llama.cpp Adds Support for GLM-5.3-Flash Model
A new pull request (#27773) by developer timkhronos adds native support for GLM-5.3-Flash (GLM5-Next) to llama.cpp. This update enables users to run the multimodal open-weights model locally on standard consumer hardware. Integrating GLM-5.3-Flash into llama.cpp allows local AI enthusiasts to deploy frontier-class multimodal capabilities without relying on cloud APIs. This furthers the ecosystem for high-efficiency, privacy-preserving LLM inference on everyday consumer GPUs and CPUs. GLM-5.3-Flash features 320 billion total parameters with 18 billion active parameters per token, using a combination of sparse and linear attention. This architectural design reduces KV cache memory usage by 4.44× and attention computation by 3.01× compared to standard models.
## BACKGROUND
llama.cpp is a widely used open-source inference engine written in C/C++ that optimizes Large Language Models to run efficiently on local hardware. The GLM series, developed by Z.ai, represents a family of open-weights models designed for strong performance in text, coding, and visual tasks.