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DeepSeek V4 Countdown: 35x Speed Boost, China's LLM 'Leap of Faith'?

DeepSeek, DeepSeek V4, Chinese LLM, AI Inference, High-Flyer Quant —

If you follow Chinese AI, you’ve probably heard the news: DeepSeek V4 is coming, expected later this month.

Rumors are flying everywhere: 35x faster inference, record parameter counts, possibly the first domestic model that truly ‘rivals GPT-5.’

As someone who’s tracked this team since DeepSeek V1, I want to talk about the story behind it—not just the technology, but why this company keeps punching above its weight.

First, that 35x speed boost. If true, this would be a massive technical breakthrough.

Large model inference speed has always been a pain point. Bigger models mean slower inference and higher costs. Current solutions fall into three categories:

First: ‘Quantization’—reducing model precision from FP16 to INT8 or even INT4, trading quality for speed. This is the most common approach, but has clear ceilings.

Second: ‘Distillation’—training smaller models to mimic bigger ones. Faster, but capabilities definitely suffer.

Third: ‘Architecture optimization’—attacking the model structure itself, like Mixture of Experts (MoE), where the model only activates a subset of parameters each time.

DeepSeek V4’s rumored 35x boost likely falls in the third category—architecture-level breakthroughs. Specifically, possibly more aggressive sparsity strategies or deep inference engine optimization.

Industry insiders speculate DeepSeek may be experimenting with ‘dynamic routing’: the model automatically decides how much compute to use based on input complexity. Fast for simple questions, deep thinking for hard ones.

If true, this would disrupt the current ‘one-size-fits-all’ inference paradigm.

But honestly, I’m less curious about technical details than about DeepSeek’s ‘underdog logic’ itself.

You may know DeepSeek is backed by High-Flyer Quant, a hedge fund. This fund is already a top player in quantitative trading, managing tens of billions.

But quant trading and AI large models are completely different beasts. How does a quant trading company make models rivaling OpenAI?

My observation: High-Flyer building DeepSeek is actually ‘capability overflow.’

What’s the core of quantitative trading? Using AI models to find patterns in massive data, then automating trades. This requires:

  1. Powerful compute (High-Flyer has tens of thousands of A100s/H100s)
  2. Top AI talent (quant fund AI teams are industry elite)
  3. Experience processing massive data
  4. Rapid iteration engineering capability

See? These are exactly what large model training needs.

More importantly, High-Flyer has the ‘can afford it’ mentality to maintain a pure research team. DeepSeek doesn’t need to worry about short-term commercialization—just focus on technology. This ‘money is no object’ mindset actually lets them take risks and make long-term bets.

Compare to other domestic LLM companies:

  • Big tech (Alibaba, Baidu, ByteDance): Resource-rich but high internal coordination costs, long decision chains
  • Startups (Zhipu, Moonshot, MiniMax): Agile and fast but fundraising pressure forces constant proof of commercial value
  • DeepSeek: Has big tech resources with startup agility, plus no short-term profit pressure

This ‘atypical’ positioning may be DeepSeek’s key to success.

Of course, DeepSeek has challenges too.

First, unclear commercialization path. DeepSeek V3 was open source; V4 probably will be too. Open source builds influence, but how do you make money? This question will need answering eventually.

Second, intensifying competition. When DeepSeek V3 launched, domestic LLMs weren’t as crowded. Now Alibaba, ByteDance, and Baidu are iterating like crazy, diluting DeepSeek’s first-mover advantage.

Third, compute constraints. While High-Flyer has its own compute reserves, getting high-end GPUs is increasingly difficult. If training even larger models in the future, compute bottlenecks will become more obvious.

Back to V4 itself.

If the 35x speed rumors are true, this would be an important milestone for Chinese AI—not just because it’s fast, but because it proves ‘non-big-tech’ can make world-class large models.

For the entire industry, this is good news. More competition means more innovation, and ultimately all users benefit.

Can DeepSeek V4 become ‘China’s GPT-5’? We’ll know by month’s end.

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