Core Insights - The fourth generation of the "MiniCPM" model, known as MiniCPM 4.0, has been released, featuring two parameter scales: 8B and 0.5B, achieving the best performance in its class [2][3] - MiniCPM 4.0-8B model utilizes a sparse attention mechanism, demonstrating performance comparable to Qwen-3-8B while requiring only 22% of the training cost [2][4] - The model achieves a remarkable inference speed of 600 Token/s, with a 220x acceleration in extreme scenarios, significantly enhancing long text processing capabilities [2][3] Performance and Architecture - MiniCPM 4.0 offers a 5x acceleration in long text inference speed compared to similar models like Qwen-3-8B and Llama-3-8B, with a maximum acceleration of 220x under memory-constrained conditions [3][4] - The model's architecture, InfLLMv2, reduces the sparsity from the industry standard of 40%-50% to just 5%, allowing for efficient long text calculations with only 1/10 of the computational load [4] - In terms of memory usage, MiniCPM 4.0-8B requires only 1/4 of the cache storage space compared to Qwen3-8B for 128K long text scenarios, indicating significant model compression and efficiency [4] Applications and Market Impact - Based on the 8B version, the company has fine-tuned two specific capability models for use as MCP Client and a research tool, MiniCPM4-Surve, which competes with Deep Research [5] - The MiniCPM series has achieved over 10 million downloads across all platforms, indicating strong market interest and adoption [5]