Core Viewpoint - The release of MiniCPM 4.0 by Mianbi Intelligent marks a significant advancement in efficient large model technology, particularly in the context of sparse models for edge computing, enabling high-speed long text inference and broad application potential [2][8]. Group 1: Product Features - MiniCPM 4.0 introduces a new generation of "Mianbi Little Cannon" with two versions: an 8B sparse lightning version and a 0.5B model, showcasing a significant leap in edge performance [2][4]. - The 8B model achieves a 5x acceleration in long text inference speed compared to similar parameter models, with a maximum acceleration of 220x in extreme scenarios [4]. - The model features a high efficiency dual-frequency switching attention mechanism, optimizing performance for both long and short texts [4]. Group 2: Performance Metrics - The MiniCPM 4.0-8B model demonstrates performance comparable to Qwen-3-8B with only 22% of the training cost, surpassing Gemma-3-12B [4]. - The MiniCPM 4.0-0.5B model achieves a performance doubling with just 2.7% of the training cost compared to larger models, reaching a rapid inference speed of 600 tokens per second [4]. Group 3: Storage and Efficiency - The 8B model requires only 1/4 of the cache storage space compared to Qwen3-8B for 128K long text scenarios, with a quantized version achieving up to 90% model compression while maintaining robust performance [5]. - The advancements in speed and performance are coupled with significant model compression, alleviating computational pressure on edge devices [5]. Group 4: Application and Compatibility - The breakthroughs in edge long text processing open up new possibilities, with the 8B version fine-tuned for specific capabilities, including MCP Client and a research report tool [6]. - MiniCPM 4.0 is compatible with major chip manufacturers like Intel, Qualcomm, MTK, and Huawei Ascend, and can be deployed on various open-source frameworks [6]. Group 5: Future Outlook - The release of MiniCPM 4.0 is a milestone in Mianbi Intelligent's pursuit of efficient large models, aiming to enhance knowledge density and intelligence levels in future developments [8].