How much FASTER is GLM 5.2 with MTP 💪 | Local AI TEST

How much FASTER is GLM 5.2 with MTP 💪 | Local AI TEST

by xCreate

Applied Sciences & Engineering AI — Models & Research UYQArtificial intelligence (AI)UYQFGenerative artificial intelligence
🎙 xCreate 👥 26K 📅 June 25, 2026 ⏱ 19 min 👁 8K 📄 original study 🧭 2026-09-09
Available in: English (current) Français

Keywords

multi-token predictionspeculative decodingtokens per secondquantizationvLLM

Summary

The video presents a practical test of Multi-Token Prediction (MTP) decoding on the GLM 5.2 model running locally on an M3 Ultra Mac Studio. The creator compares token generation speeds with MTP disabled and enabled across various tasks: C++ coding, factual knowledge, 3D game code, creative writing, and more. Results are inconsistent: sometimes MTP gives a ~20% boost (e.g., C++ code), but often it yields marginal or even negative gains, especially for creative writing and longer contexts. The creator tests different quantization levels (Q4, Q8, unquantized) of the MTP layer, showing that the quantized Q4 version is faster than the unquantized one. He also experiments with the number of prediction steps (2,3,4,5,10), finding that 3 steps (the default) give the best speed, while higher steps degrade performance. He concludes that MTP may be beneficial for short generations under ~1000 tokens but becomes a hindrance for longer outputs, and that the implementation is still immature.

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Critical Evaluation

Value of the Information & Strength of the Argument

The video provides hands-on empirical data from real-time token generation tests. The argumentation is based on live demonstrations and concrete numbers, which adds credibility. However, the tests are not rigorous: they are single runs, not repeated under controlled conditions, and the metrics fluctuate. The creator acknowledges variability and speculates on causes like memory usage or prediction accuracy. The strength lies in the direct, authentic testing approach, but it lacks statistical robustness.

Scientific Rigor, Source Quality, Title Accuracy

The title is accurate: it asks how much faster GLM is with MTP, and the video answers that question with measurements. The sources cited are mostly links to tools and the model, but no academic papers. The creator refers to the MTP layer from Hugging Face and mentions ZAI (the model developer). The video does not rely on external citations; it is an original test. The technical explanations are decent but superficial. The video could benefit from more systematic methodology. The adequacy between title and content is good.

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Title / Content Match

The title accurately reflects the content, which is a speed comparison test with and without MTP.

Quality & Reliability

5/10

The video provides live empirical measurements of token speeds, but tests are singular runs without statistical rigor, and results are variable. The methodology is informal, and no external citations support the claims.

Chapters

Cited Sources

External References

Contribution & Novelties

The video offers an early practical test of MTP on GLM 5.2, providing real-world speed measurements across tasks and settings. It highlights the variability and suggests potential optimizations, such as using a quantized MTP layer and a limited generation length. This is valuable for the community as it gives a first impression of the feature’s practical utility.

Pour aller plus loin :

  • Accelerating Large Language Model Decoding with Speculative Sampling — The foundational paper on speculative decoding, which is closely related to MTP.
  • vLLM GitHub repository — A popular engine for high-throughput LLM inference, which supports speculative decoding techniques.
  • Multi-Token Prediction (MTP) — A technique where a small draft model predicts several future tokens simultaneously, potentially increasing decoding speed. No reliable public URL was identified at this time.

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Radar Profile

The radar profile shows moderate information quantity and quality, high technical level, and lower reliability due to informal methodology. This indicates a hands-on, technically detailed video with limited scientific rigor.

Reliability 5/10