GLM 4.7 Flash: Performance Degradation with Extended Contexts

A user has encountered a performance issue with the GLM 4.7 Flash model running on LM Studio. Specifically, the processing speed, initially at 150 tokens per second with Q6 quantization, dropped dramatically after exceeding 10,000 tokens. This behavior was observed despite using the recommended settings and updating software components, including Unsloth quantization and the llama.cpp runtime.

Possible Solutions and Alternatives

A patch for ik_llama.cpp has been identified that promises to reduce this slowdown. However, the user expressed difficulty in compiling the patch. It is being considered whether other implementations of the model, such as the one in vllm, can avoid this performance issue. The issue raised highlights the importance of optimizing engines to handle large contexts without compromising inference speed.

Large language models (LLMs) are increasingly prevalent in various sectors, thanks to their ability to generate text, translate languages, and answer questions comprehensively and informatively. However, the performance of these models can vary significantly depending on the hardware, software, and optimizations used.