--- title: "Tech Giants Collectively Bet on Self-Developed Chips as AI Chip Battlefield Accelerates Shift to Inference End" type: "News" locale: "en" url: "https://longbridge.com/en/news/281893355.md" description: "Tech giants are increasing their investments in self-developed AI chips, and the AI chip market is shifting from model training to inference. This transition will impact the business models and investment logic of the semiconductor industry. Companies like OpenAI and Meta are facing computational bottlenecks. OpenAI plans to mass-produce its own chips by 2026 to reduce reliance on NVIDIA. AI inference is becoming a critical pillar of data centers and cloud infrastructure, and the investment model for inference chips differs significantly from training chips, implying a continuous revenue consumption model" datetime: "2026-04-07T13:48:47.000Z" locales: - [zh-CN](https://longbridge.com/zh-CN/news/281893355.md) - [en](https://longbridge.com/en/news/281893355.md) - [zh-HK](https://longbridge.com/zh-HK/news/281893355.md) --- > Supported Languages: [简体中文](https://longbridge.com/zh-CN/news/281893355.md) | [繁體中文](https://longbridge.com/zh-HK/news/281893355.md) # Tech Giants Collectively Bet on Self-Developed Chips as AI Chip Battlefield Accelerates Shift to Inference End The explosive popularity of generative AI is reshaping the competitive landscape of the entire semiconductor industry. The core battlefield of the AI chip market is undergoing a structural migration from model training to inference – this shift not only concerns chip design priorities but will also profoundly influence infrastructure investment logic, business models, and the long-term trajectory of the semiconductor supply chain. There are clear signals of surging inference demand. The outbreak of viral application scenarios, such as Ghibli-style image generation, has led to OpenAI's GPU resources being completely saturated. OpenAI CEO Sam Altman publicly stated that he has never seen such rapid usage growth, forcing a phased release of GPT-4.5, initially only available to paid users. Leading AI companies like Meta are also facing similar computational bottlenecks. Concurrently, OpenAI is independently developing AI chips, aiming for mass production around 2026 to reduce its reliance on NVIDIA. Its "Stargate" super data center project, jointly promoted with Microsoft, is reportedly involved in an investment scale of up to $500 billion. This series of developments indicates that AI inference is becoming a strategic pillar alongside data centers, cloud infrastructure, and semiconductors. For investors, this means **the value focus of AI computing power investment is shifting: training chips represent one-time capital expenditures, while inference chips correspond to a continuous revenue consumption model – AI is evolving from a technical tool into a pay-per-use computing engine.** ## Training vs. Inference: Two Fundamentally Different Computing Demands To understand this structural shift, we must first clarify the fundamental differences in workload between training and inference. The training phase, based on the Transformer architecture released by Google in 2017, requires forward and backward propagation on massive datasets to continuously update model weights. This involves extremely large-scale matrix operations, gradient calculations, and parameter updates, typically requiring weeks or even months of distributed computing on multi-GPU or TPU clusters. Training chips therefore must possess high-density computing cores, large-capacity high-bandwidth memory (like HBM), and the ability to scale horizontally across multiple chips. The inference phase is structurally simpler: it only requires forward propagation, without the need for gradient updates or backpropagation. The required computing power is usually an order of magnitude lower than for training. However, the real challenge of inference lies in three constraints – low latency (users expect immediate responses), high throughput (service providers must handle massive concurrent queries), and low cost (the unit cost per query directly impacts commercial viability). These demands are contrary to the training phase's logic of "disregarding latency and pursuing ultimate performance," and also dictate that inference chips must follow a path of architectural differentiation: prioritizing power efficiency, optimizing data movement, maximizing memory hierarchy and bandwidth utilization, and synergistic optimization of hardware and software. ## Hyperscalers and Startups Accelerate Inference Chip Deployment Based on these architectural differences, an increasing number of companies are choosing to bypass NVIDIA's direct competition in the training GPU market and instead build custom chips optimized for inference. On the hyperscaler front, Google has introduced TPUs (training) and Edge TPUs (edge inference), Amazon deploys Inferentia and Trainium, and Meta has developed MTIA (Meta Training and Inference Accelerator). The