June 6, 2026 — COMPUTEX 2026 concluded yesterday in Taipei. Under the theme "AI Together," this year's event set records with 1,500+ exhibitors and 6,000 booths. The head-to-head battle between NVIDIA, Intel, and AMD in the AI PC space was the defining story of the show.

1. NVIDIA RTX Spark: June Launch at $1,399​

Less than a week after its COMPUTEX debut, the NVIDIA-MediaTek RTX Spark Superchip confirmed its commercial timeline:

Market Reaction: AMD, Intel, and Qualcomm shares fell following the announcement. Analysts believe RTX Spark will reshape the market across three fronts — Windows AI PCs, creator workstations, and edge inference nodes.

2. Intel 18A in Full Production: Clearwater Forest + Crescent Island​

Intel CEO Lip-Bu Tan delivered his first COMPUTEX keynote with two key updates:

Clearwater Forest (Xeon 6+)​

• 288 cores, Darkmont architecture
• First Intel 18A process node data center CPU
• Foveros Direct 3D packaging
• Now in full production

Crescent Island AI GPU​

• 480 GB LPDDR5x memory
• 350 W air-cooled PCIe form factor
• Native FP4 support, targeting agentic inference
• Shipping H2 2026

"As AI moves into the agentic era, the CPU returns to the center of modern AI infrastructure." — Lip-Bu Tan

3. AMD Ryzen AI 400 Series Now Shipping​

AMD showcased the Ryzen AI 400 series (Zen 5 + Zen 5C hybrid + XDNA2 NPU) at COMPUTEX:

• NPU performance: 60 TOPS, the highest in x86
• 7 consumer SKUs + commercial PRO series
• Multiple OEM models already available or launching soon
• Advancing AI 2026 summit set for July in San Francisco

4. Chinese Domestic Chips Gaining Momentum​

5. The AI PC Era: Three-Way Roadmap Comparison​

This Week in AI Compute (6/1 – 6/6)​

Sources: COMPUTEX Daily, Tencent News, Phoenix Technology, Xueqiu, The Silicon Review.

June 1-5, 2026, Taipei — Computex 2026 (Taipei International Information Technology Show) wrapped up successfully this week. With the theme "AI Together," industry giants including NVIDIA, Intel, AMD, and Qualcomm unveiled numerous AI compute products in rapid succession. Below, MirrorFrog brings you a roundup of the most noteworthy developments in the compute card space this week.

① NVIDIA DGX Station for Windows: A Desktop AI Supercomputer​

NVIDIA officially launched the DGX Station for Windows during its Computex 2026 keynote, calling it "the world's most powerful desktop AI supercomputer."

Core Specifications​

Significance: DGX Station compresses AI compute power (20 PFLOPS FP4) that previously required datacenter-class clusters into a single desktop workstation. 748GB of unified memory means developers can run models with hundreds of billions or even trillions of parameters locally, without cloud dependency.

② Intel Crescent Island: Inference-Specialized AI GPU​

At Computex, Intel disclosed detailed specifications for its next-generation datacenter AI inference GPU, Crescent Island.

Significance: Crescent Island represents Intel's key strategic move in the AI inference market. 480GB of massive LPDDR5x memory (non-HBM) means significantly lower cost compared to NVIDIA H200/B200 and other competing products, targeting enterprise inference deployment scenarios.

③ Intel Xeon 6+ (Clearwater Forest): First Intel 18A Datacenter CPU​

Intel also unveiled the new Xeon 6+ processor, codenamed Clearwater Forest, its first datacenter CPU built on the 18A process:

• 288 Darkmont architecture cores
• L2 288MB + L3 576MB cache
• 12-channel DDR5-8000 memory
• Foveros Direct 3D advanced packaging
• AI Agent Era: CPU returns to the center of infrastructure

④ NVIDIA RTX Spark Ecosystem Takes Shape​

This week, the RTX Spark super chip developed in collaboration between NVIDIA and MediaTek continued to generate buzz. Multiple OEMs showcased RTX Spark-based laptop and compact desktop prototypes:

• ASUS, Dell, HP, Lenovo, Microsoft Surface, MSI all confirmed as launch partners
• Equipped with 20-core Grace CPU + Blackwell GPU (6144 CUDA cores)
• AI compute 1 PFLOPS
• Retail availability Fall 2026

⑤ Intel × Foxconn AI Infrastructure Partnership​

Intel and Foxconn announced a joint AI infrastructure initiative, covering the complete chain from chip → server → rack-scale system, targeting the datacenter market opportunity driven by surging AI inference demand.

