DeepSeek V4 Architecture Revealed: This Trillion-Parameter Model Takes MoE to New Heights

On April 18, the AI community’s focus wasn’t on a model launch, but on further disclosure of DeepSeek V4’s architecture details.

Honestly, I’ve been following DeepSeek for a long time. From V1 to V3, this company’s technical roadmap has been clear: use minimal compute to build the best model. But V4’s architecture details still surprised me.

Let’s start with parameter scale. According to disclosed information, DeepSeek V4 might have 1.6 trillion parameters—nearly 24 times V3’s 67 billion. But more crucially, its activated parameter count hasn’t grown proportionally, but stayed within a relatively reasonable range.

This brings us to MoE (Mixture of Experts) architecture.

Traditional Dense models (like GPT-4) need to activate all parameters during inference. MoE models only activate some “expert” networks during inference, allowing control of inference costs while maintaining large parameter scales.

DeepSeek V4 has taken this concept further. According to disclosures, V4’s MoE architecture is called “Mega MoE,” with activated experts jumping from V3’s 256 to 512. What does this mean?

Simply put, the model can assign tasks more precisely. For instance, encountering a programming problem, the model can select from 512 experts the ones most skilled at programming; for math problems, switch to math experts. This “expert division” approach theoretically allows the model to reach specialized SOTA levels in every domain.

But personally, I think there’s a technical challenge: how to ensure all 512 experts are fully trained? If some experts are rarely activated, their parameters might not train adequately. How did DeepSeek solve this? Official sources didn’t elaborate, but industry rumors suggest they may have introduced a new “expert load balancing” algorithm, forcing every expert to participate in training.

This is quite interesting. I tried DeepSeek V3 before—my impression was strong Chinese capabilities, but room for improvement in specialized domains (like code generation). If V4 truly perfects “expert division,” it might reach top levels across multiple domains.

Another point worth watching is training efficiency.

DeepSeek claims V4’s training efficiency improved 40% over V3. This number sounds abstract, but put another way—with the same compute, V4 can train larger models—this becomes very attractive. After all, compute cost is one of AI companies’ biggest expenses; improving training efficiency means building stronger models under the same budget.

How exactly? According to technical docs, DeepSeek introduced “dynamic sparse attention” in V4, letting the model focus only on truly useful contextual information during training, rather than processing all inputs like traditional models. This is like human reading—not word by word, but focusing on key passages.

My personal feeling is DeepSeek’s technical roadmap is increasingly mature. From initial “catcher-upper” to current “innovator,” this company demonstrates Chinese AI teams’ progress in engineering capabilities. Of course, V4’s ultimate level still needs real-world verification. But from architecture design, it’s truly charted a different path from OpenAI and Anthropic.

This reminds me of a saying: AI competition might ultimately not be about “who has the bigger model,” but “who has the smarter architecture.”

DeepSeek V4 might be a footnote to this statement.

But then again, no matter how good the architecture, we need to see deployment results. My current expectation: when will the API open? When can it be deployed locally? After all, for developers, a model that can’t be used is just a slideshow, no matter how big the parameters.

Looking forward to DeepSeek revealing more information soon.