--- title: "AMD 的 “helios” AI 机架！2026！" type: "Topics" locale: "zh-CN" url: "https://longbridge.com/zh-CN/topics/32789169.md" description: "The following is a detailed analysis of the components and core advantages of AMD's Helios AI rack system in mid-2025 (such as the Advancing AI 2025 conference held in June 2025 and subsequent official introductions). Combining AMD's strategic transformation direction, hardware innovation, and industry competition landscape, Helios is positioned as the industry's first end-to-end AI infrastructure solution designed as a unified rack system, aiming to reshape the deployment paradigm for large-scale AI training and distributed inference..." datetime: "2025-08-09T09:00:42.000Z" locales: - [en](https://longbridge.com/en/topics/32789169.md) - [zh-CN](https://longbridge.com/zh-CN/topics/32789169.md) - [zh-HK](https://longbridge.com/zh-HK/topics/32789169.md) author: "[dian11](https://longbridge.com/zh-CN/profiles/14506608.md)" --- > 支持的语言: [English](https://longbridge.com/en/topics/32789169.md) | [繁體中文](https://longbridge.com/zh-HK/topics/32789169.md) # AMD 的 “helios” AI 机架！2026！ The following is a detailed analysis of the components and core advantages of the Helios AI rack system by AMD in mid-2025 (such as the Advancing AI 2025 conference held in June 2025 and subsequent official introductions). Combining AMD's strategic transformation direction, hardware innovation, and industry competition landscape, Helios' core positioning is the industry's first end-to-end AI infrastructure solution designed as a unified system at the rack level, aiming to reshape the deployment paradigm of large-scale AI training and distributed inference. Here is an in-depth interpretation: 1\. Components of Helios: Full-stack integration of hardware and software Helios is not a single chip or server, but a complete computing unit with AMD's core technologies highly integrated, covering four core modules and underlying infrastructure: 1\. Next-generation high-performance AI accelerator card (Instinct MI400 series) ◦ Core role: As the computational heart of Helios, driving large-scale model training and inference tasks. ◦ Specifications: ◦ Single card supports FP4 precision computing power up to 40 PFLOPS, FP8 precision up to 20 PFLOPS (FP4 is the key precision for optimizing trillion-parameter models); ◦ Equipped with up to 432GB of HBM4 memory, memory bandwidth of 19.6 TB/s, meeting the memory needs of ultra-large-scale models (such as trillion-parameter models); ◦ External interconnect bandwidth of 300 GB/s, supporting ultra-high-speed data exchange between racks and clusters (Ultra Accelerator Link technology). ◦ Design goal: MI400 is optimized for Helios, achieving significant performance improvement over the previous generation MI350 and supporting rack-level density expansion. 2. New generation CPU platform (EPYC "Venice" processor) ◦ Positioning: As the system coordinator and management hub of Helios, handling scheduling, data preprocessing, and other CPU-intensive tasks. ◦ Technical specifications: Based on Zen 6 architecture (2nm process), supporting PCIe Gen 6 interface and ultra-high bandwidth interconnect, deeply optimized with GPU. ◦ Core role: Efficiently scheduling GPU computing resources, accelerating control flow, data loading, and mixed-precision computing in the AI training process. 3. Intelligent network card (DPU: Pensando Vulcano) ◦ Function: Undertakes Helios' network and storage virtualization, security acceleration, and I/O offloading tasks, freeing up CPU/GPU computing power to focus on computation. ◦ Advantages: Deeply integrated with Ethernet Alliance (UEC) standards and open network protocols (such as OCP), optimizing data transmission efficiency in large-scale clusters and reducing latency bottlenecks. 4. Underlying infrastructure design ◦ Cooling and power supply system: ◦ For ultra-high-density GPU deployment (such as 72 MI400 cards), Helios adopts a dual-width rack design (expanding physical space compared to traditional single racks), optimizing cooling layout and power supply architecture, supporting both air and liquid cooling solutions to adapt to different data center environments. ◦ System-level cooling and power consumption balance design ensures stability and energy efficiency under high load (solving the pain points of insufficient cooling and dispersed power supply in traditional GPU clusters). ◦ Interconnect architecture: Achieves full open protocol interconnect between CPU-GPU-DPU through Ultra Accelerator Link + Ultra Ethernet, with a total bandwidth of 260 TB/s (horizontal expansion bandwidth of 43 TB/s), building a high-speed communication network. 