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GLM 5.2 Explained: 1M Context, MoE Efficiency, Sparse Attention & Cheap Inference
In this video, I break down GLM 5.2 and why it’s one of the most impressive open-weight releases so far, focusing on the architecture behind its low cost and strong coding performance. I cover its MIT-licensed 744B Mixture-of-Experts design with 384 experts (about 40B active per token), the 1M token context window, and how sparse attention with an “indexer” reduces attention cost. I explain “index share,” which reuses indexing across four layers for 2.9× fewer compute ops at full context, plus multi-token prediction that boosts acceptance rate ~20% for faster inference. I also discuss thinking effort modes, agentic coding results like 74.4% on Frontier SWE, pricing vs US models, self-hosting, data-sharing concerns, and limitations like being text-only.
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00:00 Why GLM 5.2 Matters
00:29 Efficiency Over Scale
01:02 MoE Architecture Explained
01:59 Million-Token Sparse Attention
04:07 Faster Output with Multi-Token Prediction
05:37 Benchmarks and Coding Strengths
06:29 Pricing Tradeoffs and Final Take Read More Prompt Engineering
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