⑥ Domestic AI Chip Developments​

According to the IDC 2025 annual report, total AI accelerator card shipments in China reached approximately 4 million units, with domestic vendors shipping approximately 1.65 million units, capturing a market share exceeding 41%. Huawei's Ascend 950 series has entered mass production and delivery, while Cambricon's MLU690 has begun shipping to internet customers.

This Week's Compute Roundup​

Sources: NVIDIA GTC Taipei 2026 / Computex 2026 official announcements, Intel press releases, ifeng Tech, IT Home.

June 2026 — Huawei's Ascend 950 series (950PR / 950DT) has entered formal mass production and delivery, a landmark event for China's AI chip industry in 2026. Meanwhile, Cambricon's MLU690 has begun shipping and Moore Threads has announced MTT S5000 specifications, formally establishing China's tri-polar AI chip landscape.

Ascend 950 Series: A Historic Breakthrough with Self-Developed HBM​

Huawei HiSilicon's Ascend 950 series is the fourth-generation Ascend AI chip, first revealed at Huawei Connect 2025 in September and entering mass production in Q1 2026.

950PR (Prefill Inference Specialized)​

950DT (Decode + Training Specialized)​

Historical Significance​

Self-developed HBM (HiBL 1.0 / HiZQ 2.0) represents the most important technical breakthrough of Huawei Ascend 950 — this is the first time a Chinese enterprise has achieved self-developed mass production of HBM memory, completely eliminating dependence on SK Hynix / Samsung HBM supply. Combined with the domestic N+2 process, Ascend 950 has achieved full-chain domestic production from HBM → Compute Die → Packaging → System.

Cambricon MLU690: China's Only Native FP8 Support​

Cambricon's seventh-generation AI chip MLU 690 (Siyuan 690) began volume production and shipping in H1 2026. This is the first domestic AI chip with native FP8 precision support.

MLU 690's FP8 compute power (2 PFLOPS dense) is on paper comparable to NVIDIA Blackwell (B200 FP8 4.5 PFLOPS sparse). Leveraging its financing advantage as a STAR Market listed company, Cambricon targets 2026 revenue of ¥15-20B (2025: ¥7.2B).

Moore Threads MTT S5000: From Graphics to Training-Inference Unified​

Moore Threads publicly disclosed detailed specifications of the MTT S5000 in February 2026, featuring the fourth-generation MUSA "Pinghu" architecture, single-card AI compute of 1,000 TFLOPS, 80GB GDDR6X memory, 1.6 TB/s bandwidth.

Moore Threads pursues a full-function GPU path (graphics rendering + AI compute + general-purpose compute), closest to NVIDIA's strategy. The founding team comes from former NVIDIA China, and the MUSIFY toolchain helps auto-migrate CUDA code to the MUSA platform, lowering ecosystem migration costs.

China's Tri-Polar AI Chip Landscape​

Global Market Comparison (Q2 2026 Update)​

Note: NVIDIA uses sparse compute as standard, while AMD / Huawei / Cambricon use dense — not directly comparable.

Outlook for H2 2026​

• NVIDIA Rubin R200: Official shipment in H2 2026, 288GB HBM4, 6-chip CoWoS-L packaging
• Huawei Ascend 960: Roadmap H2 2027, expected FP8 compute doubled to 2 PFLOPS
• Cambricon MLU790: Expected 2027, 3nm, 384GB HBM4, 2.5 PFLOPS
• Moore Threads: Next-gen GPU expected with HBM3, 2× MTT S5000 compute

By 2026, China's AI chip industry has formed a complete product matrix from Training (Cambricon MLU690 / Ascend 950DT) → Inference (Ascend 950PR / Moore Threads S5000) → Systems (CloudMatrix / Distributed Clusters).

This article is based on public information from Huawei Connect 2025 (2025-09-18), industry analysis reports from April 2026, and the latest market data as of June 2026.

2026 H2 is the richest era for the AI compute market: NVIDIA Rubin R200, AMD MI400, Trainium 3, TPU 8t/8i, Ascend 920, and Groq 3 LPX are all in place. This article provides a complete selection tree to help you choose the most suitable product based on model size, training/inference, latency requirements, budget, and region.

