商店现已上线

我们打造用于网站、AI 智能体和 LLM 训练的 Workstation AI 机器——在线配置并购买。

获取同行价格
AI 产品
AI 实验室OpenAI 智能体Claude 代理Grok BotWorkstation AI 原生 CRM营销所有产品
AI 解决方案
应用型 AIAI 智能体生成式 AIAI 自动化AI 技能提升中小企业 AI 套餐企业 AI 实验室AI 工作站NVIDIA 硬件与软件NVIDIA 加速计算私有 AIGPU 私有云GPU 集群边缘 AI行业 AI
AI 服务
平台现代化数字工程数据基础与 AI自主运营AI 咨询DevOps 自动化网络安全软件开发智能体构建MLOps 搭建GPU 租赁GPU 私有云
关于我们
公司我们的故事合作伙伴客户案例
AI 数据中心
AI 数据中心GPU 私有云GPU 集群GPU 租赁
AI 洞察Jev 系列基准测试实验室模型选择
技术说明
文档
Linux 操作系统UbuntuRed Hat Enterprise LinuxSUSE LinuxAlpine LinuxDebian, Rocky & AlmaLinux
AI 云平台AWS & Amazon BedrockGoogle Cloud & Vertex AIMicrosoft Azure & AI FoundryGPU 云与算力市场
私有 AI 数据中心面向律师事务所面向会计师事务所本地部署平台全面安全的云
工具Ring PromoterJobshout
新闻
博客
联系我们登录

面向现代企业的 AI 工作站、AI 多智能体软件、GPU 基础设施和智能代理解决方案。

联系我们

AI 解决方案

应用型 AIAI 智能体生成式 AIAI 自动化AI 技能提升中小企业 AI 套餐企业 AI 实验室行业 AI

产品

所有产品Workstation AI native CRM 与 ERP营销OpenAI 智能体Ring PromoterJobshout

公司

关于我们公司AI 数据中心为什么选择Workstation合作伙伴客户案例定价联系TenthMatrix

资源

AI 洞察技术说明文档Linux 操作系统AI 云平台私有 AI 数据中心博客搜索网站地图
英国总部
77-79 Marlowes, Hemel Hempstead HP1 1LF路线指引 — 从 M25 外环伦敦 20 号出口驶出公司编号: 11641870周一至周五:上午 9:00 - 下午 6:00 GMT
+44 7515 356 146
比利时办公室
Workstation SRL, Rue Vanderkindere 34, 1180 Uccle, BrusselsBE 0751.518.683周一至周五:上午 9:00 - 下午 6:00 CET
+32 492 45 67 46
印度办公室
#159 Sector 9, Pocket 1, DDA Flats, 110077 Dwarka, New Delhi
+91 98881 98841
台湾办公室
3F, No. 2-4, Aisi Rd., Ren'ai Dist., Keelung City 200, Taiwan基隆市仁愛區愛四路2之4號3樓周一至周五:上午 9:00 - 下午 6:00 台湾时间(GMT+8)
+886 926 892 701+32 492 45 67 46

© 2026 Workstation AI。保留所有权利。

隐私数据删除Cookie服务条款网站地图

NVIDIA, the NVIDIA logo, RTX, RTX PRO, Blackwell, CUDA, NVLink, MGX, Omniverse, Spectrum-X, Quantum, BlueField, DGX and HGX are trademarks and/or registered trademarks of NVIDIA Corporation in the US and other countries. AMD, the AMD Arrow logo, EPYC, Instinct, Ryzen and Threadripper are trademarks of Advanced Micro Devices, Inc. Dell and PowerEdge are trademarks of Dell Inc. or its subsidiaries. Intel and Xeon are trademarks of Intel Corporation or its subsidiaries. AMAX, AceleMax and LiquidMax are trademarks of AMAX Engineering Corporation. All other product names, logos and brands are the property of their respective owners. We use them only to identify the products we describe, supply or support; their use does not mean their owners endorse us.

Loading blog...

首页 / 博客
AIAmdData CentreGPUNvidia

Chipping Away at Nvidia's Lead: Lisa Su's AMD Plan, Two Years On

Workstation2026年11月14日4 min read
79f02641 8b11 4fbd babc e97a18613314

In December 2024, Bloomberg's Emily Chang visited AMD's labs in Austin to ask Lisa Su, ten years into her time as CEO, how she planned to chip away at Nvidia's lead in AI. Almost two years on, AMD's own results let us check the plan against what happened, and ask what it means for UK organisations buying AI infrastructure today.

Watch the interview. AMD's CEO Wants to Chip Away at Nvidia's Lead | The Circuit with Emily Chang (Bloomberg Originals, 24 minutes, published 6 December 2024).

What Lisa Su said

The episode is part company profile, part race story, and Su's lines are worth hearing in full. The points that matter for anyone buying AI hardware:

  • The pace. She called AI "the most transformational technology that I've seen in my career", ahead of the internet, PCs, mobile and cloud, and said the industry had made more progress in the previous 18 months than in the ten years before.
  • The product. She showed the Instinct MI300, already in production and running work such as GPT-4o on Microsoft Azure and Meta's Llama models.
  • The underdog question. Asked whether being seen as the next-best option to Nvidia was good enough, she said AMD has always "punched well above our weight" in technology. Bloomberg's chip reporter Ian King described AMD as "a solid number two".
  • Custom chips. On hyperscalers designing their own silicon: "This is a huge market... There's like more than enough chips for everybody to build."
  • Export controls and supply. She accepted there is "a national security element" to limits on AI chip exports, while wanting "as open a market as possible", and stressed resilience in a supply chain concentrated in Taiwan.