5. Deeply optimized software stack (ROCm ecosystem) ◦ Core software: Pre-integrated ROCm 7 open-source software platform and AI development toolchain, supporting seamless migration and acceleration of mainstream frameworks (PyTorch, TensorFlow, vLLM, etc.). ◦ Features: ◦ Native support for next-generation large models such as Llama 4, GPT-5, and distributed training (KVCache optimization, Mooncake pre-fill technology); ◦ Provides one-click cluster management (Slurm/K8s integration) and Red Hat OpenShift certification, lowering development and deployment thresholds; ◦ Weakens CUDA dependency through open ecosystem, attracting developers to choose open-source alternatives. 2\. Core advantages of Helios: Redefining AI infrastructure paradigm The revolutionary value of Helios lies not only in its powerful hardware stack but also in the multi-dimensional breakthroughs brought by its system-level innovation, directly addressing the core pain points of current AI infrastructure deployment: Unified system design: Out-of-the-box, significantly reducing deployment complexity and cost • Traditional pain points: Enterprises need to purchase and assemble CPU/GPU/network card/motherboard components themselves, facing compatibility issues, time-consuming debugging, and high operation and maintenance costs (total cost of ownership TCO remains high). • Helios solution: ◦ Deeply integrates CPU, GPU, DPU, cooling, power supply, and interconnect architecture into a standardized dual-width rack, completing software and hardware collaborative optimization before leaving the factory, allowing users to run large-scale AI workloads out of the box. ◦ Significantly shortens time-to-market (TTM), avoiding the repeated testing risks of traditional DIY solutions, especially suitable for ultra-large-scale cloud service providers (such as OpenAI, Meta) to quickly deploy AI clusters. • TCO advantage: Through large-scale procurement and integrated design, Helios claims to increase AI output per dollar by 40% compared to competitors (such as NVIDIA rack solutions), reducing operating costs by double-digit percentages. Computing power density and performance crushing lead • Single rack computing power scale: A single Helios rack accommodates 72 MI400 GPUs, with a total memory capacity of 31 TB HBM4 and a total bandwidth of 1.4 PB/s (memory), FP4 computing power peak of 2.9 EFLOPS, FP8 of 1.4 EFLOPS. • Comparison with competitors: ◦ Compared to NVIDIA's contemporaneous solutions (such as Oberon/Vera Rubin racks), Helios' memory capacity is 50% higher, and its bandwidth and horizontal expansion capabilities are significantly leading; ◦ The ultra-high memory bandwidth (19.6 TB/s) and interconnect speed (300 GB/s) of MI400 GPU support efficient collaboration across clusters, avoiding the data bottleneck of traditional GPU clusters. • Scenario coverage: Easily handles trillion-parameter model training (such as GPT-5 level inference) and complex distributed tasks, with throughput improved by multiples compared to the previous generation. Energy efficiency and cooling: Breaking the high power consumption bottleneck • Energy efficiency optimization: ◦ Through the advanced process of MI400 (such as TSMC's transition from 3nm process to future 2nm) and architectural innovation, improving performance per watt; system-level power consumption management design (dynamic voltage frequency adjustment, cooling collaboration) further reduces overall energy consumption. • Dual-width rack cooling revolution: ◦ Breakthrough adoption of dual rack width design (traditionally single width), providing ample cooling space for 72 GPUs, optimizing airflow and liquid cooling pipeline layout, solving the cooling nightmare of high-density GPU clusters. ◦ AMD emphasizes: 2025 is a key period for liquid cooling popularization, Helios natively supports liquid cooling priority solutions, balancing air cooling flexibility, and long-term reducing TCO. Open interconnect architecture: Breaking ecological monopoly, enhancing scalability • Protocol openness: ◦ Helios abandons closed proprietary interconnects (such as NVLink), adopting Ultra Accelerator Link + Ultra Ethernet open standards, compatible with mainstream network protocols (Ethernet Alliance UEC), supporting seamless integration with third-party devices (such as switches from different manufacturers). ◦ Users can freely choose heterogeneous computing resources, weakening the risk of single vendor lock-in (especially beneficial for avoiding geopolitical restrictions). • Interconnect bandwidth leap: Bandwidth between CPU-GPU-DPU doubles compared to the previous generation (up to 1.6 TB/s), horizontal expansion bandwidth of 43 TB/s, building a non-blocking communication network, supporting efficient data flow transmission within racks and across clusters. Full-stack collaborative optimization: Deep integration of hardware and software • Maximizing heterogeneous computing efficiency: EPYC CPU and MI400 GPU achieve zero-copy data transmission through unified memory addressing and interconnect protocols, reducing communication overhead; DPU offloads network/storage tasks, freeing up CPU/GPU