2026年5月29日，在天津举办的2026世界智能产业博览会期间，由中国智能计算产业联盟、国家超级计算天津中心、天津市人工智能学会、深圳市人工智能行业协会、至顶科技、至顶智库联合发布了《2026全球AI算力发展研究报告》。

该报告深入分析了全球AI算力产业的发展现状与未来趋势，揭示了算力产业进入"智算驱动、体系重构"的全新发展阶段。

核心观点​

1. 算力成为国家战略要素​

全球算力产业正迈入"智算驱动、体系重构"的全新发展阶段。伴随"词元经济"的兴起，算力已成为支撑国家技术突破、产业竞争与战略布局的关键基础要素。

算力正从传统信息技术支撑演变为驱动科技创新与工业革命的战略性底座。

2. AI算力发展覆盖全链路​

AI算力发展需覆盖芯片、整机、计算集群全链路升级，同时需匹配模型训练、推理、数据准备各环节的差异化算力需求。

• 训练端：超大规模模型预训练需要万卡级算力支撑
• 推理端：超大规模模型需要千卡算力
• 数据准备：需要数十到数百卡算力规模

训练与推理两端的算力需求仍将持续增长。

3. 国内AI芯片产业特色路径​

国内AI芯片产业走"自主可控+集群突破+软硬整合+性价比优势"路线，区别于国外追求单芯片绝对算力的路径，更符合大规模算力部署需求。

4. 算力中心能耗挑战与解决方案​

算力中心已成为全球电力需求增长最快的领域。未来需构建"短期风光储一体化、中期核能、长期氢能"的多元能源供给体系。

同时，太空算力将成为解决地面算力瓶颈的新方向。

5. 算网融合成为核心方向​

未来算力将向"算网融合"方向发展，实现算力像水电一样随取随用，成为国家现代化基础设施体系的核心组成部分。

算力网已纳入国家"十五五"规划重大工程项目，与水电等公共基础设施并列成为现代化基础设施体系核心。

关键数据​

算力性能演进​

全球算力中心容量及能耗预测​

• 全球算力中心总容量：预计2030年从2026年的102GW增长至220GW

  • 其中AI负载容量从62GW提升至156GW，占比提升至71%

• 美国算力中心年耗电量：预计从292TWh增长至606TWh，占全美电力需求比重提升至11%

• 中国算力中心总容量：2030年预计接近60GW，AI负载占比提升至48%

• 全球算力中心电力消耗：根据IEA基准情景预测，2030年将从2024年的约415TWh增长到约945TWh，年均增速约15%

具身智能算力支撑数据​

• 云端算力：可实现日均生成PB级交互数据，大模型训练周期从月级缩短至周级
• 端侧算力：数十至数百TOPS算力可完成10-50ms低时延实时感知决策

产业趋势分析​

算力技术架构趋势​

1. 异构计算架构升级​

从传统CPU+GPU架构，向GPU+LPU+CPU+DPU新型异构推理架构演进。

CPU在异构架构中承担任务调度、数据预处理、串行任务处理、系统互联的核心作用。2010年"天河一号A"率先实现CPU+GPU架构规模化落地，引领全球智算底层架构方向。

