The video evaluates GLM 5.2’s Multi-Token Prediction (MTP) decoding, demonstrating up to a 20% speed increase in token generation for structured coding tasks, especially with the Q4 quantized model, though gains vary and are limited for creative or complex outputs. Despite high memory demands and mixed results, the presenter highlights MTP’s potential for accelerating specific tasks and encourages further experimentation and optimization within the community.
The video explores the performance improvements of GLM 5.2 when using Multi-Token Prediction (MTP) decoding, a technique that has doubled speeds in other models. The presenter tests various configurations of GLM 5.2, including unquantized and quantized versions (Q4 and Q8), to evaluate how much faster MTP can make the model. Initial tests with coding tasks like C++ bubble sort show a 20% speed boost, reaching around 18 tokens per second compared to the baseline of 15.4 tokens per second. However, the speed gains vary depending on the task, with structured coding tasks benefiting more than creative writing or encyclopedic knowledge queries.
The presenter notes that MTP works by using a small additional layer to predict multiple tokens ahead, reducing the load on the main large model. However, this approach requires significant memory, especially for the unquantized 20GB MTP layer, which is comparable in size to some entire models. The quantized versions, particularly Q4 at 5.6GB, offer a more practical balance between memory usage and speed. Interestingly, higher quantization levels (like Q2 or Q3) do not benefit from MTP, and the presenter finds that the Q4 version often performs better than the Q8 or unquantized versions in terms of speed.
While MTP shows promise in speeding up token generation for coding tasks, it struggles with less structured outputs such as creative writing or longer text generation. For example, when generating poems or descriptive text, the token prediction accuracy drops, resulting in slower speeds or no significant improvement over the baseline. The presenter experiments with different token prediction step counts (from two up to ten) and finds that three steps generally offer the best balance between speed and accuracy, while more steps tend to slow down the process.
The video also highlights the variability in token generation speeds during live testing, with fluctuations between 12 and 29 tokens per second depending on the prompt and MTP configuration. Despite some disappointing results in certain scenarios, the presenter remains optimistic about the potential for further optimization and invites viewers to experiment with MTP themselves. They suggest that MTP might be most useful for shorter, structured tasks where quick responses are needed, while longer or more complex tasks might still benefit from traditional decoding methods.
In conclusion, the video provides a thorough and candid examination of GLM 5.2’s MTP decoding capabilities, showing modest but meaningful speed improvements in specific contexts. The presenter acknowledges the current limitations and memory demands of MTP but sees potential for future enhancements. They encourage the community to explore alternative speculative decoding techniques and share their findings, emphasizing that while MTP is not a universal solution, it represents an interesting step forward in accelerating large language model inference.