DeepSeek V4 Set to Drive Structural Change in LLM Development: Will Today's Frontrunners Become Tomorrow's Followers?

We are standing at a pivotal moment in the evolution of large language models. The approach adopted by DeepSeek for its V4 model is not aimed at incremental gains. It is aimed at reshaping the foundations of how LLMs are built, trained, and deployed.

The signs were visible earlier than most acknowledged. The V2 model, launched in 2024, explicitly presented itself as "A Strong, Economical, and Efficient Mixture-of-Experts Language Model". Its benchmark scores, while not as striking as those later demonstrated by V3, were sufficient to indicate that the organisation was building a state-of-the-art model with deliberate awareness of hardware constraints and cost structures.

Source: DeepSeek.com

It was V3 that forced the broader community to take notice. The efficiency gains were real, the performance competitive, and financial markets responded accordingly — Nvidia's stock fell 17 percent in a single session, erasing over half a trillion dollars in market capitalisation.

Attention has now turned to V4, with a release date expected around mid-February 2026. In January 2026, DeepSeek published its Engram conditional memory paper and the architectural choices underpinning it suggest that DeepSeek is no longer merely competing within the existing paradigm. It is attempting to replace it.

A Culture of Structural Innovation

One of the more revealing aspects of DeepSeek's approach is the apparent simplicity with which its team frames frontier innovation. Their posture is unusually understated — almost as if nothing extraordinary is taking place. And yet, they have firmly established themselves within the front ranks of model developers, not through brute-force scaling but through a disciplined, long-term strategy grounded in architectural efficiency.

Consider a statement from their recent research: "[...] the intrinsic heterogeneity of linguistic signals suggests significant room for structural optimisation. Specifically, language modelling entails two qualitatively different sub-tasks: compositional reasoning and knowledge retrieval."

Stripped to its essence, the argument is this: current models waste significant computational resources retrieving information they should simply be able to look up. The question DeepSeek posed to its scientists was precise: why force a model to recalculate what it already knows? The answer is the Engram architecture.

The Engram Architecture: Memory as a New Axis

Engram Conditional Memory is one of three key innovations expected with the release of DeepSeek V4, alongside Manifold-Constrained Hyper-Connections (mHC) and Dynamic Sparse Attention coupled with the Lightning Indexer.

The innovation addresses a fundamental limitation of the prevailing Mixture-of-Experts (MoE) architecture. MoE scales capacity through conditional computation, but it treats all tasks — whether complex reasoning or routine factual recall — as requiring the same expensive neural processing. Models such as GPT-4 and Claude are forced to simulate knowledge retrieval by running full computational cycles on static data. This is both wasteful and, at scale, strategically inefficient.

DeepSeek's response is to introduce Conditional Memory as a complementary axis to neural computation. The Engram module — equipped with tokeniser compression, multi-head hashing, contextualised gating, and multi-branch integration — enables the model to retrieve static knowledge, named entities, formulaic patterns, and stereotyped code through deterministic hash-based lookups, without taxing the GPU's reasoning core.

In collaboration with Peking University, DeepSeek has identified a U-shaped Scaling Law governing the optimal allocation between neural computation (MoE) and static memory (Engram). Their research demonstrates that a model can outperform strictly iso-parameter and iso-FLOPs MoE baselines by dedicating 20–25% of sparse parameters to memory — in effect, by knowing when to look up information rather than recalculate it.

Source: DeepSeek / Peking University, "Conditional Memory via Scalable Lookup," January 2026

The results, tested on a 27B parameter model, are notable not only for the expected gains in knowledge retrieval but for improvements across general reasoning, code generation, and mathematical domains. By offloading static reconstruction from the model's early layers, the bi-axial architecture frees capacity for complex reasoning. In a like-for-like comparison, NIAH Accuracy increased by 12.8 percentage points, reaching 97.0%.
The efficiency gains over standard MoE suggest Engram addresses a fundamentally different bottleneck in LLM scaling.

The transition from a mono-axial model (MoE alone) to a bi-axial model (MoE + Engram) does not merely add a retrieval layer. It restructures the division of labour within the model itself.

The Hardware Bypass

The strategic significance of Engram extends well beyond model performance. Its most consequential implication is architectural: it reduces dependency on GPU High-Bandwidth Memory (HBM) — the scarcest and most expensive component in the current AI hardware stack, and the primary target of US export controls against China.

Unlike the dynamic routing of MoE experts, Engram uses deterministic addressing, which naturally supports the decoupling of parameter storage from GPU-bound computation. This enables the implementation of a Multi-Level Cache Hierarchy that leverages the Zipfian distribution — the statistical reality that a small fraction of data patterns accounts for the vast majority of usage. High-frequency patterns are served from GPU HBM; while Engram's architecture effectively bypasses GPU memory constraints by offloading static retrieval to system DRAM.

The result is a model that can scale to massive parameter counts without a proportional increase in high-end GPU procurement. DeepSeek is not building a model optimised for the hardware Western labs rely on. It is building a vertical stack designed to run on the hardware it has — and that Western export controls cannot easily restrict.

This is the core innovation of DeepSeek V4, dictated equally by strategic intent — DeepSeek's stated objective of making advanced AI accessible at scale — and by structural necessity — the constraints imposed by the ongoing semiconductor restrictions between the US and China.

The Geopolitical Implications

DeepSeek V4 represents the most significant architectural departure in the LLM landscape since the advent of Mixture-of-Experts. By introducing a structural bifurcation between factual retrieval and active reasoning, it moves beyond the brute-force scaling laws that have defined — and favoured — Western AI development.

The implications for the competitive landscape are substantial. If DeepSeek's claims withstand independent verification, the market equilibrium shifts from capability at any cost to frontier reasoning at commodity pricing. The pressure on incumbents — OpenAI, Anthropic, Google — becomes not merely technical but economic. By offering open weights, million-token-plus context windows, and pricing expected below $1 per million output tokens, DeepSeek is positioning itself to commoditise the core of the AI stack, forcing Western competitors to justify premium pricing in an increasingly cost-sensitive market.

DeepSeek's framing of Conditional Memory as an "indispensable modelling primitive" carries a clear message: if the competitive arena shifts from "who has the most H100s" to "who has the most efficient memory-compute allocation," the advantage tilts decisively toward the lab that can achieve frontier performance on commodity infrastructure.

For Western AI labs, the question is no longer whether to engage with this architectural shift, but how quickly they can adapt. The alternative — continuing to scale HBM-dependent clusters while a competitor demonstrates that equivalent performance is achievable on cheaper, more accessible hardware — carries its own risks.

However, a necessary caveat: until independent, third-party benchmarks confirm that the Engram architecture delivers the efficiency gains described in DeepSeek's research papers, these remain claims rather than established facts. The promise of consumer-hardware deployment for a model of this calibre is potentially transformative, but it awaits rigorous validation in real-world, non-optimised environments.

The whitepaper is compelling. The question now is whether the model delivers.

This is part of Tech Cold War's analysis of the AI competition between the United States and China. Subscribe to receive analysis directly in your inbox.

Sources
DeepSeek, "DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model," 2024 (deepseek.com)
DeepSeek and Peking University, "Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models," January 2026 (arxiv.org)
DeepSeek, "Manifold-Constrained Hyper-Connections," January 2026 (arxiv.org)
The Information, "DeepSeek To Release Next Flagship AI Model With Strong Coding Ability," January 2026 (theinformation.com)
Reuters, "DeepSeek to launch new AI model focused on coding in February," January 2026 (reuters.com)