2. 算力扩展路径清晰​

• Scale Up（纵向扩展）：通过提升单节点硬件配置追求极致性能
• Scale Out（横向扩展）：通过增加节点实现负载分担与高可用性

两者共同构成算力系统能力的核心支撑。

3. 超节点服务器成为主流​

具备超高互联带宽、低通信时延优势，可缩短模型训练周期。

代表产品：

• 华为昇腾384超节点
• 中科曙光scaleX640超节点
• 阿里云磐久AL128超节点
• 浪潮元脑SD200
• 昆仑芯超节点方案

4. 长上下文处理技术优化​

通过**压缩稀疏注意力（CSA）、重压缩注意力（HCA）**与滑动窗口机制协同，构建"粗粒度+细粒度、稀疏+稠密"的长上下文建模体系，提升算力使用效率。

代表应用：DeepSeek-V4的注意力架构设计。

AI算力关键领域发展趋势​

AI芯片领域​

国际厂商：

• NVIDIA：

  • 凭借Blackwell与Rubin架构领跑高端训练和推理市场
  • GTC 2026台北（6月1日）重磅发布：
    • Vera Rubin平台全面量产：NVL72机架系统，智能体吞吐量比Grace Blackwell提升10倍
    • Vera CPU正式发布：88核Olympus自研Armv9.2架构，LPDDR5X 1.5TB，1.2 TB/s，全球首款原生支持FP8的CPU
    • RTX Spark AI PC芯片：与联发科、微软联合研发（代号N1X），Blackwell GPU 1 PFLOP，128GB统一内存，台积电3nm
    • Nemotron 3 Ultra开源模型：SSM+MoE混合架构，推理速度提升5倍，成本降低30%
  • 依托CUDA生态持续扩大竞争优势

• Google：依托自研TPU深化软硬件垂直整合

• Google：依托自研TPU深化软硬件垂直整合

• AWS：通过Trainium训练芯片与Inferentia推理芯片协同，提供高性价比云端算力方案

国内厂商： 形成以华为昇腾910C、昆仑芯P800、摩尔线程MTT S5000、沐曦曦云C600为代表的产品矩阵。

2026年华为发布"韬（τ）定律"，以系统性降低时间常数为目标，通过逻辑折叠等技术提升晶体管密度，推动国产芯片技术演进。

AI工作站领域​

• 形态覆盖：分为塔式、移动、迷你三类，适配不同部署场景
• 算力等级覆盖：分为入门级、专业级、企业级三类，可覆盖从个人开发到企业级部署的全场景AI算力需求

AI服务器领域​

• 按功能可分为训练AI服务器和推理AI服务器
• 按部署方式可分为云端AI服务器和边缘AI服务器

具备高算力输出、高内存带宽、高速互联等能力，适配大规模并行计算任务。

AI算力中心领域​

• 呈现"高AI占比、高功率密度、高电力消耗"的发展趋势
• 超大规模AI算力中心成为建设重点
• 能源供给向多元清洁化方向发展

太空算力成为新方向，可依托太空持续光照、极寒真空、无大气干扰的环境优势，解决地面算力中心的能源、散热、互联瓶颈。

目前Starcloud公司、国星宇航已开展初步探索。

算力应用场景趋势​

1. 科研范式变革​

"干湿闭环"研究范式成为主流，将AI驱动的"干实验"与自动化实验验证的"湿实验"通过数据反馈形成闭环，推动科学研究从经验驱动转向模型驱动。

2. 合成生物学赋能​

AI的多任务学习与未知空间探索能力，可破解生物系统"序列—结构—功能"的复杂映射，在蛋白质合成、基因编辑与核酸疫苗领域实现应用突破。

如AlphaFold系列模型实现蛋白质结构预测革命性突破。

3. 具身智能支撑​

云端与终端算力高效协同，为具身智能提供全栈算力支撑，覆盖海量数据处理、高保真仿真、模型训练、端侧实时感知决策全链路闭环。

算力基础设施发展趋势​

算网融合成为核心方向，从"先互联再成网"向全国一体化算力网演进。

三大电信运营商已开展自有算力与全国分散社会算力的互联工作，推动算力普惠化供给。

产业生态趋势​

国产算力生态持续完善，政企学研协同加深。中国智能计算产业联盟、国家超级计算天津中心、各地人工智能学会、行业协会、产业服务机构共同搭建交流平台，推动算力技术研发、标准制定、成果转化与人才培养，助力国产算力产业高质量发展。

结论与展望​

1. 算力成为国家战略竞争力核心要素，全球主要国家纷纷加大算力基础设施投入，抢占AI时代战略制高点。

2. 国内AI芯片产业走特色化发展路径，通过集群突破、软硬整合、性价比优势，在大规模算力部署场景中形成竞争优势。

3. 算力技术架构持续演进，异构计算、超节点服务器、长上下文处理技术成为重要发展方向。

4. 算力应用场景不断拓展，从科研范式变革到合成生物学、具身智能，AI算力深度赋能前沿领域。

5. 算力基础设施向算网融合方向演进，未来算力将像水电一样成为普惠化、随取随用的公共基础设施。

参考文献：

• 《2026全球AI算力发展研究报告》（中国智能计算产业联盟等机构发布）
• 2026世界智能产业博览会（天津，2026年5月29日）

June 1, 2026, Taipei — During the Computex 2026 opening keynote, NVIDIA CEO Jensen Huang officially unveiled the RTX Spark super chip, marking NVIDIA's formal entry into the personal computer processor market dominated by Intel, AMD, Qualcomm, and Apple.

