jietang

@jietang

Professor @ Tsinghua, Founder of Z.ai. AGI, LLM. “The value of a man should be seen in what he gives and not in what he is able to receive.”―Einstein

Beijing

keg.cs.tsinghua.edu.cn/jietang/

2008年5月からXを利用しています

474 フォロー中

7.9万フォロワー

jietang
@jietang
11月4日
GLM-5.3 is on T-Systems now....
Carol Lin
@CarolGLMs
11月2日
GLM-5.3 is now in production on T-Systems’ Industrial AI Cloud in Munich! 🇩🇪 Proud of our team, and a big thank you to the T-Systems team for making this happen! #Zai #GLM #TSystems #EnterpriseAI #SovereignAI docs.llmhub.t-systems.net
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jietang
@jietang
10月6日
GLM-5.3 is on Amazon Bedrock! docs.aws.amazon.com/bedrock/latest…
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jietang
@jietang
9月17日
Two weeks. That's how long it took to go from GLM-5.3-Flash's first run on domestic accelerators to serving all of its production traffic, with 3.2× end-to-end throughput along the way. What I keep thinking about is who did much of the work: an Infra Agent powered by GLM-5.3.
Z.ai
@Zai_org
9月17日
We’re sharing how GLM-5.3 helped build and optimize the inference infrastructure serving GLM-5.3-Flash. The system went from its first successful run to production readiness in less than two weeks, with end-to-end throughput tripling relative to the initial baseline. The key
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jietang
@jietang
9月10日
Add oil and Happy Teacher's Day
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jietang
@jietang
9月10日
Are you sure? Finding the optimal model size is tricky: data volume, active parameter count, the number of environments, and the target inference cost. Model performance also depends on many other factors, each introducing its own variability.
Charlie O'Neill
@oneill_c
9月9日
Fable is probably ~2-2.5T parameters, not 10T. Kimi K3 is 2.8T params, trained on maybe 20–30k Blackwell-equivalents. It lands within spitting distance of Fable 5 in terms of capabilities (5, not 5.1). Anthropic has far more compute than Moonshot, better rl environments, better
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