--- title: "The Undervalued New NVIDIA Narrative: \"Proprietary Open-Source Models\" and \"Supply Chain Lock-In\"" type: "News" locale: "en" url: "https://longbridge.com/en/news/296703677.md" description: "HSBC believes that NVIDIA is heavily betting on open-source small language models (SLMs), guiding millions of developers to run AI applications on its hardware through a free, optimized ecosystem, thereby expanding its customer base from a few cloud giants to a broader ecosystem. Additionally, by systematically locking in production capacity through multi-year agreements, it places competitors at a supply chain disadvantage. These two narrative threads are expected to become key catalysts driving a re-rating of its valuation" datetime: "2026-08-23T12:57:35.000Z" locales: - [zh-CN](https://longbridge.com/zh-CN/news/296703677.md) - [en](https://longbridge.com/en/news/296703677.md) - [zh-HK](https://longbridge.com/zh-HK/news/296703677.md) generator: "portal-rs" --- # The Undervalued New NVIDIA Narrative: "Proprietary Open-Source Models" and "Supply Chain Lock-In" NVIDIA is quietly building moats on two fronts that the market has rarely fully priced in. According to HSBC's latest research report released on August 20, HSBC analyst Frank Lee believes that beyond the continuous "earnings beats," the new catalysts driving a re-rating of NVIDIA's stock price will come from two narratives previously overlooked by the market. **First, a heavy bet on open-source small language models (SLMs) is expected to expand the potential customer base from a few hyperscale cloud providers to millions of developers and sovereign nations; second, by locking in supply chain capacity in advance through a series of multi-year procurement agreements, this advantage will become increasingly prominent as competitors find it harder to obtain key manufacturing resources.** The market significance of these two new narratives lies in the fact that they both point to the same core proposition: NVIDIA's growth engine is evolving from a single structure dependent on a few hyperscale customers to a broader, more resilient customer ecosystem. If this shift gains market recognition, it will be a key variable driving a valuation re-rating. ## The Open-Source Model Offensive: From "Selling Shovels" to "Making Shovels" The HSBC report points out that after the hype around sovereign AI and emerging cloud providers (neoclouds) faded, NVIDIA failed to form a new narrative sufficient to drive a significant re-rating of its stock price, which is one of the important reasons for its underperformance against the Philadelphia Semiconductor Index year-to-date. **And the strategic bet on open-source AI is filling this void.** NVIDIA is currently actively laying out its position in the open-source model space, positioning itself as the world's largest contributor to open-source AI. According to NVIDIA's disclosure, the cumulative scale of open-source models has become the second most popular category by token generation volume. **HSBC believes that the strategic significance of this layout is that open-source small language models (SLMs) are becoming the preferred inference engine for intelligent agents and on-device applications.** There are three logical reasons behind the favor for SLMs: > - Latency and throughput advantages—Autonomous agents need to operate frequently in execution loops such as parsing intent, calling APIs, and evaluating results. Calling large frontier language models (LLMs) for every micro-step will create severe latency bottlenecks, while SLMs have significant advantages in inference cost and throughput; > - Task-specific intelligence—SLMs excel at constrained, deterministic tasks. Enterprises are embedding domain-specific SLMs into software platforms to solve complex business problems; > - Edge deployment capability—Models with fewer than 10 billion parameters can easily fit into local GPU memory or edge devices, supporting localized operation. NVIDIA's open-source product matrix is already quite complete, covering multiple core scenarios: > - The Nemotron series focuses on inference and language tasks; > - Cosmos targets "physical AI" in the fields of robotics and vision; > - GR00T N1 is positioned as the world's first open general-purpose foundation model for humanoid robots; > - Alpamayo focuses on autonomous driving scenarios; > - NVIDIA Agent Toolkit and NeMo are used respectively for building and customizing enterprise-grade AI agents and generative AI applications. HSBC points out that this free, highly optimized open-source ecosystem is essentially a strategic lever for NVIDIA to guide developers to prioritize running AI applications on its hardware. ## Supply Chain Positioning: Locking in Capacity in Advance to Build Competitive Barriers The HSBC report points out that AI computing power demand continues to exceed supply under capacity constraints. NVIDIA has systematically locked in key supply capacity in advanced packaging, memory, optical devices, and energy infrastructure through a series of multi-year agreements by 2026. **HSBC judges that supply chain constraints in multiple segments may further intensify in 2027. NVIDIA's advance procurement strategy will then generate competitive value far exceeding that of its peers and is expected to command a higher premium in the market.** According to The Information, **in terms of advanced packaging and memory**, NVIDIA signed a $1.5 billion multi-year agreement with Amkor Technology in July 2026 to support its expansion of advanced semiconductor packaging and testing capacity in Arizona, USA; In the same month, it reached a comprehensive cooperation agreement with the SK Group worth up to $500 billion, covering joint research and development of next-generation AI memory (including HBM) with SK Hynix, as well as plans to build a 2-gigawatt Vera Rubin AI factory in South Korea. **In terms of foundry capacity**, NVIDIA has booked 63% (for 2026) and 52% (for 2027) of TSMC's CoWoS-L advanced packaging capacity. This forces GPU and ASIC competitors to turn to other suppliers for alternatives, while adopting alternative suppliers faces potential risks in yield rates. **In terms of optical interconnects**, as AI network infrastructure evolves from copper cables to optical interconnects, NVIDIA has signed multi-year strategic agreements with Lumentum and Coherent respectively, each investing $2 billion to support R&D and domestic manufacturing capacity construction in the US, and gaining access to future capacity for advanced laser components; At the same time, it signed a multi-year commercial and technical cooperation agreement with Corning to expand the manufacturing scale of advanced optical connectivity solutions in the US. **In terms of energy and land, NVIDIA is locking in key assets by directly taking equity stakes in infrastructure developers.** According to Bloomberg, NVIDIA has successively taken stakes in Cloverleaf Infrastructure, Lancium, and SB Energy, binding electricity resources to ensure that its chips have "places to run" in the future, and thereby embedding NVIDIA's full suite of hardware and software stacks into the early design stages of related facilities. Among them, NVIDIA announced that through cooperation with SB Energy and OpenAI, it completed the locking of land, electricity, and building capacity at the PORTS-Pike technology park in Ohio. The initial design supports 4.25 IT-GW AI factory capacity. NVIDIA's cumulative payment obligation cap for this is $105 billion, with an additional $1.5 billion investment in SB Energy. In addition, NVIDIA plans to invest $1 billion in NAVER to expand the "GAK Sejong" AI factory from 55 megawatts to 200 megawatts by 2028, with long-term plans to move towards 1 gigawatt of sovereign AI infrastructure. **From a competitive landscape perspective, such investments are essentially a bundling strategy.** Cloud computing giants and AI laboratories usually mix and match multiple suppliers when purchasing chips, network equipment, and custom cables. 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