DeepSeek V4 Drops This Month: 1 Trillion Parameters on Huawei Ascend, China's AI Just Got Real
Reuters dropped a bombshell on April 10th: DeepSeek V4 is coming.
I’ll be honest—when I saw “running on Huawei Ascend processors,” I paused. Not because of the specs—1 trillion parameters and MoE architecture were expected—but because this “fully indigenous” signal arrived faster than I anticipated.
From “Usable” to “Actually Hard Core”
When discussing Chinese LLMs, everyone talks about “narrowing the gap” and “rapid catch-up.” But let’s be real—most models are still trained on NVIDIA GPUs. It’s like building a great car but using someone else’s engine. The car is yours, but the core tech is still in someone else’s hands.
The key breakthrough with DeepSeek V4 isn’t the parameter count or architecture—it’s that training and inference are entirely done on Huawei Ascend. What does this mean? It means our AI infrastructure has finally formed a closed loop.
I tested Ascend 910 on Tencent Cloud before—performance was solid, but the software ecosystem was the weak link. The fact that DeepSeek can train a trillion-parameter model on Ascend means Huawei’s CANN (Compute Architecture for Neural Networks) stack has matured enough to handle this scale. This is more significant than just chasing model benchmarks.
1 Trillion Parameters, MoE Architecture, Let the Data Speak
Parameter count isn’t everything, but it’s a hard metric. DeepSeek V4 uses Mixture of Experts (MoE) architecture—basically, the model has 1 trillion parameters but only activates a subset during each inference. Think of it like having 100 experts but only consulting the most relevant few for each question, instead of calling everyone to a meeting.
According to disclosed information, DeepSeek V4 reaches frontier-level performance in code generation, mathematical reasoning, and multimodal understanding. Specific benchmark numbers will come at launch, but given DeepSeek’s track record, they don’t do hype—V3 already surprised a lot of people in coding capabilities, and V4 should push further.
Compute Sovereignty = Real “Hard Power”
I wrote an article before asking “Are Chinese AI chips actually viable?” My assessment then: hardware caught up, but software ecosystem was the bottleneck. Now, that bottleneck is being rapidly resolved.
DeepSeek V4’s release is like a shot of adrenaline for indigenous AI infrastructure. It proves one thing: with purely domestic compute, we can train world-class LLMs. This signal is more powerful than any PR copy.
Not saying NVIDIA doesn’t matter—they’re still the industry benchmark. But at least we have a “backup” now, and this backup isn’t a compromise—it’s genuinely capable.
The “Wait, Actually Good” Moment in Tech Circles
I saw discussions in several tech groups—most people’s reaction was “wait, really?” Honestly, I was surprised too. Not because I doubted Huawei Ascend’s capability, but I didn’t expect trillion-parameter models to land this fast.
Sometimes technology advances faster than we imagine.
DeepSeek V4 launches in late April, and I’ll benchmark it immediately to see how this “purely domestic-trained” model scores. But from current signals, Chinese LLMs just got “hard” for real—not from hype, but from code and compute.
Don’t rush—wait for the official release and see the data.
Note: This article is based on publicly available information. Specific performance data should be verified against DeepSeek’s official release.