Navigate Select ESC Close

GLM 5.2: What Makes it So Special?

2026-06-21 Science & Technology
4.9k
202
24
Prompt Engineering
Prompt Engineering
245.0k subscribers

Unlock all features

FREE: Get instant access to 10 AI summaries, chats, or transcripts per day.

Description

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. My voice to text App: whryte.com Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 Let's Connect: 🦾 Discord: https://discord.com/invite/t4eYQRUcXB ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: https://www.patreon.com/PromptEngineering 💼Consulting: https://calendly.com/engineerprompt/consulting-call 📧 Business Contact: [email protected] Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 TIMESTAMP: 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

Top Comments (10)

@kylek29 2026-06-21

0:46 An example of necessity winning out. When the chip trade war started and sanctions came I assume they pivoted to efficiency knowing they may become constrained in the near term for getting additional high-compute capacity. The surprising thing is that they're being so open with it (a good thing overall). Compared to the capitalistic approach of the US where we just throw pallets of cash at it to brute force frontier leaps. Their (probably) subsidized budget; granted, a lot of the US companies are subsidized in various other ways like tax breaks or DoD contracts; and focus on efficiency allows them to undercut the rest of the market, especially since they _can_ build out capacity for inference compute.

6 2 replies
@SerenityMusicOasis 2026-06-21

I will definitely give it a try. Looks promising

2
@soylentpink7845 2026-06-21

Very clear & informative video, thank you! Always great when you explain those important concepts!

1 2 replies
@kenmeyer100 2026-06-21

I am all for open models and I really applaud the chinese, but the elephant in the room is: how are they going to finance these in the long run...!?

1
@alx8439 2026-06-22

I love glm models. They are prob the best we have in open space. But they are not perfect. I was laughing that today when 5.2 has failed during quite a trivial troubleshooting task, making a very wild guess where the actual issue was on the surface. 5.1 was making the same wild guesses. Still love them for building, but in the debugging they are quite shitty 😂

1
@jkyamog 2026-06-21

I can run this in Q4 variant on a dual Xeon with 384GB ram and 2 V100, really up to the brim. Around 50 t/s PP and 2 t/s TG, slow but can be still used as a background agent that does planning and review. Minimax m3 q6 is at 40 t/s PP and 5 t/s TG…. Testing and trying to see which I would replace Qwen 3.5 397B with.

0
@dr_hebaahmed 2026-06-24

great video as usual can you share please how you created these animations, visuals and handwritten text?

0
@proudindian3697 2026-06-22

Try with pi harness which seems works well

0
@mevech 2026-06-21

wow, you dont have to retrain your foundational model to use multi layer attentions?

0
@GNARGNARHEAD 2026-06-21

yeah, sounds impressive. it's great we have access to an actually capable model

0

Unlock the Data Inside
Turn Videos into Knowledge

  • Get FREE 10/day: transcripts, summaries, chats
  • Chat with videos, export text & PDF
  • $1 free API credit for RAG, chatbots & research

Free forever plan • All features unlocked

App screenshot