startup camp is equally active, with companies like Groq, Tenstorrent, Cerebras, and SambaNova seeking differentiated breakthroughs in data flow architecture, chip area allocation, power efficiency, memory access patterns, and compute core design, directly targeting superior inference efficiency and cost structures compared to general-purpose GPUs. The formation of this competitive landscape is closely related to the evolution of AI application scenarios. As AI evolves from simple question-answering to agentic AI systems – capable of planning tasks, executing workflows, calling tools, and even replacing some human labor – inference demand will not only continue to grow but will also expand rapidly. The demands of agentic systems for low latency, high memory bandwidth, and sustained computing power will further enhance the strategic value of inference-specific chips. ## NVIDIA: Transitioning from Training Era Leader to Inference Era Rule-Maker Facing this structural shift, NVIDIA is not passively responding but actively expanding its footprint in the inference market. The core design objective of its latest Blackwell architecture is to reduce the cost per token generated while increasing throughput. This logic forms a virtuous cycle: lower cost → increased usage → expanded demand → increased infrastructure scale, thereby driving the exponential growth of the AI economy. At the system level, NVIDIA is building "AI factory" architectures through tightly integrated GPU clusters like NVL72, capable of handling longer context windows, more complex inference tasks, and multi-step AI workflows, pushing AI infrastructure towards centralization, high density, and system-driven evolution. However, NVIDIA's true moat lies not solely in its hardware. From CUDA to TensorRT-LLM and its inference optimization software stack, NVIDIA is transforming itself from a chip supplier into a full-stack AI infrastructure provider. The continued adoption of this architecture by cloud service providers such as Microsoft, Oracle, and CoreWeave further strengthens the high switching costs and industry standardization effect of its ecosystem. Customers are no longer just buying GPUs, but an entire AI factory platform. Nevertheless, the intensity of competition in the inference market is significantly increasing. Inference chips are no longer a secondary option to training GPUs but are becoming the primary computing engines for AI cloud services, edge devices, embedded systems, and real-time applications. Driven by both hardware evolution and application expansion, the core proposition of AI chip competition is fundamentally changing: from "who can train the largest model" to "who can run models at scale with the highest efficiency." ## Structural Shift Reshapes Semiconductor Industry Competition This migration from training to inference has impacts that extend beyond chip design itself, deeply penetrating into AI system architecture, commercial deployment strategies, and supply chain structures. At the business model level, the economic logic of AI is undergoing a fundamental restructuring. Training corresponds to capital expenditure, while inference corresponds to continuous revenue – computing power is directly linked to revenue, and GPUs are evolving from hardware devices into token generation machines. This paradigm shift implies that the scale and efficiency of inference infrastructure will directly determine the profitability and competitive moat of AI companies. At the supply chain level, the rise of the post-training era – including the widespread application of techniques like fine-tuning, LoRA, and adapters, as well as inference enhancement methods such as dynamic prompt structure adjustment and multi-model collaboration – is significantly increasing the reliance on inference computing power, driving rapid expansion of demand for diversified inference hardware such as NPUs, ASICs, and FPGAs. For investors, this structural shift signals a clear market trend: the value focus of AI infrastructure investment is migrating from the training end to the inference end. Companies that can achieve advantages in inference efficiency, cost control, and large-scale deployment simultaneously will hold a dominant position in the next phase of AI computing power competition. Risk Disclosure and Disclaimer Markets are subject to risk; investment requires caution. This article does not constitute personal investment advice, nor does it consider the specific investment objectives, financial situation, or needs of individual users. Users should consider whether any opinion, view, or conclusion in this article is consistent with their specific circumstances. Investment based on this is at your own risk. ### Related Stocks - [T-REX 2X Long NVIDIA Daily Target ETF (NVDX.US)](https://longbridge.com/en/quote/NVDX.US.md) - [GraniteShares 2x Long NVDA Daily ETF (NVDL.US)](https://longbridge.com/en/quote/NVDL.US.md) - [Meta Platforms, Inc. 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