llama.cpp Adds Optimized Support for GLM-5 Multi-Token Prediction
Pull request #29928 for llama.cpp, created by developer pwilkin, adds optimized support for running GLM-5 Flash models using Multi-Token Prediction (MTP). This allows users to run GLM-5 Flash locally with native MTP acceleration. This enhancement enables local LLM enthusiasts to achieve higher inference throughput on GLM-5 models without relying on a separate draft model for speculative decoding. It expands the options for running high-efficiency, advanced models on hardware with limited resources. The pull request integrates the GLM5Next MTP code path and optimizes its tensor operations within llama.cpp. By predicting multiple future tokens per forward pass directly through native prediction heads, the model significantly reduces inference latency.
## BACKGROUND
llama.cpp is a popular open-source inference framework written in C/C++ designed to run large language models efficiently on local consumer hardware. Multi-Token Prediction (MTP) is an architectural approach where models predict several future tokens at once, effectively serving as an internal draft mechanism to accelerate text generation. GLM-5 Flash is a lightweight, high-efficiency model in the GLM model family optimized for fast execution.