DeepSeek-V4 Ignites China's AI Chip Sector: Domestic LLMs Drive Revaluation of Compute Hardware
DeepSeek-V4 Ignites the AI Chip Value Chain: Technical Breakthroughs Reshape the Valuation Framework for Compute Hardware
On the morning of April 24, A-share markets faced broad pressure—China’s ChiNext Index plunged 2.20% in the first half of trading, and over 3,800 stocks across the Shanghai and Shenzhen exchanges declined, signaling markedly cautious investor sentiment. Yet amid this widespread downturn, one sector surged against the tide: Hua Hong Semiconductor soared over 12% in a single day; SMIC rose nearly 9%; and chip design and distribution firms—including Fullhan Microelectronics, Shenzhen Huaqiang, and Zongyi Co.—all hit daily trading limits. This counter-cyclical strength was not driven by short-term capital speculation or policy rhetoric—but by a clear signal emanating from the open-source preview release of DeepSeek-V4: China’s large language models (LLMs) are transitioning decisively from “functional” to “truly usable,” and their underlying technological leap is materially reshaping the upgrade roadmap for compute infrastructure. This marks a fundamental shift in investment logic across the AI value chain—from the prior “concept-driven” model, reliant on overseas LLMs as catalysts, to a new era of “technology realization,” powered by autonomous, homegrown model evolution.
Performance Leap: V4 Is Not an Iteration—It’s a Paradigm Shift
Although still an early preview version, DeepSeek-V4 has already sent shockwaves through the industry. Public user feedback highlights its marked superiority over current mainstream open-source models in key dimensions: agent task completion rates, multi-step reasoning stability, and depth of long-context understanding. More critically, the official release emphasizes that V4 achieves significantly reduced memory (VRAM) and compute requirements—delivering superior bandwidth efficiency for High Bandwidth Memory (HBM) and higher GPU core utilization at equivalent performance levels. In other words, V4 is not merely about scaling up parameters; rather, through innovative architectural design—including dynamic sparse activation and mixed-precision KV cache compression—as well as highly efficient training paradigms, it delivers a qualitative leap in compute-output efficiency per unit of hardware resource. Notably, users have rated its overall experience as “surpassing Anthropic’s Sonnet 4.5”—a recently launched high-performance closed-source model widely regarded as an unattainable benchmark for the open-source ecosystem. This comparison carries profound symbolic weight: for the first time, a Chinese-developed model has achieved localized superiority over a leading international closed-source counterpart—measured not in abstract benchmarks, but in real-world usability.
This technical breakthrough directly reshapes hardware demand structures. The traditional “brute-force compute procurement” logic—i.e., indiscriminately adding more GPUs—is now being empirically invalidated. Market attention is rapidly pivoting toward true bottlenecks that constrain model performance: High Bandwidth Memory (HBM), advanced packaging technologies (e.g., CoWoS), low-power AI acceleration IP cores, and specialty process nodes on mature nodes. Hua Hong Semiconductor’s global leadership in specialized process platforms—including 55/40nm BCD and 90nm RFSOI—alongside SMIC’s scale advantages and yield excellence in 28nm and above mature nodes, align precisely with the urgent chip requirements of V4-class models: high reliability, ultra-low latency, and robust I/O capability. The sharp stock price moves are not speculative froth—they reflect the industry’s value re-rating of companies capable of co-optimizing chips and models.
From “Concept-Driven” to “Technology-Realized”: A Fundamental Shift in Chip-Chain Valuation Anchors
Historically, domestic AI chip-related stocks traded largely on thematic momentum tied to the “LLM boom,” with valuations lacking solid earnings foundations—resulting in volatile, short-lived rallies. In contrast, post-V4, fund flows exhibit structural divergence: On one hand, CPO optical module leader New Bright Optoelectronics plunged over 10% in a single day—reflecting cooling market expectations for pure “data-pipe” hardware. On the other, companies with demonstrable chip design capabilities (e.g., Fullhan’s ISP + AI co-processor), advanced packaging moats (e.g., Tongfu Microelectronics’ Chiplet mass-production capacity), and differentiated foundry strengths received concentrated institutional buying. This signals clearly: capital is abandoning vague “AI+” labels and instead anchoring valuation on verifiable technical chokepoints and proven volume production capability.
The recent regulatory adjustment raising the single-stock holding limit for public mutual funds—from 10% to 25%—further accelerates this trend. This change is not merely about boosting liquidity; its deeper intent is to encourage public funds to adopt long-term, industrially grounded portfolio allocations—rather than short-term tactical trading. When institutions gain the flexibility to allocate up to 25% of their portfolios to semiconductors with genuine technical barriers and deep customer integration, the “domestic substitution” narrative shifts from macro-level slogan to micro-level portfolio reality. The surges in Hua Hong and SMIC stocks represent the market’s explicit vote of confidence in one critical question: Can these firms reliably supply customized chips optimized specifically for the V4 architecture?
Dual Catalysts Converging: Self-Reliance and AI Industrialization Enter the “Deep Water” Phase
V4’s technical breakthrough is, in essence, a microcosm of accelerating AI industrialization in China; the chip sector’s outperformance, meanwhile, signals that semiconductor self-reliance has entered a new phase—“application-driven feedback.” Together, they form the most robust dual-pillar investment thesis for tech equities today:
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Self-reliance no longer means merely “being able to make it”—it demands “making it better.”
When domestic models demonstrably surpass select international peers in real-world experience, downstream application vendors’ willingness to procure domestic chips evolves from “political correctness” to commercial rationality. This will significantly alleviate market pressure on SMIC and Hua Hong amid constraints on cutting-edge nodes—and instead unlock a second growth curve via extreme optimization of mature nodes and system-level co-design. -
AI industrialization no longer depends on “feeding” applications with imported models—it is now spawning indigenous compute infrastructure standards.
V4’s streamlined VRAM and compute requirements may accelerate commercialization of complementary domestic technologies: HBM2e/HBM3, compute-in-memory chips, and RISC-V-based AI acceleration instruction sets. A vertically integrated ecosystem—defined by domestic models, powered by domestic chips, and orchestrated by domestic software stacks—is moving from blueprint to reality.
Conclusion: Valuation Re-rating Is Just the Beginning—Ecosystem Building Is the Endgame
The chip-chain rally triggered by DeepSeek-V4 appears, on the surface, to be event-driven. In reality, it reveals an irreversible trend: China’s AI industry is breaking free from passive dependence on overseas technology pathways—and entering a new cycle where indigenous innovation defines standards, and real-world applications actively shape hardware development. Short-term price volatility remains inevitable—but the valuation center of gravity for firms like Hua Hong and SMIC has already quietly shifted: from “global semiconductor cycle beta” to “China’s AI industrialization alpha.”
The true challenge lies ahead:
How do we convert this breakthrough into sustained, iterative advancement?
How do chipmakers and model developers build deep, joint-optimization mechanisms?
How do we construct a resilient, multi-path supply chain—one not beholden to any single technology stack?
The answers do not lie in the next viral model—but in every successfully taped-out chip, in every late-night joint debugging session with LLM engineering teams, and in every hardware compatibility whitepaper openly published for developers. Once “technology realization” becomes market consensus, China’s AI hard-tech valuation re-rating has only just begun.