--- 格式版本: 2 标题: "AMD Launches Helios™: The Highest Performing Rackscale AI Infrastructure Solution" 原文链接: "https://www.amd.com/en/blogs/2026/amd-launches-helios-the-highest-performing-rackscale-ai-infrastructure-solution.html" 发布日期: "2026-07-23" 发布时间校准状态: "found" 发布时间需复核: "否" 发布时间来源: "rule:scrape:provider_published_at" 发布时间证据: "provider publishedAt: 2026-07-23" 发布时间校准原因: "规则确认唯一严格发布时间，来源 scrape:provider_published_at" 发布时间校准置信度: "high" 发布时间候选数量: 5 发布时间严格候选数量: 1 发布时间原页读取状态: "原页面已读取" 发布时间未找到原因: "" 发布时间校准时间: "2026-07-25T22:44:04+08:00" 发布时间仲裁状态: "skipped" 发布时间仲裁尝试次数: 0 发布时间仲裁耗时毫秒: 0 发现时间: "2026-07-25T22:33:24+08:00" 入库时间: "2026-07-25T14:44:04.877Z" 来源平台: "固定入口" 搜索渠道: "fixed_url" 搜索词: "https://www.amd.com/en/blogs.html" 匹配关键词: - "GPU" - "Scale-up" - "HBM" - "NVL72" - "Vera Rubin" - "delivery" - "deployment" - "performance" - "latency" - "bandwidth" - "throughput" 相关厂家: - "AMD" - "NVIDIA" - "Meta" - "Microsoft" - "Oracle" - "OpenAI" 相关专家: [] 内容类型: "网页" 抓取工具: "Free Fetch + Defuddle" 清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取" 原始附件: [] AI优质: "是" AI打分: 100 AI分档: "高置信优质" AI质检状态: "通过" AI打分理由: "AMD官方发布Helios机架级AI系统，含MI455X GPU、HBM4、UALoE互连、72卡域，与NVIDIA NVL72对比，性能参数详实，获OpenAI等多家采用，完全符合项目主题。" AI质检模型: "deepseek-v4-flash" AI质检时间: "2026-07-27T11:20:26+08:00" AI主题相关性: 20 AI来源权威性: 15 AI新颖性: 20 AI技术细节: 20 AI商业部署信号: 15 AI完整性: 10 采集批次: "2026年7月25日22点32分16秒" 采集批次ID: "20260725-223216-273" 去重键: "https://www.amd.com/en/blogs/2026/amd-launches-helios-the-highest-performing-rackscale-ai-infrastructure-solution.html" --- ### Why the AI Factory Needs a New Architecture Monthly token consumption has increased 158X in two years as AI moves from experimentation into products and services. Training compute has continued to grow, increasing 5X per year since 2020. Inference is becoming the largest AI workload as models serve billions of interactions. ![Line chart shows AI training compute (FLOPS) scaling 5x yearly from 10^24 in 2022 to 10^28 by 2030, with models like GPT-4, Grok 4. Stacked bar chart shows AI workload split: Training (brown) decreases from 60% in 2024 to 40% in 2026, while Inference (teal) increases from 40% to 60% over the same period. AMD logo visible.](https://www.amd.com/content/dam/amd/en/images/blogs/designs/5086250-helios-blog/5086250-helios-blog-2.jpg) Agentic AI increases that demand further. A single request can trigger multiple reasoning steps, sub-agents, retrieval operations and tool calls, along with CPU-driven routing, scheduling and memory management. The demand is therefore not only for more tokens; every useful result requires more inference, orchestration and data movement, placing additional pressure on compute, memory capacity, networking, latency and cost per token. ![Diagram titled 'Frontier AI Needs an Open Rackscale Blueprint'. Five stages: AMD EPYC and Instinct chips for Leadership Compute Capability; Open Rack Architecture; AMD Pensando, Ultra Ethernet, ESUN for Open Fabric to Scale AI; Rack Scale Efficiency & Serviceability; and Turnkey Solutions with server racks.](https://www.amd.com/content/dam/amd/en/images/blogs/designs/5086250-helios-blog/5086250-helios-blog-3.jpg) AI requires a new infrastructure blueprint. Leadership compute is essential, but compute alone is not enough. Customers also need open rack architecture, high-bandwidth scale-up and scale-out fabrics, efficient power and cooling, serviceability, and turnkey solutions that can be deployed rapidly. AMD set out to design these capabilities together as one system. ### AMD Instinct™ MI455X GPU is the Engine. AMD Helios is the System. At the heart