#MiniCPM5-2B

A 1.61 GB weight file that still hits ~70 tokens/s on a Tesla T4 shows how far small open models can be squeezed onto aging or low-power GPUs, no datacenter needed. Practitioners running local inference on modest hardware can pair it with ExLlamaV3 or TabbyAPI to serve on-device chat or agent backends. Compared with running the full-precision MiniCPM5-2B, this trade-off suits edge and consumer setups over maximum accuracy.

OpenBMB@OpenBMB11 sourcesSource

Mixed-precision quantization with a size target lets you hit an exact memory budget rather than settling for whatever Q4/Q5 yields, which matters on edge devices where every few hundred MB counts. Builders running local inference should compare these 1.14–1.46 GiB MiniCPM5-2B builds against their current preset and try FIT-GGUF on their own models. The models are on Hugging Face for direct download.

OpenBMB@OpenBMBSource

MiniCPM5-2B offers superior performance for edge deployment with its 2B parameters, outperforming larger models while being accessible for developers in the region.

面壁的MiniCPM5-2B在4B以下模型里AA榜单第一，还有512K上下文和智能体能力，马上要开源了，小模型也能干大事。