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23 posts tagged with "Domestic Chips"

Chinese domestic AI chip development

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Computex 2026 Wrap-Up: AI PC Chip War Begins, NVIDIA RTX Spark Arrives Fall 2026

· 3 min read
Industry Research Team

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:

DetailInfo
Launch OEMsASUS, Dell, HP, Lenovo, Microsoft Surface, MSI
AvailabilityFall 2026
Starting PriceNot yet announced (analysts estimate $3,000-4,000)
Core SpecsArm CPU (up to 20 cores) + Blackwell GPU (6,144 CUDA cores)
Unified Memory128 GB LPDDR5X (300 GB/s)
Model CapacityRuns 120B parameter models, up to 1M token context

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​

VendorProductStatus
HuaweiAscend 950PR/950DTIn production, self-developed HBM
CambriconMLU6902 PFLOPS FP8, shipping
Moore ThreadsMTT S50001,000 TFLOPS, specs public

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

DimensionNVIDIA RTX SparkIntel Clearwater Forest + Crescent IslandAMD Ryzen AI 400
CPU Cores20-core Grace (Arm)288-core Darkmont (x86)Up to 12-core Zen5+5C
GPU/NPUBlackwell GPUCrescent Island (discrete GPU)XDNA2 NPU (60 TOPS)
AI Compute1 PFLOPSTBD60 TOPS NPU
TargetPersonal AI agentsDual-track: DC + AI PCCopilot+ PC
ProcessTSMC 4NPIntel 18ATSMC 4nm
AvailabilityJune 2026H2 2026Shipping now

This Week in AI Compute (6/1 – 6/6)​

DateEvent
Jun 1NVIDIA GTC Taipei: RTX Spark, Vera Rubin production, DGX Station for Windows
Jun 1Intel unveils Crescent Island, Clearwater Forest
Jun 2COMPUTEX 2026 opens: "AI Together"
Jun 5COMPUTEX closes: 1,500+ exhibitors, record scale
Jun 6RTX Spark confirmed June launch at $1,399

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

Huawei Ascend 950 Mass Production and the Full Picture of China's AI Chip Ecosystem

· 4 min read
Industry Research Team

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)​

ItemSpecification
ArchitectureDa Vinci v5 (SIMD + SIMT dual-model)
ProcessN+2 (SMIC domestic)
HBMHiBL 1.0 (Huawei self-developed) , 128 GB
FP8 Compute1 PFLOPS (HiF8 format)
TDP~400 W
TargetInference Prefill (video recommendation, real-time interaction)

950DT (Decode + Training Specialized)​

ItemSpecification
ArchitectureDa Vinci v5 (SIMD + SIMT dual-model)
ProcessN+2 (SMIC domestic)
HBMHiZQ 2.0 (Huawei self-developed) , 144 GB, 4 TB/s
FP8 Compute1 PFLOPS (HiF8 format)
TDP~500 W
TargetInference Decode + Model Training

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.

ItemMLU 690
Process5nm (TSMC / SMIC)
FP8 dense2 PFLOPS
HBM192GB HBM3E, 5 TB/s
TDP~500 W
Unit Price (OAM)~$8,000-12,000

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​

DimensionHuawei AscendCambriconMoore Threads
Core ArchitectureDa Vinci v5MLUv07MUSA 4th Gen
ProcessN+2 domestic5nm6nm
FP8 Compute~1 PFLOPS2 PFLOPS0.5 PFLOPS (estimated)
HBM Self-Sufficiency✅ Self-developed HiBL/HiZQ❌ Purchased❌ Purchased
EcosystemCANN + MindSporeNeuWare + MindSporeMUSA + MUSIFY
AdvantageFull-chain domesticHighest FP8 computeFull-function + CUDA migration
2025 Revenue(Huawei internal)¥7.2B¥2.2B

Global Market Comparison (Q2 2026 Update)​

TierVendorFlagship ChipFP8/PFLOPSHBMMass Production
Tier 1NVIDIARubin R20025 PF (sparse)288GB HBM42026 H2
Tier 2AMDMI40020 PF (dense)432GB HBM42026
HuaweiAscend 950DT1 PF (dense)144GB self-developed HBM2026 Q1
CambriconMLU6902 PF (dense)192GB HBM3E2026 H1
AWSTrainium 35.7 PF (dense)144GB HBM2025 Q4 GA
Tier 3IntelGaudi 31.8 PF128GB HBM2eIn production
GoogleTPU v74.6 PF(TFLOPS)192GB HBM2025
Moore ThreadsMTT S50001 PF80GB GDDR6X2025 Q1

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.

China AI Chip Landscape 2025: Ascend, Cambricon, Hygon — Who Will Dominate?

· 5 min read
Industry Research Team

Escalating U.S. export controls are forcing China's AI chip industry to accelerate self-reliance. By 2025, the discussion around domestic Chinese AI chips has shifted from "are they usable?" to "which one should I choose?"

This article systematically reviews the major players, core products, and actual deployment status of domestic AI chips, helping developers and procurement decision-makers understand the competitive landscape.