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Open weightsOpenBMB/10 trustVerified
MiniCPM-4 8B logo

MiniCPM-4 8B

8B on-device LLM with trainable sparse attention for hybrid reasoning on the edge.

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About

MiniCPM4-8B is the flagship of OpenBMB's MiniCPM4 series, released June 6, 2025, and designed explicitly for end-side devices. It packs 8 billion parameters trained on 8 trillion tokens, with systematic efficiency innovations across architecture, training data, training algorithms, and inference systems.

The headline innovation is InfLLM-v2, a trainable sparse attention mechanism where each token computes relevance with less than 5 percent of tokens in 128K long-context processing, dramatically cutting the cost of long-text inference. Combined with hybrid reasoning and frequency-ranked speculative decoding, the series claims over 5x generation acceleration on typical end-side chips while maintaining best-in-class performance at its scale.

The model is open-weight under the Apache 2.0 license, runs on Hugging Face transformers, vLLM, SGLang, llama.cpp, Ollama, and CPM.cu, and sits in a family that also includes a 0.5B version, a survey-paper agent variant, and an MCP-tool-calling variant.

Specs

Context window

Pricing

Free quota

API access

Weights

Open weights

Released

June 6, 2025

Benchmarks

Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.

Use cases

on-device-aiedge-inferencelong-context

Trust score — 8/10

/10 trustVerified

Scored 8/10 (verified tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 118; Trending score 82/100 from researcher assessment.

More in the minicpm family

Sources