GLM-5.3 reportedly helped triple inference throughput for GLM-5.3-Flash

Z.ai says the system reached production readiness less than two weeks after its first successful run. It credits detailed tests and measurements with guiding the optimization.

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TLDR

Z.ai says GLM-5.3 helped build and optimize the infrastructure running GLM-5.3-Flash, with end-to-end throughput tripling relative to the initial baseline. The company reports that the system went from its first successful run to production readiness in less than two weeks. It credits correctness tests, execution traces, microbenchmarks and end-to-end measurements with enabling targeted hypothesis testing rather than reliance on aggregate performance metrics alone.

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5 Sources, first seen 12h ago

GLM-5.3 reportedly helped triple inference throughput for GLM-5.3-Flash

Z.ai says the system reached production readiness less than two weeks after its first successful run. It credits detailed tests and measurements with guiding the optimization.

5 Sources, ,

5 Sources

@kimmonismusWhile the US is discussing a slowdown, China is currently putting full force into its RSI. Zai says GLM is taking early steps toward recursive self-improvement: AI helping build and optimize the infrastructure that runs AI. GLM-5.3 helped build the infrastructure running GLM-5.3-Flash, with engineers and the agent tripling inference throughput *in under two weeks*. They say this work will “directly change how the next generation of models is trained.”@AndrewCurran_From Z․ai's research blog post this morning documenting the early signs they are seeing of recursive self improvement. 'GLM is increasingly helping build AI itself'@rohanpaul_aiA brilliant post from the GLM-5.3 team on RSI (recursive self-improvement). GLM-5.3 was already used to optimize the infrastructure that runs GLM itself, including production kernel and concurrency fixes. It helped triple GLM-5.3-Flash throughput on 100,000+ accelerators. Their early self-improvement loop: the model improves its serving system, that system runs the model, and the engineering knowledge accumulates for the next optimization cycle. Engineers still set objectives and boundaries, while the agent handled analysis, hypotheses, code changes, and experiments. In one test, Prefill plus KV Transfer lagged Prefill alone by over 20%, and the agent traced the slowdown to the Python GIL, and releasing that lock cut the gap below 1% Another kernel change reached a 1.71x speedup over the prior version by eliminating repeated FP32 normalization and gating work.@menhguinZAI and Minimax will be the first publicly traded companies where recursive-self-improvement will materially affect earnings quarter-on-quarter.@menhguin1. most software companies simply cannot do this. an product/application-layer focused company has a lot of human bottlenecks. a foundational model lab has one main product that is complex and highly verifiable. 2. scaffolds can substantially improve efficiency and reduce training/inference costs 3. it is pretty likely Anthropic and OpenAI operate some version of this, but they're not publicly traded atm

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64.4K

5 Sources, first seen 12h ago

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12h ago

first seen 12h ago

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79 comments

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69 reposts

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Positive68.3%31.7%Negative

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12h ago

first seen 12h ago

TLDR

79 comments

152 saves

69 reposts

Sentiment

Positive68.3%31.7%Negative

5 Sources

Sentiment

Positive68.3%31.7%Negative

Summary

Sentiment

Positive68.3%31.7%Negative

Positive accounts welcomed China's full-force RSI push with GLM as shifting development pace, while negative replies questioned the lead or called the RSI framing overstated.

Based on 43 sentiment-bearing replies from 41 accounts across 2 conversations.

Summary

Positive accounts welcomed China's full-force RSI push with GLM as shifting development pace, while negative replies questioned the lead or called the RSI framing overstated.

Based on 43 sentiment-bearing replies from 41 accounts across 2 conversations.

Today's Rank

#19

Today's Rank

#19