New Stock Analysis | Denglin Technology: Revenue Exceeds RMB200 Million in 4 Months, Is the Inflection Point Ahead of the Cost Curve?

For a long time, the market's impression of domestic general-purpose computing power chips remained stuck at "the technology works, but commercialization is yet to be proven." But as Denglin Technology formally knocks on the door of a Hong Kong Stock Exchange IPO, a host of domestic computing power chip manufacturers are now delivering real evidence of technology industrialization through shipments, revenue, and cash flow. On October 25, according to HKEX disclosures, Suzhou Denglin Technology Co., Ltd. (hereinafter "Denglin Technology") passed the HKEX main board listing hearing, with CITIC SEC, China Securities Co.,Ltd. International, and BOC International as joint sponsors. The company officially submitted its 18C specialist technology IPO application to HKEX on February 27, and after more than seven months finally reached the threshold of HKEX. Founded in 2017, the company is a general-purpose graphics processing unit (GPGPU) provider that develops and sells GPGPU products to meet AI computing needs across China's myriad industries. According to Frost & Sullivan, based on 2025 revenue, Denglin Technology is the third-largest company in China's enterprise AI GPGPU market, with a 1.0% market share. Next, let us step beyond the pure "technology myth" narrative and examine, from the two dimensions of operating performance and industry landscape, the true quality of this heterogeneous GPGPU manufacturer and the domestic computing power growth opportunity it represents. Revenue exceeds RMB200 million in 4 months, operating leverage begins to release According to the prospectus, Denglin Technology focuses primarily on the development and sale of hardware products (especially GPGPU boards), supplying original equipment manufacturers (OEMs), system integrators, and AI solution providers. The company operates on a fabless business model, the mainstream business model for companies in the semiconductor industry focused on chip innovation and architectural advancement. This model allows the company to concentrate resources on the design and development of energy-efficient GPGPUs while leveraging the expertise of third-party partners for manufacturing. For fabless chip design companies, many people fall into a misconception: having IP and a sample board equals success. But the cruel truth of the chip industry is that many can make chips, yet very few can ship stably at volume and be paid for by real customers. As the third-largest company in China's enterprise AI GPGPU market, whether Denglin Technology can achieve volume shipments is clearly beyond doubt. But through several core financial data points, perhaps we can see more. In 2023, Denglin Technology achieved revenue of approximately RMB28 million; in 2024, revenue was RMB83 million; in 2025, revenue reached RMB171 million; and in the first four months of 2026 alone, revenue already reached RMB200 million, surpassing the full year of 2025. Corresponding GPGPU board shipments also accelerated: 12,144 units shipped in full-year 2025, and 10,344 units shipped in the first four months of 2026, approaching last year's full-year level. Revenue and shipments accelerating in tandem is not simply "good-looking numbers"it means orders are no longer sporadic pilots, but customers shifting from "testing and validation" to "volume procurement." Looking at the profit side, Denglin Technology's improvement in profitability is also quite significant. In 2023, the company had a net loss of RMB332 million; in 2024, a net loss of RMB297 million; in 2025, a net loss of RMB281 million; in the first four months of 2026, the company still recorded a net loss of RMB16 million on its books; after excluding non-cash items such as share-based payments, adjusted net profit turned positive in the first four months of 2026, at RMB20.6 million. R&D expenses were not compressed during the same periodfrom 2023 to 2025, the company's R&D expenses remained between RMB238 million and RMB258 million. In the first four months of 2026, R&D expenses as a proportion of revenue declined significantly, due to the rapid expansion of the revenue base rather than any reduction in absolute investment. More critical is the trajectory of gross margin. From 34.4% in 2023 to 49.2% in 2025, and further to 50.9% in the first four months of 2026. The structural uplift in gross margin shows that the company's products are not trading price for volume, but entering customers' deployment lists with a premium. This is also the true meaning of operating leveragethat is, the fixed costs invested earlier in architecture and software stack are being diluted by rapidly expanding revenue. This coincides with a statement in Denglin's prospectus: "The turnaround from loss to profit was primarily driven by revenue growth and operating leverage effects, rather than by cutting R&D expenses." It can be seen that Denglin's profitability inflection point is where the revenue side has run ahead of the cost curve. As of April 30, 2026, Denglin Technology had RMB449 million in cash on its books and had raised approximately RMB1.5 billion in cumulative financing, with industrial capital such as Qualcomm China, China Internet Investment Fund, and Hikvision Fund on its shareholder list. Its capital reserves cannot be described as ample enough to rest easy, but combined with IPO proceeds, they are sufficient to support the iteration window for the next two to three generations of products. It is reported that Denglin Technology expects to raise approximately US$300 million to US$400 million, to be used for iterative upgrades and commercialization of existing GPU product lines, development of new product categories, software toolchain development, and market channel expansion. From the above performance, it can be seen that Denglin Technology focuses on energy-efficient GPGPU design under a fabless model, and its financial data are validating its transition from "making chips" to "volume delivery." The simultaneous acceleration of revenue and shipments, gross margin rising to 50.9%, and adjusted net profit turning positive also mean that the company's growth and profitability trajectory are releasing positive signals. Track growing 58.1%, Denglin breaks through with a differentiated route If viewed in isolation, Denglin's financial inflection point is merely the operating improvement of a small company. Placed in the industry coordinate system, it has caught a track that is