If you are trying to sell a $20 per month coding subscription while spending billions on compute, what the hell are you even doing?
DeepSeek does not have competitive coding plans like GLM or MiniMax.
They have ignored the multimodal hype of audio and video.
They remain ruthlessly committed to open source.
If you view them as a software company, it looks like financial madness.
Right?
So, why would investors throw $10 billion into an open-source research lab?
An AI infrastructure analyst recently published a massive technical teardown on X exposing exactly what DeepSeek is actually building.
They are not playing a short-term software game.
They are executing a highly calculated engineering strategy to bypass Western hardware monopolies, enable a $10 trillion Chinese AI hardware ecosystem, and achieve a $1 trillion valuation for themselves in the process.
Here is the breakdown from my notes on how DeepSeek is systematically destroying the dependency on cutting-edge GPUs, and the exact hardware arbitrage they are executing today.
The HBM Bottleneck and the KV Cache Math
The weakest link in the Chinese AI supply chain is High Bandwidth Memory (HBM).
To bypass this, DeepSeek began ruthlessly optimizing memory consumption starting with the V2 models.
Look at the raw KV Cache math for processing a 1 million token context window using 8-bit KV precision:
- Qwen3–235B: Requires a massive 89 GB of HBM.
- GLM5 (700B): Requires 60 GB of HBM.
- DeepSeek V4 Pro (1.6T): Requires just 5.48 GB of HBM.
DeepSeek is a 1.6 trillion parameter model, yet it consumes less than 10% of the memory of its much smaller competitors. Because their KV Cache footprint is microscopically small, they can offer long-held cache at ridiculous prices.
More importantly, a tiny KV cache allows the model to efficiently offload data directly to SSDs. This completely shifts the hardware dependency away from scarce HBM and directly onto NAND flash memory.
The Architectural Teardown
DeepSeek’s entire research trajectory is a masterclass in constraint engineering. They systematically identify a hardware bottleneck and invent an algorithmic bypass. The original analyst documented their exact progression:
- GRPO and RLVR: Instead of using the dominant and expensive PPO algorithm for Reinforcement Learning, they built GRPO from first principles. They then layered on Reinforcement Learning from Verified Rewards (RLVR) to spike reasoning ability.
- MoE and MLA: Introduced in V2. Mixture of Experts slashed compute requirements by 50%, while Multi-head Latent Attention (MLA) compressed the KV cache by 90%.
- DSA: Introduced in V3.2. It ensures that computation demand stays flat even as the context horizon grows.
- mHC (Manifold-Constrained Hyper-Connections): This is the breakthrough from late 2025. Standard unconstrained hyper-connections caused massive instability at scale (a 3000x signal explosion at 27B parameters). DeepSeek constrained the residual mixing matrices to be doubly stochastic using Sinkhorn-Knopp projection. It adds only 6.7% training overhead but delivers massive reasoning gains because it guarantees signal stability across infinite depth.
- CSA and HSA: Introduced in V4 in April 2026. This compresses the KV tokens even further, drastically reducing raw FLOPs and relieving pressure on both HBM and the core ASIC.
- TileLang: The execution layer. TileLang allows developers to write computational kernels once and run them on multiple hardware platforms. It is a direct assault on the NVIDIA CUDA moat, unlocking alternative hardware like AMD and Chinese domestic chips.
Trading Memory for Compute (The Engram Breakthrough)
Chinese GPUs and ASICs will likely lag behind Western hardware in raw FLOPs for the foreseeable future due to transistor density limits. DeepSeek knows this. Their solution is to aggressively trade memory for compute.
In Q1 2026, they introduced Engram. Modern transformers inefficiently simulate data retrieval through heavy computation. Engram modernizes the classic N-gram embedding into an O(1) hash-based lookup.
You save massive amounts of GPU computation, but you need abundant memory to host the embedding tables. The brilliance of this trade-off is that a memory lookup from LPDDR is dramatically cheaper than a full forward pass through transformer layers.
The Capital Strategy: Equity in the Ecosystem
This is where the $10 trillion business model crystallizes.
Who supplies massive quantities of NAND and SSDs? Companies like YMTC.
Who manufactures the LPDDR memory required for Engram lookups? Companies like CXMT.
DeepSeek is structurally reshaping AI architectures so they can run efficiently on Chinese domestic memory and alternative ASICs.
Look at the precedent set by OpenAI.
OpenAI received warrants to buy up to 160 million shares of AMD stock at a low price, contingent on scaling up deployment milestones.
DeepSeek is perfectly positioned to execute identical agreements with Chinese memory, ASIC, and networking manufacturers.
By fundamentally lowering the resource requirements for AGI, DeepSeek is making alternative hardware viable. They will undoubtedly take equity and warrants in the hardware ecosystem they are saving.
DeepSeek CEO Liang Wenfeng is not playing a short-term game.
They are not selling $20 coding wrappers.
They are solving the math required to run a trillion-parameter intelligence on highly constrained silicon.
They are ensuring that when the Chinese hardware ecosystem scales to a $10 trillion valuation, DeepSeek holds the equity that made it possible.
In case we are meeting for the first time, come over here, it’ll be worth the roller coaster of articles that are gonna come up in the next few weeks.