BEIJING, Oct. 15, 2026 /PRNewswire/ -- ModelBest and the OpenBMB community announced the release of MiniCPM-V 4.7, the latest addition to their open-source on-device multimodal model series. Available in 35B-A3B and 1B versions, the release brings advanced image, document and video understanding to edge devices with about 3GB of memory.

ModelBest describes MiniCPM-V 4.7 35B-A3B as the first open-source MoE multimodal model built to run smoothly on-device without compromising model quality. The model activates about 3 billion parameters per token and the most experts stay on SSD storage and load as needed, bringing large-model multimodal capability to IOS and Android. It handles general visual understanding, STEM problem solving, OCR, document and chart interpretation, and long-video understanding. According to ModelBest, the model pushes forward the intelligence-cost Pareto frontier for on-device multimodal AI by delivering strong overall performance with short inference time.

The 1B version builds on MiniCPM-V 4.6. Designed for lightweight edge devices, it supports general vision, OCR, chart reasoning and multi-image understanding. It can run smoothly on entry-level devices, bringing multimodal AI to more everyday hardware.

ModelBest's Lookahead Expert Scheduling (LES) predicts which experts the next layer will need and loads them while the current layer computes. In most scenarios, it prepares more than 80% of required experts in advance without additional training. The model's original router still makes the final expert selection, so outputs are identical with LES enabled or disabled.

LES works together with layer-wise quantization and contiguous expert storage. Combined, these optimizations increased average generation speed approximately 5x on an iPad Pro M4 with 16 GB of memory. The model reaches more than 17 tokens per second on that device and more than 13 tokens per second on the iPhone 17 Pro Max.

The model also supports both 4× and 16× visual token compression, allowing developers to prioritize detail or speed. At 16× compression, it retains strong performance, scoring 90.9 on MMBench and 90.4 on OCRBench.

A new Self-Replay Fine-Tuning method mixes the model's own high-quality responses into training data. This helps the model learn new tasks while retaining its general abilities, at a cost close to standard supervised fine-tuning.

MiniCPM-V 4.7 has Day-0 support from AMD, Apple, Arm, Houmo AI, Intel, Rockchip and NVIDIA. Through the FlagOS software stack, it also has Day-0 support for AI and edge chips from CIX Technology, Huawei, Hygon, Iluvatar CoreX, Kunlunxin, MetaX, Moore Threads, Sunrise, T-Head and TsingMicro. Model weights, inference code and fine-tuning code are publicly available for developers to download, evaluate and deploy.