I Ran MiMo V2.6 Locally for 24 Hours... #1 Local AI is SCARY GOOD 🤯

I Ran MiMo V2.6 Locally for 24 Hours... #1 Local AI is SCARY GOOD 🤯

by xCreate · View the channel on PSI

Formal & Physical Sciences AI — Models & Research UYQArtificial intelligence (AI)UYQFGenerative artificial intelligence
🎙 xCreate 👥 27K 📅 September 23, 2026 ⏱ 35 min 👁 212 📄 expert opinion 🧭 2026-09-23
Available in: English (current) Français

Keywords

MiMolocal inferencebenchmark3D generationreinforcement learning

Summary

In this video, the creator runs Xiaomi’s MiMo V2.6 model locally for over 24 hours, showcasing its capabilities. He compares benchmark scores with larger models like GLM, noting that MiMo V2.6 performs similarly or better despite being much smaller. The video demonstrates various applications including 3D modeling in Blender, Python games, first-person shooters, nuclear explosion simulations, and photorealistic face rendering. The creator emphasizes the model’s speed (over 30 tokens per second) and its ability to generate complex interactive scenes. He also discusses its reinforcement learning training and notes some limitations like quantization artifacts. Overall, the video provides a comprehensive practical evaluation of MiMo V2.6’s local performance.

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Critical Evaluation

Value of the Information & Strength of the Argument

The value of this video lies in its extensive real-world testing of MiMo V2.6, offering tangible evidence of the model’s capabilities through numerous demonstrations. The argumentation is based on personal experience and benchmark comparisons, which is compelling but subjective. The creator systematically tests various tasks, from simple 3D scenes to complex games, and highlights strengths and weaknesses. However, the lack of controlled methodology and the potential for bias due to affiliate links reduce the rigor of the analysis.

Scientific Rigor, Source Quality, Title Accuracy

The creator references benchmarks but does not provide official sources. The description includes links to the Hugging Face model and an inferencer tool, which are directly relevant. The title accurately reflects the content, emphasizing the impressive performance. The community comments (if any) might show engagement, but none provided. Overall, the sources are limited but appropriate for a hands-on review.

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Title / Content Match

The title accurately reflects the content, which showcases MiMo V2.6's impressive local performance.

Quality & Reliability

7/10

Extensive hands-on testing with many demonstrations, but subjective evaluation and potential bias from affiliate links.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

External References

Contribution & Novelties

This video provides a unique hands-on evaluation of MiMo V2.6, showcasing its capabilities in various generative tasks and comparing its performance with much larger models. The creator’s systematic testing offers valuable insights into the model’s strengths and weaknesses, particularly regarding speed and efficiency. The video highlights the potential of local AI inference, making high-performance models accessible to individuals.

Pour aller plus loin :

  • Reinforcement Learning — Core training method for MiMo V2.6.
  • Quantization (machine learning) — Technique enabling efficient local execution.
  • WebGL — Technology used for browser-based 3D generations.
  • Blender (software) — Software automated by the model for 3D modeling.

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Radar Profile

The radar indicates high volume and quality of information, with a solid technical level. The reliability is slightly lower due to the subjective nature of the evaluation.

Reliability 7/10