of AMD Helios, the AMD Instinct™ MI455X GPU delivers a generational leap in AI compute, HBM4 capacity and memory bandwidth. ![AMD Instinct MI455X GPU performance chart. Left: Bar chart shows MI455X delivers 4x, 15x, 34x higher token throughput for low, medium, high interactivity vs MI355X. Right: Line graph shows MI455X has up to 18x lower cost per million tokens vs MI355X across interactivity levels.](https://www.amd.com/content/dam/amd/en/images/blogs/designs/5086250-helios-blog/5086250-helios-blog-4.jpg) On DeepSeek-V4-Flash, one of the leading open-weight models in its class, AMD Instinct™ MI455X GPUs deliver up to 34X higher token throughput at high interactivity and up to 18X lower token cost compared with AMD Instinct™ MI355X GPUs. The results connect the architectural gains to the performance and economics required for production inference. ### Inside AMD Helios: One Rack, One Co-Designed System AMD Helios is designed around the path an AI workload takes through the rack. Work enters through the host layer, moves into the GPU domain, accesses model and active data in HBM4 and communicates across the rack through the scale-up fabric. ![AMD Helios Rackscale AI Infrastructure infographic. Shows server racks. Details: HOST (6th Gen AMD EPYC Server CPUs, 96 high-frequency cores, PCIe Gen6 CPU-to-GPU, 1.6 TB/s memory bandwidth), ACCELERATION (5th Gen AMD Instinct MI455X GPUs, 40 PF FP4 AI compute, 432 GB HBM4 capacity, 23.3 TB/s memory bandwidth), FABRIC (AMD Pensando + UALoE, 260 TB/s scale-up bandwidth, 72 GPUs one domain, 3x 800G NIC per GPU).](https://www.amd.com/content/dam/amd/en/images/blogs/designs/5086250-helios-blog/5086250-helios-blog-5.jpg) The AMD Helios rackscale solution combines 6th Gen AMD EPYC™ “Venice” 9006 Series Server CPUs, 5th Gen AMD Instinct™ MI455X GPUs and AMD Pensando™ networking with UALoE fabric in a fully co-designed platform. Together, these components connect 72 GPUs in one scale-up domain with 260 TB/s of scale-up bandwidth for frontier AI inference and training. ![Infographic details AMD Helios rackscale AI solution performance vs. Vera Rubin NVL72, showing 15% more AI compute, 50% more memory, and 50% more scale out bandwidth. Compute: 2.9 ExaFlops FP4, 1.4 ExaFlops FP8. Memory: 31 TB HBM capacity, 1.7 PB/s HBM bandwidth. Networking: 43 TB/s scale out, 260 TB/s scale up. An AMD Helios server rack is shown on the right.](https://www.amd.com/content/dam/amd/en/images/blogs/designs/5086250-helios-blog/5086250-helios-blog-6.jpg) That rack-scale design sets a new competitive bar. Compared with an NVIDIA Vera Rubin NVL72 rack, AMD Helios is designed to deliver up to 15% more AI compute, 50% more HBM capacity and 50% more scale-out bandwidth. AMD Helios combines those advantages with 2.9 exaflops of dense FP4 compute, 1.4 exaflops of FP8 compute, 31 TB of HBM4, 1.7 PB/s of aggregate HBM bandwidth, 260 TB/s of scale-up bandwidth and 43 TB/s of scale-out bandwidth. ![Bar chart: AMD Helios Delivers Industry Leading Rackscale Performance. AMD HELIOS shows +15% (Low), +12% (Medium), +10% (High) throughput vs NVIDIA Vera Rubin.](https://www.amd.com/content/dam/amd/en/images/blogs/designs/5086250-helios-blog/5086250-helios-blog-7.jpg) More compute and HBM capacity give large models and active workloads additional room within the rack. Higher HBM bandwidth helps keep the GPU domain supplied with data, and greater scale-out bandwidth provides more capacity for expanding beyond a single rack. AMD Helios brings those capabilities together as one rack-scale platform rather than treating compute, memory and