1. Global AI Chip Market Landscape Reshaping at Accelerated Pace
On July 30, 2026, research firms IDC and Gartner almost simultaneously released their Q2 2026 global AI chip market tracking reports. Data showed the global AI chip market reached a record $68 billion in the quarter, up 42% year-over-year. Although NVIDIA continued to dominate the AI training GPU market with a 73% share, AMD raised its share to 14% with the MI450 series, and Intel's Gaudi 3 captured 5%. However, the most notable change occurred in the inference chip arena—Chinese manufacturers such as Cambricon and Horizon Robotics together surpassed 20% market share, nearly doubling from the same period last year.
Following this data release, NVIDIA's stock fell slightly by 1.2%, while AMD rose 4.5% and Intel rose 2.3%. The market generally believes that the AI chip competition has expanded from pure training to a full-stack ecosystem of training + inference, and the market landscape could fundamentally change within the next two years.
2. Training GPUs: NVIDIA Defends, AMD Attacks
In the high-end training chip segment, NVIDIA's H200 remains mainstream, but its next-generation architecture Rubin has been delayed to 2027, increasing market acceptance of AMD's MI450 series. The MI450 adopts a chiplet design and unified memory architecture, achieving about 85% of H200 performance in large model training tasks at only 60% of the price, offering significant cost-performance advantages. AMD also released the ROCm 6.0 software stack, greatly lowering migration barriers.
Intel's Gaudi 3 also made breakthroughs; its support for the open-source Triton compiler attracted some small and medium model training users. However, Gaudi 3's stability in ultra-large-scale cluster training still needs verification. NVIDIA, meanwhile, reinforced its ecosystem moat by launching the DGX Cloud subscription service and the CUDA 12.8 update.
3. Inference Chips: Chinese Market Emerges Strongly
Inference is the new focus of GPU competition. With the large-scale deployment of generative AI applications, inference computing demand is expected to surpass training demand by 2027. In Q2 2026, global inference chip shipments reached 3.5 million units, up 55% year-over-year. Chinese manufacturers stood out: Cambricon's Siyuan 590 saw surging sales in the mid-to-low-end inference market, capturing 12% of global share; Horizon Robotics' Journey 6 shipped over 1 million units in the automotive AI field; Hygon's DCU also began batch deployment in server inference.
This is driven by the ongoing decoupling of the US-China tech supply chain. The strengthened US export controls in 2025 forced Chinese AI companies to accelerate substitution with domestic chips, while China achieved mass production at 7nm-class process nodes, providing a capacity foundation for local inference chips. Leading cloud computing providers such as Alibaba Cloud and Tencent Cloud have started large-scale deployment of domestic inference chips for use cases like short video recommendation and intelligent customer service.
4. Blockchain + AI: Rise of Decentralized Computing Power Markets
Beyond traditional chipmaker competition, the most notable trend in Q2 2026 is the deep integration of blockchain and AI. Decentralized computing network projects such as Akash, Render Network, and Flux aggregate idle GPU resources into shared computing pools through token incentives. These computing costs are only 30%-50% of traditional cloud services and are not subject to geopolitical restrictions, attracting an increasing number of small and medium-sized AI development teams.
In July 2026, the DeCompute project on Solana completed a $120 million funding round co-led by a16z and Multicoin, aiming to build the world's first blockchain-native AI training network. Meanwhile, Ethereum layer-2 network Arbitrum launched an AI computing market pilot, supporting developers to pay for inference GPU fees with ETH.
This trend presents both challenges and opportunities for traditional GPU manufacturers. NVIDIA has begun collaborating with decentralized computing platforms to launch compliant GPU leasing solutions. AMD, more aggressively, announced that its MI series will be optimized to support RISC-V and blockchain verification instruction sets.
5. Paradigm Shift in AI Track Investment Logic
From an investment perspective, the changing AI chip landscape means the previous single logic of 'buy NVIDIA' needs revision. Diversified opportunities are emerging:
- Upstream Hardware: Focus on the progress of AMD and Intel catching up with NVIDIA, as well as IPO/funding opportunities for Chinese inference chip leaders.
- Software Ecosystem: The maturity of open-source ecosystems such as ROCm, Triton, and CUDA alternatives will be key variables.
- Decentralized Computing Power: Blockchain AI infrastructure projects (e.g., compute leasing, model verification, privacy computing) may experience explosive growth.
- Tesla's AI Ecosystem: As a vertical domain, Tesla's self-developed chip strategy for autonomous driving and the Dojo supercomputer is worth continuous tracking. Tesla's earnings report shows its AI training cluster has deployed 7,000 H200s but is also testing AMD MI450 as a backup option.
6. Outlook: Three Key Variables in 2027
Based on current trends, the AI chip market in 2027 will revolve around the following variables:
- NVIDIA's Rubin Architecture Mass Production Progress: If delayed, AMD and Intel may further erode its share.
- China's Advanced Process Breakthrough: If successful in achieving self-sufficiency below 7nm, domestic chips will penetrate the training field.
- Blockchain AI Compliance: Regulatory attitudes toward decentralized computing networks in various countries will determine their development potential.
It is foreseeable that the AI track has entered a new phase of 'software-hardware decoupling and multi-polar symbiosis.' Investors need to move beyond dependence on a single giant and capture the dividends of technological paradigm shifts from a more systematic perspective.