computing power to focus on core AI computation. • Software-driven hardware advantages: ◦ ROCm 7 stack deeply adapts to Helios hardware features, automatically optimizing FP4/FP6 low-bit operations and distributed training algorithms (such as Triton Kernel optimizing FP8/FP6 GEMM operations); ◦ Actual tests show that when running models such as Llama 3.1 under open-source frameworks (such as vLLM), Helios' inference throughput is 1.2–1.3 times higher than NVIDIA solutions (proprietary TensorRT-LLM framework), with significant cost-effectiveness. Strategic ecological value: Responding to industry competition and geopolitical needs • Countering NVIDIA's ecological barriers: Through open interconnect (avoiding NVLink binding) and open-source ROCm software, attracting customers seeking technological autonomy (such as some Chinese internet companies and research institutions facing supply chain risks). • Local deployment flexibility: Helios design is compatible with open computing project (OCP) standards, facilitating customized production and supply chain localization in regional markets (reducing sensitivity to export restrictions), especially suitable for government and enterprise customers with high requirements for supply chain controllability. 3\. Industry positioning and strategic significance of Helios • Target market: Large-scale AI training clusters, distributed inference services, cloud computing centers, and high-performance computing (HPC) laboratories, serving leading cloud service providers (such as AWS, Azure), AI startup giants (OpenAI, etc.), and research institutions. • Technology evolution route: Helios is the core carrier of AMD's "three-year AI strategic plan": ◦ 2026 deployment baseline version: Based on EPYC Venice, MI400, and Pensando Vulcano; ◦ 2027 iterative upgrade: Integrating next-generation EPYC Verano, MI500 GPU, and optimized cooling and power supply, maintaining performance leadership. • Transformation signal: Marks AMD's transition from a traditional chip supplier to an end-to-end AI system solution provider, competing for NVIDIA's dominant data center market share through system-level innovation. • Developer and customer appeal: OpenAI and other partners have publicly affirmed Helios' potential (such as Sam Altman calling it "redefining data centers"), with some companies achieving over 40% reduction in inference costs through this solution. 4\. Comparison with competitors: Advantages materialized Using NVIDIA's contemporaneous high-end rack solutions (such as Vera Rubin NVL144) as a reference: Dimension Helios advantage manifestation Computing power density Single rack integrates 72 MI400 (FP4 2.9 EFLOPS) vs. competitors with approximately the same scale but lagging FP4 computing power (AMD claims to lead by 1.9 EFLOPS) Memory and bandwidth HBM4 capacity (31 TB) and bandwidth (1.4 PB/s) both exceed competitors by more than 50%, supporting larger models and faster transmission Deployment openness Open interconnect protocol compatible with third-party devices, avoiding single ecosystem binding; supports dual-mode cooling (liquid/air) to adapt to a wider range of environments TCO and energy efficiency Claims 40% higher output per dollar, significantly reduced operating costs; dual-width rack cooling optimization extends hardware life, indirectly reducing costs Software autonomy ROCm open-source stack weakens CUDA dependency, lowers development threshold, and supports convenient model migration (such as vLLM framework performance advantage) 5\. Summary: The disruptive nature of Helios The Helios AI rack system launched by AMD in mid-2025 is a milestone in its strategic upgrade: • In terms of composition, it integrates top-level MI400 GPU, Zen 6 EPYC CPU, Pensando DPU, innovative cooling and power supply, and ROCm software stack, forming an out-of-the-box supercomputing engine; • In terms of advantages, it eliminates deployment complexity through unified system design, with crushing computing power density, ultra-high interconnect bandwidth, open architecture, and deep software collaboration, solving long-standing performance bottlenecks, cost overruns, and ecological monopoly issues in AI infrastructure. Helios is not just a hardware stack, but a declaration of AMD's system-level thinking to redefine AI computing boundaries—aiming to make AI computing as efficient, easy to use, and scalable as electricity. As it officially lands in 2026 and iterates subsequently, Helios may reshape the industry competition landscape, driving AI from "laboratory exploration" to "large-scale inclusive application" era. In the future, whether it can fulfill its promise depends on AMD's continuous execution in mass production stability, software maturity, and customer ecosystem expansion, but it has undoubtedly set a new benchmark for the industry. $AMD(AMD.US) ### 相关股票 - [AMD (AMD.US)](https://longbridge.com/zh-CN/quote/AMD.US.md)