Ascend 910C AI Chip: Three Breakthrough Technologies
While Nvidia flexes its H100 muscles, the Ascend 910C rewrites the rules with "Chinese innovation." Benchmarks show 780 TFLOPS FP16 performance per card, reaching 60% of H100's inference capability. Three killer features make it the "Pride of Chinese AI": Dual-Chip Packaging Powerhouse
Using Chiplet technology to combine two 910B processors into a "performance beast," the 910C delivers 40% more compute power with 15% lower power consumption. One cloud provider reported 47% faster training times for 100B-parameter models compared to traditional solutions. Optical Interconnect Revolution
Replacing copper with 6,912 800G LPO optical modules creates an all-optical network. In CloudMatrix 384 supercomputers, 384 chips achieve 153,600 Gb/s total bandwidth - 5.3x Nvidia's GB200 - reducing parameter synchronization latency from milliseconds to microseconds. 7nm Process Breakthrough
SMIC's N+2 process + CoWoS-L packaging crams 53 billion transistors into the 910C. Despite trailing TSMC's 4nm, architectural optimizations achieve 2.1 TFLOPS/W efficiency - 116% better than H100. One autonomous driving company saw 89% better LiDAR processing efficiency.
Ascend 910C AI Chip in Action: Turbocharging Chinese LLMs
Training Chinese LLMs is like fitting rockets to elephants - massive data, complex logic. The 910C's "inference-optimized design" makes it effortless:
| Metric | Ascend 910C | Nvidia H100 |
|---|---|---|
| Single-Card Inference | 1920 tokens/s | 3200 tokens/s |
| Cluster Density | 300 PFLOPS | 180 PFLOPS |
| Memory Bandwidth | 3.2 TB/s | 3.35 TB/s |
| Power Efficiency | 1.87 W/TFLOP | 0.81 W/TFLOP |
?? DeepSeek-R1 Case Study
A 910C cluster deployed by Silicon Minds and Huawei Cloud achieves 1920 tokens/s decoding throughput under 20 TPS pressure. Elastic parallel technology boosts sparse MoE model efficiency by 220% versus traditional GPUs.
?? Industrial Inspection Breakthrough
A 3C electronics manufacturer improved defect detection accuracy from 99.2% to 99.97% with the 910C, slashing inspection time from 5s to 0.8s per circuit board - saving ￥27M annually.
5 Steps to Master the Ascend 910C AI Chip
STEP 1: Hardware Selection
The CloudMatrix 384 solution combines 12 compute racks + 4 network racks with optical interconnects. One AI company trained 175B-parameter models 1.7x faster than H100 clusters. STEP 2: MindSpore Framework Tuning
CANN 6.0's auto-mixed precision reduces FP16 training loss fluctuations by 43%. With ModelArts compression tools, ResNet-50 models shrink 68% with just 0.3% accuracy drop. STEP 3: Optical Network Optimization
Adjusting LPO wavelength allocation cuts cross-rack latency from 15μs to 7μs. One cloud provider increased BERT-large inference throughput by 134%. STEP 4: Power Efficiency Tactics
Dynamic voltage/frequency scaling (DVFS) reduces cluster power 28% at<60% load.="" liquid="" cooling="" optimizes="" pue="" from="" 1.35="" to="" 1.12.="">STEP 5: Ecosystem Migration
Huawei's CUDA-to-CANN converter cuts PyTorch migration work by 72%. One AV company fully migrated perception algorithms in 3 weeks.
The Future: Ascend 910C AI Chip's Roadmap
While others play sanction games, the 910C charts three evolutionary paths:
?? 6nm Process + 3D Stacking
Next-gen 920C will use SMIC N+3 for 65% more transistors. Through-silicon vias (TSV) enable triple stacking, targeting 1.5 PFLOPS per card.
?? Global AI Compute Network
Huawei plans a "Galaxy AI Net" with 100K 910C nodes for exascale distributed training. This "compute grid" lets remote researchers access Shanghai Supercomputing Center's idle capacity.
?? Edge-to-Cloud Deployment
The Ascend Nano phone chip will federate learning with 910C. One medical consortium improved cross-hospital tumor model accuracy by 39% without data sharing.