LongCat 2.0: The Beginning of the End of NVIDIA MOAT?

bycloud · 2026-08-08

LongCat 2.0: The Beginning of the End of NVIDIA MOAT?bycloud ▶ Play the video

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Why it earned a slot

The title screams clickbait, but the deep dive into LongCat 2.0’s sparse attention mechanism is actually high-quality engineering analysis. The real story here isn't just the model performance, but the fact that Meituan trained this entire stack on Chinese ASICs—that's a massive signal for the hardware market. If they can achieve these results without NVIDIA H100s, the 'GPU moat' argument gets a lot weaker. Bycloud does a good job connecting the architecture to the broader supply chain implications without getting too political.

The short version

NVIDIA's moat is leaking.

Why it matters

We are heading towards a post-scarcity compute era where specific hardware optimizations (like LongCat's sparse attention on ASICs) matter more than raw CUDA cores. This changes the economics of training for everyone.

My take

I've been saying it for a while: the future isn't just bigger models, it's smarter architectures that run on cheaper hardware. LongCat 2.0 proves you don't need the American chip embargo to play the game, and the sparse attention paper is worth a read for anyone optimizing inference costs.

How it connects

• The rise of alternative hardware (ASICs) for LLM training
• Sparse attention as a key efficiency technique
• Geopolitical impact on AI infrastructure supply chains

Bottom line

Don't design your stack assuming NVIDIA dominance; design for architectural flexibility.