RTX Spark: The "Heart" of the Personal AI Computer​

RTX Spark was developed in collaboration between NVIDIA and MediaTek, featuring a heterogeneous package with a 20-core Grace CPU + Blackwell RTX GPU, equipped with 6144 CUDA cores. AI compute reaches 1 PFLOPS (one quadrillion floating-point operations per second), meaning personal computers now possess computing power comparable to a datacenter-class H100 GPU for the first time.

Jensen Huang's "Full-Stack AI" Strategy​

The launch of RTX Spark is a key step in NVIDIA's "full-stack AI" strategy. Jensen Huang stated during the keynote: "AI should not only run in the cloud. Everyone's computer should have the ability to run AI agents."

RTX Spark transforms NVIDIA from a datacenter GPU monopolist into a full competitor in the personal computing market. Following the announcement, shares of AMD, Intel, and Qualcomm fell accordingly.

Market Impact​

• Intel: Personal computer AI processor business faces direct threat
• AMD: Ryzen AI series must compete at the same level
• Qualcomm: Snapdragon X Elite's Copilot+ PC positioning challenged
• Apple: M-series chips are no longer the only high-performance AI PC option

Vera Rubin Platform Enters Full Mass Production​

During the same keynote, Jensen Huang also announced that the NVIDIA Vera Rubin platform has entered full mass production. Rubin R200 features a 6-chip CoWoS-L package (1× Vera CPU + 2× Rubin GPU die + I/O/HBM die), equipped with 288GB HBM4, 22 TB/s bandwidth, and 50 PFLOPS FP4 compute (sparse).

The Rubin NVL72 rack (72 Rubin GPUs + 36 Vera CPUs) will begin shipping in H2 2026.

Other Highlights from Computex 2026​

• AMD: Showcased the MI350 series (192GB HBM3e, 5 PFLOPS FP8 dense), officially launching in June
• Intel: Jaguar Shores publicly unveiled for the first time
• Qualcomm: AI 200 / 300 series inference card roadmap updated
• Domestic AI Chip Zone: Huawei, Cambricon, Moore Threads, and others showcased their latest products

Industry Significance​

The launch of RTX Spark means AI compute is no longer confined to datacenters. Individual developers, designers, and researchers will be able to run large model tasks locally that previously required cloud GPUs, potentially redefining the market landscape for personal AI computing.

The mass production of Vera Rubin further consolidates NVIDIA's absolute leadership in datacenter AI training. Together, both product lines form NVIDIA's full-stack AI computing landscape of "cloud training + personal inference."

This report is based on official NVIDIA announcements from Computex 2026 / GTC Taipei on June 1, 2026.

In 2026, AI compute growth has hit a hard constraint — electric power. With NVIDIA Rubin NVL576 single-rack power consumption at 1 MW, the xAI Colossus cluster at 200 MW, and OpenAI's planned Stargate campus at 5 GW, power supply is becoming the biggest bottleneck for AI development. This article provides an in-depth analysis of this "power crisis" and the solutions.

2026 AI chip market enters a "winner takes all" phase. NVIDIA holds 90%+ market share, AMD struggles at 10%, and Google/AWS/Huawei/Cerebras each occupy niche segments. But a group of AI chip startups are fighting to survive in the cracks — this article analyzes the 2026 status and future of Tenstorrent, SambaNova, Graphcore, Cambricon, Moore Threads, Biren, and Iluvatar.

The bottleneck for AI compute has shifted from compute itself to memory bandwidth and capacity. HBM (High Bandwidth Memory) , as a core component of AI chips, has a 2026 market size of $80B+, but there are only 3 suppliers globally — SK Hynix, Samsung, Micron. This article provides an in-depth analysis of this "memory three kingdoms" battle.

2026 AI compute enters the "rack-scale" era. Single-chip comparisons have receded, and full-rack solutions have become the main battleground. This article provides an in-depth comparison of the five major rack-scale solutions: NVIDIA Rubin NVL72/NVL576, AMD Helios, Groq 3 LPX, AWS Trn3 UltraServer, and Google TPU 8t pod.