rapidly widening. According to Frost & Sullivan data, China's enterprise AI GPGPU market grew from RMB1.9 billion in 2021 to RMB11.8 billion in 2025, at a compound annual growth rate of 58.1%. During the same period, China's public cloud AI GPGPU market grew from RMB21.6 billion in 2021 to RMB111.8 billion in 2025, at a compound annual growth rate of 50.8%. From 2026 to 2030, the enterprise AI GPGPU market is expected to maintain a high compound annual growth rate of 61.3%, reaching RMB108.5 billion in 2030, while the public cloud AI GPGPU market is expected to grow at a compound annual growth rate of 40.5%, reaching RMB494.2 billion in 2030. Driven jointly by the two segments, China's overall GPGPU market is expected to achieve a compound annual growth rate of 43.3% from 2026 to 2030. This is a window where both total volume and structure are favorable. A more important structural change is occurring on the demand side. IDC's judgment is that 2026 is a key year for China's computing power demand to transition from "cloud training" to a dual-driven model of "training + inference." The training market pursues extreme computing power and cluster scale, where Nvidia's moat is deepest; the inference market is far more fragmented, with customers far more sensitive to energy efficiency ratio, deployment cost, private adaptation, and total cost of ownership than to peak computing power. The scenarios Denglin focuses onenterprise private clouds, smart cities, industrial quality inspection, education, edge video analyticsfall precisely within this fragmented range. But this track also has very high barriers. The biggest obstacle is often not the hardware itself, but the software ecosystem. After more than a decade of accumulation, the CUDA ecosystem has formed enormous user inertia; for domestic manufacturers, if customer migration costs are too high, even good hardware specifications will be difficult to truly scale. This is why not all chips can reach volume delivery: hardware can be caught up, but software stacks, driver libraries, and model adaptation require year after year of "grinding scenarios" together with customers, ISVs, and integrators. From this perspective, the opportunity in the track is clear: AI inference demand continues to explode, and self-controllability brings structural dividends; but the challenges are equally obviousthere are no shortcuts in ecosystem building, only time and deployment cases piled up bit by bit. Against this backdrop, Denglin Technology has also carved out its own differentiated position through GPU+ Minsky on-chip heterogeneity. On the technology route, Denglin's GPU+ (Minsky) architecture integrates programmable general-purpose computing units, tensor acceleration engines, and high-bandwidth on-chip memory into a single chip, replacing traditional multi-chip discrete solutions with on-chip heterogeneity, reducing data movement latency and power overhead. Combined with its self-developed Hanming software stack, it achieves compatibility with CUDA and mainstream AI frameworks, greatly reducing the code rewrite cost of customer migration. For enterprise customers, "not having to tear everything down and start over" is itself a scarce purchasing reason. In terms of scenario selection, Denglin's commercialization is concentrated on the inference side and cost-sensitive industry scenarios, rather than general large-model training clusters. Smart cities, enterprise private clouds, and industrial quality inspection may not have dazzling single-order sizes, but demand is dispersed, decision chains are short, and the weight of localized services is highprecisely giving domestic GPUs room to penetrate with cost-effectiveness and hands-on service. The company claims to be the first GPU enterprise in China to achieve large-scale commercial deployment, and its Gaolin and Nashi series have already been mass-produced and adopted by leading enterprises in multiple industries such as the internet and smart cities. Of course, advantages do not equal victory, and we must also objectively see its current shortcomingson the one hand, overall market share remains low, at approximately 1.0% of the domestic market based on 2025 revenue, with a long way to go to catch up with industry leaders; on the other hand, software ecosystem building is a protracted war, and compatibility does not equal "complete equivalence," requiring continued investment in massive adaptation work to cover more niche industry models. From the above, it can be seen that Denglin Technology has broken through with a differentiated heterogeneous architecture, deeply cultivating the high-growth blue ocean of inference computing power, and using continuously high-growing revenue and steadily rising gross margin to prove the commercial viability of its differentiated route. Although market share still needs to improve and ecosystem building still requires deep cultivation, industry dividends continue to be released, and the wave of domestic substitution is unstoppable. Conclusion As national digital transformation moves from single-point informatization to systematic intelligence in government and enterprises, industry, and urban governance, computing power is no longer just the business of a few companies, but a secure and controllable industrial foundation. The market does not have room only for one or two leading companies; it needs a group of domestic computing power suppliers that can deliver stably, be invoked in real scenarios, and stay close to customers in cost and service. Denglin's value lies precisely in its entry into the fragmented inference-side market with GPU+ on-chip heterogeneity and the Hanming software stack, using a migration experience of "not having to tear everything down and start over" and hands-on service to advance domestic GPUs from "technically feasible" to "commercially usable"; what it represents is not the financial improvement of a small company, but a realistic sample of domestic computing power landing across multiple routes and multiple scenarios. But listing is only a ticket to a new stage, not the climax of the story. Whether Denglin can truly break out ultimately depends on three things: whether it can continuously deliver hardware with stable performance and controllable yield; whether it can grind its software ecosystem from "compatible" to "easy to use," enabling more models, ISVs, and integrators to adapt at low cost; and whether it can turn scattered projects into replicable industrial orders, forming customer repeat purchases and ecosystem compounding.