscription
- Target
- Budget-conscious users wanting 24/7 health and fitness tracking without recurring fees
- Deployment
- Wearable band with app
- Strength
- Low upfront cost with no subscription; 10-day battery life
- Watch for
- Limited band size at launch; no bicep band option; small charging puck easily lost
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