Explore the in-depth comparison of Huawei Ascend 910C vs Nvidia H100 AI chips, analyzing performance, efficiency, and architecture for AI workloads in 2025.
In the rapidly evolving world of artificial intelligence, the Huawei Ascend 910C emerges as a pivotal player, an AI accelerator chip engineered for high-performance computing, focusing on demanding AI tasks like training and inference. In 2025, its significance is amplified by geopolitical tensions, particularly U.S. export controls that restrict China’s access to advanced chips like NVIDIA’s H20 and H100 (as reported by Reuters). These restrictions have spurred China’s drive for technological self-sufficiency, positioning the Ascend 910C as a cornerstone of its AI ambitions and a direct AI GPU alternative to NVIDIA within the nation.
Developed by Huawei's HiSilicon, the Ascend 910C challenges the dominance of Western companies like NVIDIA, offering a domestic solution for Chinese tech giants and their burgeoning AI industry. This article delves into the Ascend 910C’s technical prowess, provides an AI chip comparison 2025 against leading GPUs, explores its real-world applications, and evaluates its strategic implications, providing a comprehensive guide for tech professionals and enthusiasts navigating the complex AI hardware landscape. We'll also touch upon its potential as a Chinese AI chip for deep learning hardware stacks.
The Ascend 910C, developed by Huawei’s HiSilicon, is the latest iteration in the Ascend series, which began with the Ascend 310 for edge computing and the original Ascend 910 for data centers (Unite.AI notes its challenge to NVIDIA). While the exact public announcement date remains somewhat unspecified, mass shipments reportedly began in May 2025, following extensive testing with major Chinese firms such as Baidu, ByteDance, and China Mobile.
This AI accelerator chip targets data centers, AI model developers, and large technology companies, particularly within China. Here, it aims to address the critical void left by restricted access to high-end NVIDIA GPUs. Its strategic positioning is vital for bolstering Huawei's ecosystem and aligns directly with China's overarching goal of establishing a self-reliant AI infrastructure, making it a strategically important Chinese AI chip.
The Ascend 910C is engineered for high-performance AI workloads, leveraging advanced specifications. Below is a detailed breakdown:
| Specification | Details |
|---|---|
| Architecture | Da Vinci, chiplets (likely dual Ascend 910B dies) |
| Manufacturing Process | SMIC 7nm N+2 |
| FP16 Performance | 800 TFLOPS (some sources report 320 TFLOPS) |
| INT8 Performance | ~1600 TOPS (estimated) |
| BF16 Performance | ~781 TFLOPS (derived from CloudMatrix 384) |
| Memory | 128GB HBM3 |
| Memory Bandwidth | 3.2 TB/s |
| Power Consumption | ~310W (potentially higher for dual-die) |
| Software Ecosystem | CANN (Compute Architecture for Neural Networks), MindSpore, PyTorch, TensorFlow |
The Huawei Ascend 910C enters a competitive field dominated by NVIDIA’s H100 and AMD’s MI300X. Here’s how this AI GPU alternative to NVIDIA stacks up in this AI chip comparison for 2025:
| Specification | Huawei Ascend 910C | NVIDIA H100 (SXM5) | AMD MI300X |
|---|---|---|---|
| FP16 Performance | 800 TFLOPS | 989 TFLOPS (Sparsity: 1979) | 1307.4 TFLOPS (Sparsity: 2614.8) |
| INT8 Performance | ~1600 TOPS | ~1979 TOPS (Sparsity: 3958) | 2614.9 TOPS (Sparsity: 5229.8) |
| Memory | 128GB HBM3 | 80GB HBM3 | 192GB HBM3e |
| Memory Bandwidth | 3.2 TB/s | 3.35 TB/s | 5.3 TB/s |
| Power Consumption (TDP) | ~310W (potentially higher) | Up to 700W | 750W |
| Software Ecosystem | CANN, MindSpore, PyTorch, TensorFlow | CUDA, cuDNN, TensorRT | ROCm, HIP |
Note: NVIDIA and AMD often quote performance with sparsity features; dense compute figures are used for a more direct comparison where possible.