The rest covers her ten years turning AMD around (the Zen architecture was "the most important moment"), the Mercedes Formula 1 partnership, a Porsche collection named after AMD products, and her advice to young engineers: "run towards problems".

Two years on: the scorecard

What Lisa Su said in December 2024, and what has happened since. Five themes from the interview checked against November 2026. AI pace: she called AI the most transformational technology of her career; AMD's Data Center revenue has nearly doubled year on year. Competing with Nvidia: Bloomberg called AMD a solid number two; Helios now ships to five of the largest AI buyers, though Nvidia remains far larger. Custom chips: she said there are more than enough chips for everybody; hyperscalers build their own and also buy AMD, and Meta is deploying a custom MI450-based GPU. Supply resilience and export controls: she backed national-security controls with as open a market as possible, and stressed supply-chain resilience; manufacturing is still concentrated in Taiwan. Software: not covered in depth on camera; ROCm 10 is now the deciding factor for most buyers.
Five themes from the December 2024 interview checked against November 2026. Sources: the Bloomberg Originals episode and AMD's third-quarter 2026 results.

AMD's third-quarter 2026 results, published on 3 November, are the clearest check. Data Center revenue reached a record $8.5bn, up 97% year on year, driven by EPYC server processors and the launch of the Instinct MI450 Series. Anthropic, Meta, Microsoft, OpenAI and Oracle Cloud Infrastructure are running models on the first Helios racks, AMD's answer to Nvidia's rack-scale systems. AMD expects around $17bn of total revenue in the fourth quarter.

AMD's AI data-centre push, December 2024 to November 2026. A timeline. December 2024: Lisa Su on Bloomberg's The Circuit, ten years as CEO, with the Instinct MI300 in production and running GPT-4o on Microsoft Azure and Meta's Llama. June 2025: Instinct MI350 Series launched. October 2025: multi-year agreement with OpenAI for 6 gigawatts of Instinct GPUs. 2026: Helios rack-scale system with 72 Instinct MI455X GPUs enters production and ships in volume in the third quarter; ROCm 10 released. November 2026: third-quarter Data Center revenue a record 8.5 billion dollars, up 97 percent year on year, with Anthropic, Meta, Microsoft, OpenAI and Oracle running workloads on the first Helios racks.
From the interview to Helios: MI350 in June 2025, the OpenAI agreement in October 2025, Helios shipping in volume from the third quarter of 2026. Milestones from AMD's announcements.

Helios packs 72 Instinct MI455X GPUs into one rack, each with 432 GB of the newest HBM4 memory, for 31 TB per rack. AMD claims it beats Nvidia's Vera Rubin NVL72 on compute, memory and tokens per dollar. Those are AMD's numbers, and Nvidia remains far larger, with the deeper software ecosystem. But "solid number two" now means a second full platform that the biggest AI companies run in production, not a niche alternative.

What it means if you are buying AI infrastructure in the UK

  1. You have a real choice. For many workloads, especially serving large language models with long contexts, AMD's larger memory per GPU can mean fewer GPUs per model. That is worth testing, not assuming.
  2. Software is the deciding question. Code written in PyTorch, or served with vLLM or SGLang, usually runs on AMD's ROCm software with little change. Custom CUDA code and Nvidia-only tools are where the work is.
  3. Two vendors give you leverage. Teams that can run on both negotiate better prices and are less exposed when one vendor's lead times stretch.
  4. Supply risk has not gone away. As Su said, the industry depends on a concentrated supply chain. Plan lead times into AI projects whichever vendor you choose.
Workflow: choosing AMD, NVIDIA or both for an AI project. Four questions in sequence. Does the model need more memory per GPU than you can comfortably get, for example very large models or long contexts? If so, AMD's 432 GB per GPU can reduce how many GPUs each copy of the model needs. Does your software depend on CUDA-only libraries or custom kernels you cannot port soon? If so, stay on NVIDIA for that workload. Does a benchmark of your own model, on your own serving stack, show better tokens per pound on one vendor? Follow the measurement, not the datasheet. Can you get the hardware, support and skills in time? Supply, lead times and your team's experience can decide it. Many organisations end with both: NVIDIA for CUDA-bound work and AMD where it measures better.
Four questions to decide between AMD, Nvidia or both: memory, software dependencies, a benchmark on your own model, and whether hardware and skills arrive in time.

For the full engineering detail, with the platforms compared layer by layer, memory arithmetic, the CUDA-to-ROCm software map, a porting workflow with commands, running both vendors on one Kubernetes platform and a fair benchmarking method, read our technote: AMD Instinct vs NVIDIA for AI infrastructure: evaluate, port and run both.

How Workstation helps

We design and run GPU infrastructure for UK organisations and help teams evaluate AMD alongside Nvidia: a like-for-like benchmark of your own model on both, a check of your code for CUDA-only dependencies, and a Kubernetes platform that runs either. See our GPU private cloud guide, which includes AMD's official Helios video, compare leasing and buying at gpuleasing.co.uk, or talk to us.

Quotes are from the Bloomberg Originals video. Company figures are from AMD's announcements and are vendor claims, not independent benchmarks. This post is independent and not endorsed by Bloomberg, AMD or Nvidia.

Acknowledgement of rights: AMD, Instinct, EPYC, ROCm, Helios and Zen are trademarks of Advanced Micro Devices, Inc. NVIDIA and Vera Rubin are trademarks of NVIDIA Corporation. Bloomberg is a trademark of Bloomberg Finance L.P. Other names are the property of their respective owners.