Closing the Gap: Domestic Chips and Large Models Driving China’s AI Advancement
Six Pieces - A curated summary of the latest in Data + AI, to keep you updated about the fast paced Technology Landscape.
China’s AI sector is rapidly advancing on two fronts: self-reliant hardware and globally competitive AI models. Huawei, Alibaba, Tencent, DeepSeek, Zhipu, and ByteDance announced breakthroughs across chips, large-scale LLMs, and multimodal systems—positioning the country’s ecosystem to close benchmark gaps with the U.S. while driving down costs of development and deployment.
1. Huawei: Ascend 910C Chip and Roadmap
Huawei began shipping its Ascend 910C chip to major firms like Baidu and ByteDance for testing. Claimed to match Nvidia’s H100 in performance, this domestic GPU breakthrough supports self-reliant AI infrastructure, with initial deliveries enabling faster training of large models on Chinese hardware. Huawei also announced its 2025-2028 Ascend roadmap, incorporating self-developed HBM memory for 2 PFLOPS per chip to ensure supply-chain independence, with clusters matching NVIDIA’s aggregate power despite single-chip gaps.
2. Alibaba: Qwen3-Max Model Leadership
Building on this hardware foundation, Alibaba made headlines with its Qwen3-Max, a 1T+ parameter model that topped Hugging Face rankings. Backed by a ¥380B AI infrastructure investment, it surpasses Llama 3.1 in math and coding tasks, supports 29+ languages, and offers a massive 2M-token context window. The Qwen3-Max-Instruct variant targets coding, instruction-following, and agent applications, underpinning rapid enterprise adoption and closing the U.S.-China benchmark gap.
3. Tencent: Hunyuan Image 3.0 Multimodal Model
Meanwhile, Tencent contributed advancements on the multimodal front by releasing Hunyuan Image 3.0, an 80-billion-parameter Mixture-of-Experts model. Unlike standard diffusion models, it uses a unified autoregressive framework to tightly fuse text and image generation, excelling in photorealism and creative control while broadening accessibility for AI content creation across industries.
4. DeepSeek: V3.2-Exp Efficiency Upgrade
Efficiency and cost-effectiveness received attention as well, with DeepSeek unveiling its V3.2-Exp intermediate model. Building on prior versions, it introduces DeepSeek Sparse Attention to enhance long-context processing while halving API costs. Optimized for Chinese native chips and supporting CUDA cross-compatibility, it positions itself as a flexible, cost-competitive alternative in the LLM market.
5. Zhipu AI: GLM-4.6 and Claude Migration Plan
Zhipu AI also advanced with GLM-4.6, enhancing long-context reasoning and agent workflows while supporting massive input and output token windows. Available via API and open weights for local deployment, Zhipu’s release comes with a migration plan targeting Anthropic Claude users—offering a lower-cost, higher-usage alternative designed to attract a large user base.
6. ByteDance: Doubao 1.6-Vision Multimodal Launch
Finally, ByteDance’s cloud and AI unit Volcengine launched Doubao 1.6-Vision, a multimodal model introducing tool-calling for complex visual tasks. It delivers advanced visual reasoning and image operation capabilities like cropping and annotation, while slashing deployment costs nearly in half compared to its predecessor, enabling broader affordability and scalability for visual AI services.
Taken together, these announcements show a coordinated acceleration of China’s AI ecosystem—expanding from hardware foundations to frontier models and cost-efficient applications. The focus on domestic chip capability, long-context LLMs, multimodal systems, and accessible pricing signals more than just catch-up; it highlights a maturing ecosystem increasingly able to set competitive benchmarks on its own terms.