Its 128GB HBM3 memory is a strong point, surpassing the H100's 80GB, though less than the MI300X's 192GB. This is critical for large AI models. Compute performance, while substantial, generally lags behind both NVIDIA and AMD's flagship offerings in raw TFLOPS. However, if the ~310W power consumption figure holds true for the entire package, its performance-per-watt could be very competitive, a crucial factor for data center operational costs. The CANN framework's support for PyTorch and TensorFlow significantly broadens its appeal beyond MindSpore, but the ecosystem is less mature than NVIDIA's CUDA. Tom's Hardware reports on research suggesting it delivers 60% of H100 inference performance for some models.
The NVIDIA H100 leads in many compute benchmarks and benefits immensely from CUDA's robust and mature ecosystem, which is the de facto standard in AI development. Its 80GB HBM3 memory, while fast, can be a limitation for the largest AI models, sometimes requiring more complex model parallelism. NVIDIA's strength also lies in its extensive software libraries and tools optimized for its hardware.
The AMD MI300X offers the highest raw compute performance and the largest memory capacity (192GB HBM3e) with superior bandwidth among the three. This makes it very attractive for memory-bound AI workloads. However, its power consumption is the highest, and its ROCm software ecosystem, while rapidly improving and offering HIP for CUDA code porting, is still developing and has less widespread adoption than CUDA.
The Ascend 910C may find a niche by excelling in power efficiency (performance-per-watt), which is a critical metric for large-scale data centers managing operational costs. If its ~310W power draw is accurate for the dual-die package, it could offer significant TCO advantages, even if its raw performance is outpaced by the H100 and MI300X, which consume significantly more power.
NVIDIA’s CUDA remains the gold standard due to its maturity, extensive toolset, and widespread community support. AMD’s ROCm is a growing alternative, gaining traction with its open-source approach. Huawei’s CANN framework, while promising and crucial for supporting PyTorch and TensorFlow, is still primarily China-focused and building its global developer community. This ecosystem factor is often as important as raw hardware specs.
The Ascend 910C is rapidly gaining traction within China’s burgeoning AI ecosystem, driven by the urgent need for powerful domestic solutions amidst U.S. export restrictions.
Major Chinese technology companies like Baidu, ByteDance (owner of TikTok), and China Mobile are reportedly testing or actively deploying the Ascend 910C. This adoption is crucial for developing and running their AI services, from search algorithms and recommendation engines to cloud computing and large language models (ExtremeTech on early adoption). This positions the Ascend 910C as a vital piece of deep learning hardware for these enterprises.
The chip is already powering sophisticated AI models. A notable example is its use with DeepSeek R1, a cost-effective Chinese AI reasoning model. The Ascend 910C's architecture and memory capacity are well-suited for such models, showcasing strong inference capabilities (Huawei Central on DeepSeek R1 and TrendForce analysis).
Independent research and testing by entities like DeepSeek suggest that the Ascend 910C can deliver approximately 60% of the NVIDIA H100’s inference performance in certain tasks. This makes it a viable option for demanding applications like natural language processing (NLP), computer vision, and recommendation systems. However, its performance and reliability for large-scale AI training, while capable, are areas where it is still being refined compared to established NVIDIA solutions (Tom's Hardware on inference performance).
Huawei has also demonstrated the Ascend 910C's scalability through systems like the CloudMatrix 384. This AI supercomputing cluster, comprising 384 Ascend 910C chips, reportedly achieves 300 PFLOPS of BF16 compute performance. This showcases the chip's prowess in large-scale data center deployments, essential for training foundational AI models and supporting extensive AI cloud servers (SemiAnalysis on CloudMatrix 384 and TweakTown comparison).
The Huawei Ascend 910C is not just a piece of technology; it's a significant development in the broader context of global tech competition and China’s strategic AI ambitions.
One of the most immediate impacts of the Ascend 910C is its role in reducing China's heavy reliance on NVIDIA GPUs. With U.S. export controls limiting access to NVIDIA's most advanced chips (and even their China-specific variants like the H20), the Ascend 910C offers a domestically produced, viable AI GPU alternative to NVIDIA for Chinese companies (Reuters on seeking NVIDIA alternatives). This is crucial for maintaining momentum in China's AI development.
The Ascend 910C's production using SMIC’s 7nm N+2 process is a testament to China's progress in its domestic semiconductor supply chain. This advancement is critical for achieving "AI sovereignty"—the ability to develop and deploy AI technologies independent of foreign restrictions. While challenges such as manufacturing yield rates persist (initially reported low around 20% by TrendForce, but later reports suggest significant improvement to around 40% or even comparable to H100 according to WCCFTech), the capability itself is a strategic win for China.
