MODEL PROFILE

MiniCPM-SALA

openbmb · MiniCPM-SALA

> [!NOTE] > ### 🏆 2026 Sparse Operator Acceleration & Race (SOAR) is Now Live! > > **"The MiniCPM-SALA architecture is just the beginning. Realizing its full potential requires deep system-level synergy and cross-layer compilation optimization."** > > In collaboration with **SGLang** and **NVIDIA**, OpenBMB invites global geeks to push the boundaries of 9B-scale, 1M-token inference on **NVIDIA 6000D**. > > 💰 **Prize Pool: >$100,000 USD** (🥇 Top Prize: **$89,000**) | 🚀 **Challenge:** Single & Multi-batch Optimization > > 👉 **Click Here to Join the Race @ soar.openbmb.cn** MiniCPM-SALA (Sparse Attention and Linear Attention) is the first large-scale hybrid model effectively integrating sparse and linear attention for million-token context modeling ✅ Innovative Hybrid Architecture: Synergizes 25% Sparse Attention (InfLLM-v2) for high-fidelity long context modeling with 75% Linear Attention (Lightning Attention) for global efficiency.

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/31 11:09:47
OVERVIEW

Model overview

> [!NOTE] > ### 🏆 2026 Sparse Operator Acceleration & Race (SOAR) is Now Live! > > **"The MiniCPM-SALA architecture is just the beginning. Realizing its full potential requires deep system-level synergy and cross-layer compilation optimization."** > > In collaboration with **SGLang** and **NVIDIA**, OpenBMB invites global geeks to push the boundaries of 9B-scale, 1M-token inference on **NVIDIA 6000D**. > > 💰 **Prize Pool: >$100,000 USD** (🥇 Top Prize: **$89,000**) | 🚀 **Challenge:** Single & Multi-batch Optimization > > 👉 **Click Here to Join the Race @ soar.openbmb.cn** MiniCPM-SALA (Sparse Attention and Linear Attention) is the first large-scale hybrid model effectively integrating sparse and linear attention for million-token context modeling ✅ Innovative Hybrid Architecture: Synergizes 25% Sparse Attention (InfLLM-v2) for high-fidelity long context modeling with 75% Linear Attention (Lightning Attention) for global efficiency.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。