networking as separate infrastructure decisions. ### Performance and Economics at AI Factory Scale Peak specifications establish the scale of AMD Helios. In production, the practical measures are throughput at the required level of interactivity, tokens per dollar and the number of active workloads a rack can support. ![Data center aisle with server racks and blue lights. Text: Up To 30% More Tokens / $ Using AMD Helios Vs. Nvidia Vera Rubin NVL72.](https://www.amd.com/content/dam/amd/en/images/blogs/designs/5086250-helios-blog/5086250-helios-blog-8.jpg) On Kimi K2 Thinking with a 32K input and 8K output sequence, modeled AMD Helios throughput per GPU is up to 15% higher at low interactivity, 12% higher at medium interactivity and 10% higher at high interactivity than modeled NVIDIA Vera Rubin NVL72 rack performance. The comparison shows how the rack’s compute, memory and fabric design supports throughput across different token-delivery targets. ### Open at Every Layer, From Rack to Software ![AMD Helios presentation: 'AMD Helios Adopted By Leading AI Companies'. Lists 'AT SCALE AI PARTNERS' (OpenAI, Meta, Microsoft, Oracle) and 'INFRASTRUCTURE PARTNERS' (Dell, HPE, IBM, Cisco). Dark gray AMD Helios server rack shown.](https://www.amd.com/content/dam/amd/en/images/blogs/designs/5086250-helios-blog/5086250-helios-blog-10.jpg) AMD ROCm™ software connects the rack-scale architecture of AMD Helios to production AI workloads. Native support for leading frameworks, including PyTorch, TensorFlow and JAX, lets developers use familiar tools for high-throughput inference and distributed training. Optimized libraries and open standards help applications take advantage of AMD Instinct™ GPU compute, memory and fabric capabilities across the rack. AMD ROCm software also provides tools for deployment, observability and lifecycle management, giving infrastructure teams a consistent software foundation for operating AMD Helios at scale. ![AMD Helios Adopted by Leading AI Companies](https://www.amd.com/content/dam/amd/en/images/blogs/designs/5086250-helios-blog/5086250-helios-blog-11.jpg) AMD Helios is being adopted across AI leaders, cloud partners and infrastructure partners. That breadth matters because rack-scale infrastructure reaches production through a connected supply chain, from model developers and cloud operators to the companies building and servicing the physical systems. ### Final Takeaway AMD Instinct™ MI455X GPUs bring a major increase in compute, memory bandwidth and HBM capacity. AMD Helios scales that engine into a complete rack-scale platform, connecting 72 GPUs with AMD EPYC™ “Venice” server CPUs, AMD Pensando™ networking, AMD ROCm™ software and an open system architecture. AMD Helios also marks a broader shift in how AMD advances AI infrastructure. Annual execution now connects successive AMD Instinct GPU generations with progress in memory, interconnect and GPU scale-up. The result is an open, multi-generation compute cadence for rack-scale AI. With AMD Helios, the rack is no longer simply where the AI system is installed. The rack is the AI system, and AMD is delivering an open platform for the AI factory era. Footnotes GENERAL DISCLAIMER The information contained herein is for informational purposes only and is subject to change without notice. While every precaution has been taken in the preparation of this document, it may contain technical inaccuracies, omissions and typographical errors, and AMD is under no obligation to update or otherwise correct this information. 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