The industry has long had a common misconception about DeepSeek: most people focus on its model performance, open source strategy, low-priced API, and the shortcomings of lacking multimodal and subscription applications, viewing it as a model manufacturer that relies solely on technology competition and price for market.

But through surface level competition, it is not difficult to find that DeepSeek's ultimate ambition has never stopped at monetizing at the application layer. All of its technological iterations, architectural innovations, and open-source layouts revolve around a core underlying logic: in the context of high-end GPUs, advanced processes, HBM memory, and limited CUDA ecosystems, through systematic AI architecture innovation, the hardware threshold for AI training and inference is minimized to the extreme.

From MoE hybrid experts, MLA/CSA/KV cache compression, to Engram memory architecture, mHC cross layer connectivity, Dual Path loading, TileLang cross end programming, DeepSeek is building a brand new AI underlying technology system. This system not only reduces costs and increases efficiency for itself, but also deeply adapts to the domestic storage, GPU, and ASIC hardware ecosystem, attempting to break the monopoly of overseas technology, leverage the $10 trillion AI infrastructure industry, and sprint towards a trillion dollar valuation.

Compared to short-term API and subscription revenue, DeepSeek's true long-term strategy is to adapt hardware, restructure computing power costs, and cultivate a new domestic AI hardware ecosystem.

1、 Anti common sense layout: Abandoning short-term monetization and deepening the cultivation of underlying technological barriers

In the industry trend of leading domestic model manufacturers competing to focus on multimodal, audio and video, intelligent programming, paid subscriptions, and enterprise applications, quickly landing commercial scenarios and harvesting short-term profits, DeepSeek has taken a completely different path against the trend.

To this day, DeepSeek has not yet launched a mature paid subscription system, has no multimodal, audio and video product lines, and has not established a complete external task scheduling framework, seemingly missing out on the monetization dividends of mainstream application layers. At the same time, it insists on large-scale open source of core technologies, public disclosure of underlying architecture solutions, and sharing of technological iteration results, which is considered almost "free and profitable" by the outside world.

But this is not blindly burning money, but a precise strategic choice. When peers are caught in the homogenization and internal competition of the application layer, DeepSeek avoids red ocean competition and focuses on the most core, bottom layer, and monopolistic potential areas of AI: model architecture innovation, computing power cost optimization, and hardware adaptation reconstruction.

Looking back at its development history, each iteration of DeepSeek is a set of underlying innovations that break through industry bottlenecks: abandoning traditional dense models and delving deeper into high difficulty MoE hybrid expert architectures; Replacing high cost PPO reinforcement learning with GRPO and RLVR algorithms significantly reduces training costs; Maximizing GPU resource utilization through multi token prediction, zero bubble pipeline, and wide expert parallel strategy; Continuously iterating attention mechanisms, memory architecture, and cross layer connections to systematically address industry pain points such as long context, high VRAM, and difficulty in training large models.

This long-term oriented technological layout is enabling DeepSeek to break away from its single positioning as a "model manufacturer" and become the underlying rule maker of the new generation AI hardware ecosystem.

2、 Core technology breakthrough: Extreme compression of resource requirements, breaking the dependence on high-end computing power

The core constraints of current global AI development are concentrated in the scarcity of high-end computing power, expensive HBM memory, CUDA ecological monopoly, and limited advanced processes. DeepSeek's full range of technological innovations precisely target these bottlenecks and achieve breakthroughs in "running top-level AI models with low-end hardware" through architecture optimization. Among them, KV Cache's ultimate compression is the most representative landing result.

Based on measured data from a professional KV cache calculator, the DeepSeek V4 Pro shows a crushing advantage in terms of memory usage in 1 million ultra long context scenarios and FP8/INT8 accuracy standards: it only requires 5.48GB HBM of memory to run; In the same scenario, the GLM-5 with 70 billion parameters requires 60GB HBM, and the Qwen3 with 23.5 billion parameters requires as much as 89GB HBM.

It is worth noting that DeepSeek V4 Pro is a super large model with 1.6 trillion parameters, far exceeding its competitors in parameter scale, but achieving ten to hundred times the level of video memory optimization. Relying on a series of self-developed attention mechanisms such as MLA, DSA, CSA, HSA, DeepSeek has achieved over 90% KV Cache compression, completely solving the problem of memory explosion in large model long context inference.

This innovation brings two core values: firstly, significantly reducing the operating costs of long-period AI agents and ultra long text tasks, unlocking new AI application scenarios; Secondly, weaken the dependence on scarce high-end HBM memory, allowing AI inference to scale down to ordinary storage devices.

3、 Empowering hardware with technology: binding domestic storage and building an alternative computing power system

DeepSeek's underlying innovation perfectly matches the development advantages of the domestic hardware ecosystem, forming a perfectly complementary industrial closed loop. There are shortcomings in the fields of advanced processes, high-end GPUs, and EUV lithography machines in China, but in storage areas such as NAND flash memory and LPDDR memory, it already has mature mass production capabilities and global competitiveness.

Relying on the ultimate KV Cache compression technology, DeepSeek has achieved lightweight and long-term storage of cache, efficiently offloading massive KV cache to SSD and NAND flash without repeated calculations, greatly reducing the computing pressure on GPU and ASIC. This technology directly revitalizes the domestic NAND industry represented by Yangtze Memory Technology (YMTC), opening up the AI computing power application market for massive civilian and industrial grade SSDs.

