China's First Frontier Model?

TLDR;

Welcome! Today, we discuss China’s latest model, GLM-5.2, which is possibly China’s first frontier model, as well as many other news related to SpaceX, Anthropic, Google, SK Hynix, Microsoft, and more.

Enjoy!

RESEARCH

The Time has Come

Zhipu, one of China’s top AI Labs, has released a model that might set a precedent in AI, as it’s the first Chinese AI model, to my knowledge, to be actually competitive in raw performance with top US models.

Yes, I’m not saying intelligence-per-cost, I’m saying raw performance. Besides being better than anything Google has ever released for Large Language Models (LLMs), at least on benchmark metrics, it beats GPT-5.5 on several benchmarks.

Previous models like DeepSeek v4 showed promise, but they are unequivocally behind in most benchmarks. However, the latest string of Chinese AIs, models like this one or Kimi K2.7 Code, are putting up a very serious fight in raw performance while remaining overwhelmingly superior on a cost-efficient basis.

Architecturally, the model is very similar to everything we’ve seen before. But just like any other Chinese model, they use sparse attention, particularly the same DeepSeek uses (appropriately named DeepSeek Sparse Attention).

But what does that mean?

Most LLMs today work the same way; they take in a sequence of words and output the next. To do this, every word looks back at previous words looking for interesting attributes to attend to (e.g., in “The green cup”, ‘cup’ attends to ‘green’ to gain the attribute of “greenness”).

The question here is the following: should every word attend to all previous words?

For example, is the sequence “The green, uhm, ehhh, mmm, ah yes, cup, was…” does ‘cup’ have to attend to ‘uhm or ‘ehhh’, or should it simply focus on ‘green’?

In dense attention, what US models mostly do, we perform dense attention; each word pays attention to all, without distinction. This ensures that all relationships are found, but at the cost of a very large amount of required compute.

Sparse attention mechanisms actually ask that question first, whether all words matter, preidentify good candidates, and only then pay attention to those selected. This takes the form of an indexer that selects good candidate words that a particular word can attend to and sets a limit. If the indexer only lets you attend to 20 words and you have 1,000 previous words, only 20 will be attended to.

This allows the compute required to perform attention to remain constant across sequence length for every word with only a slight performance reduction, making it an irresistible alternative for Chinese Labs, which aren’t nearly as compute-rich as US Labs.

With regards to DeepSeek’s models, GLM-5.2 adds an extra compute-saving technique called IndexShare. While a DeepSeek model applies the indexer at every single model layer, GLM-5.2 applies the indexer only every few layers, avoiding this ‘candidate identification’ process from being executed more times than needed. In long sequences, this reduces compute by almost 3x.

If you want to better understand this indexer mechanism and…

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