Huawei's Bold AI Leap: Doubling Ascend 910C Output as Nvidia Stumbles in China
It promises to fuel AI dreams without borders. As of September 2025, shipments are ramping up, signaling a shift in global tech power.
This article dives deep. We’ll explore its specs, rivalries, and future impact. Ready to uncover how the Ascend 910C changes the game?
What is Huawei Ascend 910C?
Huawei’s Ascend 910C marks a milestone in AI hardware. It is a graphics processing unit (GPU) built for heavy AI tasks like training massive large language models or running complex simulations.
Launched in early 2025, it targets data centers and cloud providers.
Key Features:
Architecture: Da Vinci with dual chiplets for boosted performance
FP16 Performance: Up to 800 TFLOPS, ideal for AI inference
Memory: 128GB HBM3, handling large datasets smoothly
Power Draw: Around 350W, balancing efficiency and speed
Process Node: Built on SMIC’s 7nm technology
These features make it a go-to for Chinese firms like Baidu and ByteDance. Early tests show it handles real-world AI workloads efficiently. Unlike consumer GPUs, this beast focuses on enterprise-scale computing and integrates with Huawei’s Atlas clusters for seamless scaling.
Why does it matter? In a world hungry for AI, supply chains falter. The Ascend 910C fills that gap. It empowers developers to build without delays. Imagine training a chatbot in hours, not days—that’s the promise.
Huawei Ascend 910C vs Nvidia H100: The Key Battles
The AI chip race boils down to Huawei vs Nvidia. The Ascend 910C takes on the Nvidia H100 head-on. Both shine in AI training and inference, but differences stand out.
Comparison Highlights:
Architecture: Ascend 910C uses Da Vinci dual chiplets; H100 uses Hopper
FP16 Performance: Ascend 910C at 800 TFLOPS vs H100 at 1,979 TFLOPS
Memory: Ascend 910C has 128GB HBM3 vs H100’s 80GB HBM3
Bandwidth: Close match at ~3.2 TB/s vs 3.35 TB/s
Power Consumption: 910C draws 350W vs H100’s 700W
Price: 910C costs ~$20,000–$25,000 vs H100’s $30,000+
The H100 edges in raw speed, but the 910C delivers about 60% of its inference power at lower cost and energy use. For memory-hungry models, the 910C’s extra HBM3 gives it an advantage.
Real-world example: A major Chinese e-commerce giant swapped H100s for 910Cs. Inference times dropped 20%, saving millions in power bills.
With Nvidia’s restricted chips like the H20, Huawei gains the edge in accessibility. As Gartner notes, in 2025, regional self-reliance trumps global speed.
Huawei AI Chip Production: SMIC 7nm Yields in Focus
Behind every chip lies manufacturing challenges. Huawei relies on SMIC for the Ascend 910C, and their 7nm process is crucial.
By mid-2025, yields—success rates of usable chips—climbed to 60–70%, beating early hurdles.
First batches shipped: May 2025
Year-end shipments: 300,000 units
Challenges: Packaging defects remain at 25% failure rate
Capacity: SMIC expands to 45,000 wafers per month
Local fabrication cuts costs and reduces dependency on imports. According to McKinsey, this boosts China’s AI economy by 15% annually. Huawei’s journey from sanctions to surplus is becoming a blueprint for resilience.
China’s Drive for AI Chip Self-Reliance
China’s tech vision is clear: Independence. The Ascend 910C embodies this push. U.S. bans since 2019 forced innovation, and now Huawei leads the charge.
Key Drivers:
Policy Push: Beijing invests $50 billion in semiconductors by 2025
Ecosystem Build: Firms like Cambricon join Huawei in chip design
Market Share: Huawei captures ~40% of China’s AI chip market
This self-reliance pays off. DeepSeek runs its large models fully on Huawei hardware. For businesses, this means stable supply and reduced risks from trade wars.
Ascend 910B Upgrade: What’s New in 910C?
The Ascend 910C builds on its predecessor, the 910B (2023).
Upgrades at a Glance:
Compute Power doubled from 400 TFLOPS → 800 TFLOPS
Dual-die integration merges two 910B units
20% better energy efficiency per task
Smoother cluster scaling via Huawei’s Atlas 900 A3 supercomputer
It’s like upgrading from a sports car to a rocket—faster, smarter, and built for scaling.
Tackling HBM Shortages in China’s AI Boom
High-bandwidth memory (HBM) is AI’s fuel, and China faces shortages in 2025. The Ascend 910C needs 128GB per chip, straining supply chains.
Impacts:
Production limited to 1 million units without local HBM
Huawei stockpiles imports and optimizes software to cope
China invests $10 billion to scale HBM supply, ensuring Huawei’s momentum continues.
Future Huawei AI Chips: 2026 and Beyond
The road ahead excites. Huawei plans to double Ascend 910C shipments to 600,000 units in 2026, totaling 1.6 million dies.
Coming Soon:
Ascend 950PR: Q1 2026, doubles cluster scale
Ascend 950DT: Late 2026, targets 2x performance
Atlas 950: Supports 8,000+ chips for mega-clusters
Reuters predicts Huawei could claim 50% of China’s AI chip market by 2027. With Nvidia’s struggles, expect smarter AI powering autonomous cars, cloud services, and personalized medicine.
Frequently Asked Questions (FAQ)
What is Huawei Ascend 910C?
Huawei’s advanced AI GPU for training and inference, featuring 800 TFLOPS FP16 and 128GB HBM3, built on SMIC’s 7nm process.
How does Huawei Ascend 910C compare to Nvidia H100?
It offers ~60% of H100’s inference power with more memory and lower power draw. It’s also cheaper and unaffected by U.S. bans in China.
Why is China focusing on AI chip self-reliance?
U.S. export controls limit access to top chips. Huawei’s efforts ensure stable AI growth for local firms.
What are SMIC 7nm yields for Huawei chips?
By 2025, yields reached 60–70%, enabling mass production.
What’s next for Huawei AI chips in 2026?
The Ascend 950 series launches, promising doubled performance and mega-cluster support.
Wrapping Up: The Ascend 910C Era Begins
Huawei’s Ascend 910C isn’t just a chip—it’s a statement. It challenges Nvidia, boosts self-reliance, and powers China’s AI future. From 60% H100 performance to upcoming 2026 roadmaps, the momentum is undeniable.
As bans reshape global markets, Huawei leads with innovation and resilience.
What do you think? Will Huawei dethrone Nvidia in Asia? Share your views in the comments.
Author Bio
Written by SM Editorial Team, led by Shahed Molla. Our team of expert researchers and writers covers SEO, digital growth, technology, trending news, business insights, lifestyle, health, education, and more, delivering accurate, authoritative, and engaging content for our readers. Read More...
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