Lot of discussion on GLM 5.3-Flash built on Chinese domestic chips but my observation has been more focused on continuing trends in model architectures.
Full attention at every layer and one-token-at-a-time decoding are both becoming too expensive. So the architecture is changing on two important fronts among many other 1. cheaper ways to represent long context, and 2. cheaper ways to generate multiple tokens per decode step.
The direction has been building for a while. DeepSeek V4 uses hybrid compressed attention to avoid paying full attention cost everywhere. Kimi K3 mixes recurrent KDA layers with periodic MLA layers, keeping most of the sequence state compact while still recovering global context. GLM-5.3-Flash takes a similar idea in another direction. Most layers use linear attention, while periodic sparse-attention layers retrieve only the historical tokens that matter.
These architectures also pair naturally with speculative decoding and multi-token prediction. Once the expensive target model is designed to verify several candidate tokens efficiently, a smaller draft model or native MTP head can propose multiple tokens at once and the main model validates them in parallel. That reduces the number of full decode steps. GLM-5.3-Flash includes native MTP support, while Kimi K3 and DeepSeek-style architectures are also increasingly being served with speculative decoding techniques to reduce the cost of serving their very large target models.