One place to follow every major model release, funding round, product launch, and breakthrough from China's AI ecosystem.
China AI Index is a research project built and maintained by Tony Peng — a long-time observer of the AI industry with a background in journalism and communications — with the assistance of AI (Claude Code, Qoder, and Z Code). Tony is also the writer behind Recode China AI, a newsletter tracking the latest advances and news stories in intelligent technologies.
It exists to give English-speaking researchers, investors, journalists, and developers a structured reference for China's AI ecosystem — most authoritative information is published in Chinese, and this site translates and synthesizes it.
All pricing, benchmark scores, funding amounts, and valuations are sourced from public company announcements, tech media reporting, and developer documentation. They are provided for informational purposes only and may be approximate or subject to change.
Stock prices shown are approximate historical ranges — not real-time quotes. Always verify with a live financial data source before making investment decisions. Benchmark scores reflect results at the time of model release and may not account for subsequent updates.
This database is independently maintained and has no affiliation with any of the companies listed. Information is updated manually on a best-effort basis.
Sets a lifecycle security framework for AI agents across five stages — assessment, preparation, deployment, use, decommissioning. Requires least-privilege access, restricted network exposure, full audit logging, secondary confirmation on high-risk operations (payments, deletions, permission changes), sandboxing, and secure data wipe on decommission. Reflects a shift from treating agents as lightweight LLM wrappers to integrated systems with memory/tools/autonomy that need dedicated governance.
View Official Text ↗Mandates two-layer labeling for all AI-generated text, images, audio, video, and virtual scenes: an explicit label visible to users (text, voice, or graphic indicator) and an implicit label embedded in file metadata as a digital watermark carrying generation information and producer details. Download, copy, and export functions must preserve labels. Content distribution platforms must verify metadata and add indicators when redistributing AI content. Users publishing AI-generated content must declare its origin. Prohibits malicious deletion, tampering, forgery, or concealment of required labels, and bans tools that enable such conduct. Issued March 7, 2025; effective September 1, 2025.
View Official Text ↗Domestic AI accelerators, large-scale training clusters, and memory chips — tracking the hardware layer behind China's AI buildout.
| Company | Chip | Role | Peak Compute | Memory Tech | Capacity | Bandwidth | Interconnect Bandwidth |
TDP | Process | Status | Highlights |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Huawei ↗ | Ascend 910B |
Training |
256 TFLOPS
BF16
|
HBM2e | 64 GB | 2.0 TB/s |
392 GB/s
HCCS 3.0
|
400 W | SMIC N+2 | Production | Most-deployed domestic training chip. Used at Alibaba, ByteDance, Baidu, and Tencent. |
| Huawei ↗ | Ascend 910C |
Training |
800 TFLOPS
FP16
|
HBM2e | 96 GB | 4.0 TB/s |
800 GB/s
HCCS
|
900 W | SMIC N+2 | Production | Dual-die upgrade to 910B. Powers the Atlas 900 A3 SuperPoD (300 PFLOPS). |
| 🔒 18 more accelerators — paid subscribers only | |||||||||||
| Cluster / Supernode | Operator | Chip | Chip Count | Total Compute | Interconnect Fabric | Notes |
|---|---|---|---|---|---|---|
Atlas 900 A3 SuperPoD (CloudMatrix 384) |
Huawei Cloud | Ascend 910C | 384 |
300 PFLOPS
FP16
|
All-optical (OXC) | Huawei's current-generation supernode. Uses optical circuit switching (OXC) for low-latency inter-chip communication. Announced April 2025 as a full-stack domestic alternative to Nvidia GB200 NVL72. |
Atlas 950 SuperPoD |
Huawei Cloud | Ascend 950 | 8,192 |
8 EFLOPS
FP8
|
16.3 PB/s aggregate | Next-generation supernode targeting 2026 delivery. 1,152 TB total memory. Full liquid cooling. Footprint larger than two basketball courts. Would represent a major leap in domestic training capacity if delivered at spec. |
| 🔒 3 more clusters — paid subscribers only | ||||||
| Company | Product | Type | Capacity | Bandwidth | Process | Status | Notes |
|---|---|---|---|---|---|---|---|
| CXMT ↗ | DDR5 |
DDR | 16–32 Gb dies | ~6400 MT/s | ~16nm (G4) | Production | CXMT's latest-generation DDR5, unveiled Nov 2025 — its push into the high-end DRAM market against Samsung, SK Hynix, and Micron. |
| CXMT ↗ | LPDDR5X |
LPDDR | Mobile | ~8533 MT/s | ~16nm | Production | Low-power mobile DRAM for smartphones and edge/AI devices. |
| 🔒 2 more memory chips — paid subscribers only | |||||||