DeepSeek V4 to Debut with Native Multimodal Capabilities, Breaking Industry Norms by Excluding US Chipmakers

By Creati.ai Editorial Team
March 2, 2026

In a move that signals a deepening fracture in the global artificial intelligence supply chain, Chinese AI laboratory DeepSeek is poised to release its fourth-generation flagship model, DeepSeek V4. Reports indicate the model will launch in early March, coinciding with China’s annual "Two Sessions" parliamentary meetings. Unlike its predecessors, V4 is a natively multimodal system capable of generating text, images, and video, positioning it as a direct competitor to Google’s Gemini 3.0 and OpenAI’s latest offerings.

However, the technological leap is being overshadowed by a significant strategic pivot: DeepSeek has reportedly withheld pre-release optimization access from US semiconductor giants Nvidia and AMD. Instead, the laboratory has granted exclusive early access to domestic Chinese chipmakers, specifically Huawei and Cambricon, to optimize the model for their hardware. This decision breaks a long-standing industry protocol where major model developers collaborate with Nvidia to ensure day-one performance, marking a distinct shift toward "sovereign AI" ecosystems.

The Strategic Pivot: Prioritizing Domestic Silicon

For years, the standard operating procedure for top-tier AI labs—including OpenAI, Anthropic, and previously DeepSeek—has been to provide Nvidia and AMD with model weights and architectural details weeks before a public launch. This "optimization window" allows chip manufacturers to update their software stacks (such as CUDA and ROCm) to ensure the new model runs efficiently on their GPUs immediately upon release.

By denying this access to US firms, DeepSeek is effectively forcing a performance lag for users running V4 on Nvidia hardware at launch, while ensuring the model runs seamlessly on Huawei’s Ascend 910C and Cambricon’s MLU series chips.

Implications of the Exclusion Strategy:

Technical Architecture: The "Engram" and 1-Million Context

Beyond the geopolitical maneuvering, DeepSeek V4 introduces substantial architectural innovations designed to maintain the lab’s reputation for extreme cost efficiency. The model is built on a massive Mixture-of-Experts (MoE) architecture with an estimated 1 trillion total parameters, yet it activates only roughly 32 billion parameters per token. This sparsity allows it to deliver performance comparable to dense models like GPT-5-class systems while requiring a fraction of the compute power for inference.

A key differentiator for V4 is the introduction of the "Engram" conditional memory architecture. This novel mechanism separates static knowledge retrieval from dynamic reasoning, allowing the model to access a context window exceeding 1 million tokens without the quadratic computational penalty associated with traditional Transformer attention mechanisms.

Key Technical Specifications of DeepSeek V4:

• Model Type: Native Multimodal (Text, Image, Video)
• Total Parameters: ~1 Trillion (MoE)
• Active Parameters: ~32 Billion per token
• Context Window: 1,000,000+ Tokens
• Key Innovation: Engram Conditional Memory & Manifold-Constrained Hyper-Connections (mHC)
• Target Hardware: Optimized for Huawei Ascend 910 series; Consumer-grade inference possible on dual RTX 4090s via quantization.

Multimodal Convergence: entering the "Omni" Era

DeepSeek V4 represents the lab's first foray into a truly "omni" model structure. Previous iterations, such as the Janus series, separated visual understanding from text generation. V4 unifies these modalities, allowing for complex reasoning tasks that interleave text, code, and visual inputs.

For instance, the model is reported to handle video-to-code generation, where a user can upload a screen recording of a UI interaction, and the model generates the corresponding frontend code. Similarly, its video generation capabilities are expected to rival specialized models, leveraging the vast context window to maintain temporal consistency across longer clips.

This capability places DeepSeek V4 in direct competition with Google’s Gemini 1.5 Pro and Gemini 3.0, which have defined the current standard for long-context multimodal reasoning. However, DeepSeek’s open-weights approach (expected to follow the V3 licensing model) could disrupt the market by placing these capabilities into the hands of developers for free, undercutting the API-based business models of Western competitors.

The "Blackwell" Controversy and Export Controls

The release of V4 comes amidst heightened scrutiny regarding DeepSeek’s training infrastructure. Recent reports from Reuters and the Financial Times cite anonymous US officials alleging that DeepSeek may have trained its models on restricted Nvidia Blackwell chips, potentially acquired through gray market channels in violation of US export controls.

DeepSeek’s pivot to Huawei for the V4 launch serves a dual purpose in this context:

1. Plausible Deniability: By showcasing superior optimization on Huawei hardware, DeepSeek reinforces the narrative that its breakthroughs are indigenous, distancing itself from reliance on illicitly acquired US technology.
2. Regulatory Shielding: Explicitly favoring domestic chips aligns the company with Beijing’s strategic interests, offering protection against potential domestic regulatory crackdowns while simultaneously thumbing its nose at US sanctions.

Market Impact: A Challenge to the "Capex Bubble"

The release of DeepSeek V4 poses a subtle but dangerous threat to the current AI economic model, often referred to as the "Capex Bubble." Western tech giants are currently spending hundreds of billions of dollars on AI infrastructure, predicated on the belief that scaling laws require exponential increases in compute and energy.

DeepSeek challenged this assumption with its V3 and R1 models, which were trained for less than $6 million—a fraction of the cost of OpenAI’s GPT-4. If V4 delivers "state-of-the-art" multimodal performance on a similarly shoestring budget, it further validates the thesis that algorithmic efficiency (via MoE and Engram architectures) matters more than brute-force compute.

Potential Market Ripples:

• Software Efficiency: A validation of DeepSeek's "Manifold-Constrained Hyper-Connections" (mHC) could lead to a global rethink of Transformer architecture efficiency.
• Pricing Pressure: If V4 is open-sourced, it will likely force API providers like OpenAI and Anthropic to further lower prices for multimodal tokens to remain competitive.
• Hardware Democratization: The ability to run V4 on consumer-grade hardware (like the RTX 4090 or 5090) could decentralize AI development, reducing the reliance on massive cloud clusters.

Conclusion: A New Phase of AI Bifurcation

The impending release of DeepSeek V4 is more than just a product launch; it is a geopolitical statement. By decoupling its optimization roadmap from Nvidia and AMD, DeepSeek is effectively drawing a line in the silicon. The message is clear: China intends to build a self-sufficient AI stack, from the chip layer to the application layer.

For the global AI community, the V4 release presents a dilemma. The model’s likely open availability and high performance make it irresistible for researchers and developers. Yet, its optimization bias toward non-Western hardware may fracture the community, creating "walled gardens" of optimization where models perform best on the hardware of the geopolitical bloc they originated from.

As the "Two Sessions" convene in Beijing next week, the world will be watching not just the political speeches, but the benchmarks of a model that promises to redefine what is possible with limited compute and sovereign silicon.