U.S. sanctions, ironically, appear to have accelerated Huawei’s innovation in chip design and spurred investment in China's domestic semiconductor industry. The Ascend 910C is a direct result of these pressures. However, reliance on potentially stockpiled TSMC-produced components for earlier chip versions or initial production runs, and the ongoing hurdles in mass-producing cutting-edge chips domestically at high yields, remain significant challenges (CSET Georgetown on export controls impact). The move to SMIC's 7nm process, as reported by TrendForce about sampling, is a key step in mitigating these dependencies.
The development of the Ascend 910C and associated systems like CloudMatrix 384 fosters robust local alliances. Partnerships between Huawei and Chinese AI firms like DeepSeek, as well as collaborations with cloud service providers, strengthen China's entire AI ecosystem. This internal synergy enhances China's overall competitiveness in the global AI landscape, even if its hardware solutions are primarily adopted domestically for now.
The Huawei Ascend 910C stands as a formidable AI accelerator chip, reportedly delivering up to 800 TFLOPS of FP16 performance, a substantial 128GB of HBM3 memory, and broad software compatibility with frameworks like PyTorch and TensorFlow via its CANN ecosystem. While it may trail behind the absolute peak compute power of NVIDIA’s H100 and AMD’s MI300X, its impressive memory capacity and potential for superior power efficiency make it a strong and strategically vital contender, particularly within the Chinese market.
The Ascend 910C directly challenges NVIDIA’s dominance within China, providing a much-needed domestic alternative in a sanction-constrained environment. However, its global penetration faces significant limitations due to the maturity of competing ecosystems like CUDA and geopolitical barriers. For Chief Technology Officers (CTOs) and AI architects in regions where Huawei operates or where access to Western chips is restricted, the Ascend 910C offers a potentially cost-effective and capable solution, especially for inference-heavy workloads and large model deployment.
Ultimately, the Ascend 910C’s role in bolstering China’s AI self-sufficiency underscores its profound strategic importance. Its journey towards widespread global adoption will likely depend on further advancements in performance, ecosystem development, and navigating the complex geopolitical landscape. It is a clear signal of China's determination to be a leader in AI hardware, irrespective of external pressures. For insights on selecting the best GPUs for LLMs, consider the Ascend 910C as a relevant option in specific contexts.
The Huawei Ascend 910C is an advanced AI accelerator chip developed by Huawei’s HiSilicon for high-performance computing, particularly for AI training and inference tasks. It's a key component in China's strategy for technological self-sufficiency in AI, competing with GPUs from NVIDIA and AMD.
The Ascend 910C offers more HBM3 memory (128GB vs. H100's 80GB) which is beneficial for very large AI models. However, the NVIDIA H100 generally leads in raw compute performance and benefits from the highly mature CUDA ecosystem. The 910C aims to be competitive, especially in power efficiency and for specific workloads within China, but the H100 is often considered superior in overall performance and ecosystem support globally.
The Ascend 910C is believed to combine two Ascend 910B dies using chiplet technology (as rumored). This configuration aims to significantly enhance performance by effectively doubling resources and addressing potential interconnect bottlenecks present in earlier designs.
The Ascend 910C supports Huawei’s native MindSpore framework. Crucially, it also offers compatibility with popular frameworks like PyTorch and TensorFlow through Huawei’s Compute Architecture for Neural Networks (CANN) framework, which facilitates integration into existing AI development workflows.
Yes, the Ascend 910C is designed for AI training, including LLMs, thanks to its 128GB HBM3 memory and significant FP16/BF16 compute capabilities. While it may show strong inference performance (around 60% of H100 for models like DeepSeek R1), training reliability for very large, complex models is an area under continued development compared to established players like NVIDIA.
The Ascend 910C is manufactured by SMIC (Semiconductor Manufacturing International Corporation), China's largest foundry, using their 7nm N+2 process. This is a significant achievement for China's domestic semiconductor industry.
The primary market for the Ascend 910C is China. Due to U.S. export restrictions on advanced AI chips, there is strong demand within China for domestically produced alternatives to power its AI development and data centers.
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