I Ran GLM-5.3-Flash Locally For 24 Hours... Qwen 4 OBSOLETE?! 🤯

I Ran GLM-5.3-Flash Locally For 24 Hours... Qwen 4 OBSOLETE?! 🤯

by xCreate · View the channel on PSI

Formal & Physical Sciences AI — Models & Research UYQArtificial intelligence (AI)UYQFGenerative artificial intelligence
🎙 xCreate 👥 26K 📅 August 29, 2026 ⏱ 26 min 👁 27K 📄 review 🧭 2026-09-10
Available in: English (current) Français

Keywords

GLM-5.3local AIinferenceQwencoding

Summary

The video presents a detailed local run of GLM-5.3-Flash, a new open-source model from Zhipu, on a Mac Studio with M3 Ultra. The creator tests various capabilities, including coding, game generation, and human face fidelity, comparing with Qwen models. He explains the quirks of the model, such as the lack of non-thinking mode and the need for limited thinking to avoid long inference times. He demonstrates creating a solar system simulation, a GTA-like game, and a kids’ painting app, highlighting that with limited thinking and proper prompting, the model produces high-quality results. However, the model requires significant hardware (370 GB RAM) and has issues with code correctness, but it shows promise for local AI applications.

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Critical Evaluation

Value of the Information & Strength of the Argument

The video’s value lies in its practical, hands-on demonstration of running a large local model, providing insight into inference challenges and solutions. The argumentation is based on empirical testing, showing side-by-side comparisons and discussions of errors. However, the assessment is subjective and based on the creator’s own benchmarks, without external validation.

Scientific Rigor, Source Quality, Title Accuracy

The video cites no academic sources but references the model’s Hugging Face repository and associated tools. The title is appropriate, as it accurately describes the content and comparison. The description includes affiliate links, which are disclosed. The presentation is technically rigorous, but the lack of citations to external studies and the subjective nature of the tests limit scientific rigor.

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Title / Content Match

The title accurately reflects the content: the creator runs GLM-5.3-Flash locally and compares it to Qwen 4, claiming Qwen is obsolete, which is substantiated by the tests shown.

Quality & Reliability

7/10

Detailed hands-on testing of GLM-5.3-Flash with multiple benchmarks and real coding challenges, but evaluation is subjective and lacks external verification.

Chapters

Cited Sources

External References

Contribution & Novelties

The video offers a practical guide to running a large local model, introducing limited thinking to manage inference time, and provides qualitative comparisons, which is valuable for the local AI community.

Pour aller plus loin :

  • Mixture of Experts (MoE) — The architecture used in many modern LLMs, including Qwen and GLM, to reduce compute cost.
  • DeepSeek V4 architecture — The base architecture mentioned in the video, which GLM-5.3-Flash is based on.
  • Qwen — The series of models by Alibaba, used as comparison in the video.

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Radar Profile

The radar profile shows high scores in technical level and information quantity, but lower fiability global due to subjective testing and lack of external validation.

Reliability 6/10

💬 Très positif. Sur les 30 commentaires analysés, la majorité salue la qualité de la démonstration et la clarté des explications, avec quelques questions techniques et demandes de comparaisons supplémentaires.