On this basis, DeepSeek's self-developed Engram memory architecture further completes the strategic upgrade of "memory conversion power". By utilizing a modern N-gram hash retrieval mechanism, we aim to develop O (1) high-speed conditional memory lookup capability, replacing the inefficient repetitive computation of Transformers. We utilize low-cost LPDDR memory to support massive knowledge embedding tables, significantly reducing the forward computational overhead of the model.

This innovation precisely adapts to the LPDDR product system of Changxin Storage (CXMT). The gap between domestic LPDDR technology and overseas is only half to one generation, with sufficient mass production scale, which can perfectly meet the memory requirements of the Engram architecture, forming a new computing paradigm of "NAND carrying cache, LPDDR carrying knowledge memory, and low-end GPU completing calculations", completely breaking away from dependence on high-end HBM and top-level GPU.

4、 Full stack architecture iteration: comprehensively consolidating the underlying foundation of domestic AI in all aspects

In addition to storage adaptation, DeepSeek has achieved a breakthrough in the bottom layer of the full stack from three dimensions: model architecture, training stability, and cross end adaptation, fully filling the ecological gaps of domestic AI hardware.

1. MoE hybrid expert architecture: reduces the training computation of super large models by 40% -50%, combined with a wide expert parallel strategy, significantly increases the inference batch capacity, and reduces the cost of a single token. With only 2048 limited H800 GPUs, it is possible to train a trillion parameter model at the top level of benchmark closed source, maximizing the utilization of computing power.

2. DSA dynamic sparse attention: Cracking the pain point of long context computation, achieving context length expansion and basically constant computation, completely solving the industry problem of "longer text, higher computing power consumption" in traditional models, and further alleviating the bandwidth pressure of HBM.

3. mHC manifold constrained hyperconnectivity: Reconstruct the inter layer information flow mechanism of Transformer, constrain multiple parallel information channels with double random matrices, and solve the problems of signal attenuation and gradient explosion in training of super large models with only a 6.7% increase in training time. Significantly improve model reasoning, mathematical calculation, and general knowledge abilities, achieving 'equal computing power, stronger intelligence'.

4. TileLang Cross End Programming Framework: Targeting the pain points of CUDA ecological monopoly, achieving one-time encoding, multi terminal deployment, and compatible with various domestic GPU and ASIC hardware. By leveraging the CUDA translation capabilities of manufacturers such as Moore Thread, Mu Xi, and Bi Ren, we aim to completely break down the barriers of overseas software ecosystems and establish a universal AI development foundation for domestic hardware.

5、 Industry resonance: technology open source sharing, leading global AI paradigm innovation

DeepSeek's underlying technological innovation is no longer a technical barrier for a single enterprise, but has become a public infrastructure for the entire industry. At present, its core architectures such as MLA and DSA have been widely adopted by leading domestic model manufacturers such as GLM and Moonshot, becoming the standard technical solution for the new generation of large models. The industry technology roadmap is completely approaching DeepSeek's direction of lightweight, low graphics memory, and high adaptability.

With the continuous optimization of hardware adaptability and the continuous reduction of computing power costs, DeepSeek is able to invest in more extreme AI research: large-scale reinforcement learning post training, trillion level trajectory data iteration, ultra long context model polishing, and RSI artificial intelligence autonomous experimental system. The research paradigm of AI autonomous trial and error, autonomous iteration, and autonomous innovation will lay the core foundation for the implementation of AGI.

6、 Ultimate Business Chess Game: Benchmarking against OpenAI, laying out a trillion yuan industrial ecosystem

DeepSeek's abandonment of short-term application layer monetization is essentially abandoning the business of "small profits, short cycles" and aiming for the ultimate dividend of "big ecology, long cycles, and high barriers". Its business model can be compared to OpenAI's deep binding model with AMD and Cerebras: it is not limited to its own product revenue, but empowers hardware manufacturers through technology, deeply binds core players in the industry chain, and obtains industry equity and ecological dividends.

OpenAI has achieved a milestone in computing power procurement, locking in AMD's large-scale stock warrants and deeply sharing the growth dividends of the hardware industry. Similarly, DeepSeek, with its exclusive underlying technology, has become a domestic storage GPU、ASIC、 The core enabler of network chip manufacturers, helping domestic hardware break through the bottleneck of computing power, achieve commercialization, and seize the global market.

The current global overseas AI industry chain stock market value has exceeded $10 trillion, while the domestic AI hardware ecosystem is still in its infancy. DeepSeek is cultivating a domestically produced AI infrastructure system with a scale of 10 trillion yuan that is independently controllable through open source technology, architecture adaptation, and ecological co construction.

In this ecosystem, DeepSeek can achieve commercial value far beyond the application layer through industry equity, ecosystem sharing, technology licensing, and other means without relying on subscriptions and API profits, ultimately achieving a valuation of $1 trillion.

Conclusion

The true value of DeepSeek has never been a high-performance large model, but a next-generation AI underlying system that adapts to domestic hardware, breaks through overseas monopolies, and reconstructs computing power costs.

When everyone in the industry is competing for users, applications, and short-term revenue, DeepSeek silently reconstructs the underlying rules of computing power, memory, storage, and programming frameworks. It proves through repeated underlying innovations that the future of AI does not depend on who has more applications, but on who can make computing power more accessible, hardware more usable, and industries larger.

This bottom-up layout spanning several years will ultimately create a trillion dollar domestic AI hardware ecosystem and realize the ultimate value of DeepSeek.