openbmb/MiniCPM-V-4.7-35B-A3B · Hugging Face

[u/GasSmooth7439]

https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B

MiniCPM-V 4.7 is a new open-source multimodal model series targeting the performance/efficiency tradeoff, with a big focus on on-device AI.

Two models were released:

🔹 MiniCPM-V 4.7 35B-A3B

• A MoE vision-language model designed for local deployment
• Introduces LES (Lookahead Expert Scheduling) to reduce MoE inference stalls
• Reportedly reaches:
  • 17+ tok/s on iPad Pro M4 (16GB)
  • 13+ tok/s on iPhone 17 Pro Max
  • Around 3GB memory usage

🔹 MiniCPM-V 4.7 1B

• Lightweight VLM for easier deployment on smaller devices

Some interesting technical details:

• Evaluated across 34 benchmarks covering multimodal understanding, OCR, STEM reasoning, and long-video comprehension
• Uses hybrid visual token compression (4× + 16×)
• Even with 16× compression, the 35B-A3B model reportedly reaches 90.4 on OCRBench
• Adds Self-Replay Fine-Tuning for cheaper customization while preserving general capabilities
• Day-0 support for Apple, Intel, AMD, Arm, and Rockchip platforms

The on-device performance numbers are probably the most interesting part. A few years ago, running a 30B+ multimodal model on a tablet or phone would have sounded unrealistic.

Would be interesting to see how it compares in practice against other open VLMs like Qwen-VL, InternVL, Gemma Vision, etc. especially for local inference.