Let's Run Ling-2.5 - TRILLION Param Local AI (Sibling of Kimi K2.5 & Qwen 3.5)

Let's Run Ling-2.5 - TRILLION Param Local AI (Sibling of Kimi K2.5 & Qwen 3.5)

by xCreate · View the channel on PSI

Applied Sciences & Engineering AI — News & Applications UYComputer scienceUYQArtificial intelligence (AI)
🎙 xCreate 👥 26K 📅 February 22, 2026 ⏱ 19 min 👁 4K 📄 expert opinion 🧭 2026-09-09
Available in: English (current) Français

Keywords

Ling-2.51T parameterslocal AIquantizationAnt Group

Summary

The video presents a practical test of Ling-2.5, a 1 trillion parameter language model released by Inclusion AI, the AI arm of Ant Group. The presenter runs the model locally on a Mac Studio with 512GB RAM using a 3.7-bit quantized version he created, claiming to be the first to provide a quantization for this model. He discusses the architecture, which includes linear attention and mixture of experts, and compares it to similar models like Kimi K2.5 and Qwen 3.5. The tests cover coding challenges (regex, newline handling, 3D visualizations) and logic puzzles (car wash, surgeon, trolley problem), with mixed results. Notably, the model correctly solved the regex and newline issues, but fell short on some logic questions. The presenter also tests the model’s personality and its ability to ground answers in web content. Performance metrics show a stable 15 tokens per second generation rate and memory usage of around 436-437GB. The video includes a brief sponsor segment at the end, and concludes with the presenter’s overall impression that the model has potential despite being a low-bit quant. There is a mention of a sponsor for an ‘intelligent agent cookbook’ near the end.

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Critical Evaluation

Value of the Information & Strength of the Argument

The video’s main value lies in its real-world demonstration of a very large model running locally, providing specific metrics like token generation speed and memory footprint. The argumentation is based on empirical tests, though the sample size is small and the tests are somewhat ad hoc. The presenter’s enthusiasm and transparency about the quantization process add credibility. However, the benchmarks cited (e.g., beating DeepSeek) are from the model’s own documentation and not independently verified, weakening the argument for superiority. The logical reasoning tests reveal inconsistencies, which are honestly reported. Overall, the information is useful for practitioners interested in running large models locally, but the conclusions are preliminary and subjective.

Scientific Rigor, Source Quality, Title Accuracy

The video cites the HuggingFace repository for the quantized model and the Inferencer app as tools, but does not provide external references for the claimed benchmarks. The sources in the description include product links (clearly marked as affiliate) and companion videos, which are not authoritative. The title accurately matches the content and does not overstate the scope. The presenter does not engage with peer-reviewed literature or independent benchmarks, relying instead on his own tests. There is a clear distinction between the model’s official claims and the presenter’s hands-on assessment, which is a positive sign of critical engagement. The sponsor segment is kept brief and does not influence the technical evaluation.

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Title / Content Match

The title accurately reflects the video's content: running Ling-2.5, a trillion-parameter model, locally on a Mac Studio. It appropriately highlights the model's scale and its relationship to Kimi K2.5 and Qwen 3.5.

Quality & Reliability

7/10

The video provides hands-on testing of a newly released 1T parameter model, including quantitative metrics (tokens/sec, memory usage) and qualitative assessments of coding and logic tasks. The presenter is clearly knowledgeable, but the evaluation is subjective and limited to a few examples, and the benchmarks cited are from the model's own claims without independent verification.

Key Moments

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

Cited Sources

Contribution & Novelties

This video provides an early hands-on evaluation of Ling-2.5, a newly released 1T parameter model, and is notable for being the first to offer a quantized version (3.7-bit) that runs on consumer-grade hardware. The presenter tests its capabilities on coding and logic tasks, offering insights into the model’s real-world performance beyond official benchmarks. The quantized model is made publicly available, which is a significant contribution for the local AI community.

Pour aller plus loin :

  • Mixture of experts — The architecture of Ling-2.5 incorporates MoE, which is relevant to understanding its parameter efficiency.
  • Ant Group — The company behind Ling-2.5, providing context on the corporate origins and financial scale.
  • Large language model — General background on the evolution and characteristics of LLMs, including parameter counts and quantization considerations.

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Radar Profile

The radar profile shows high scores for technical level and information quantity, reflecting the hands-on nature and detailed metrics. Quality and reliability are slightly lower due to the subjective testing methodology and reliance on the presenter's own quantized build, while the overall score is balanced by the novelty and practical value.

Reliability 6/10