SpikingBrain2.0: Brain-Inspired Foundation Models for Efficient Long-Context and Cross-Platform Inference

2026-04-24

AI Summary

This work addresses the high computational cost, low inference efficiency, and cross-platform deployment challenges of Transformer models in long-context scenarios by introducing SpikingBrain 2.0, a 5B-parameter foundation model that integrates sparse attention with spiking encoding. The model features dual-space sparse attention (DSSA), Sparse Softmax (MoBA), and Sparse Linear Attention (SSE), combined with a dual quantization pipeline leveraging INT8 spiking encoding and FP8 GPU acceleration, alongside a Transformer-to-Hybrid training protocol tailored for LLMs and VLMs. Experiments demonstrate a 10.13× reduction in first-token latency at 4M context length, enabling inference beyond 10M tokens; under FP8 mode, it achieves a 2.52× speedup for 250k-context inference, while on neuromorphic hardware, it reduces power consumption and chip area by 46.5% and 70.6%, respectively.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Scaling context length is reshaping large-model development, yet full-attention Transformers suffer from prohibitive computation and inference bottlenecks at long sequences. A key challenge is to design foundation models that maintain performance and long-context efficiency with minimal training overhead. We introduce SpikingBrain2.0 (SpB2.0), a 5B model that advances both architecture and training efficiency of its predecessor. Our contributions are two-fold. (1) Architectural Innovation: We propose Dual-Space Sparse Attention (DSSA), an inter-layer hybrid of Sparse Softmax Attention (MoBA) and Sparse Linear Attention (SSE), achieving an improved performance-efficiency trade-off for long-context modeling. SpB2.0 further supports dual quantization paths: INT8-Spiking coding enables sparse event-driven computation, while FP8 coding accelerates inference on modern GPUs. (2) Enhanced Training Strategy: We develop an optimized Transformer-to-Hybrid (T2H) pipeline with dual conversion paths for LLMs and VLMs using curated open-source data. Empirically, SpB2.0-5B and SpB2.0-VL-5B recover most of the base Transformer (Qwen3-4B) capability with under 7k A100 GPU hours. SpB2.0 achieves a 10.13x TTFT speedup at 4M context and supports over 10M tokens on 8 A100 GPUs under vLLM, where full-attention models exceed memory limits. It also demonstrates strong cross-platform compatibility, enabling FP8 GPU inference (2.52x speedup at 250k) and efficient neuromorphic execution (64.31% sparsity, with 70.6% and 46.5% area and power reduction at 500MHz). Overall, SpikingBrain2.0 provides a practical pathway for lightweight, multimodal, spiking foundation models, highlighting the potential of combining brain-inspired mechanisms with efficient architectures for resource-constrained and edge scenarios.

Problem

• long-context modeling
• efficient inference
• foundation models
• cross-platform compatibility
• spiking neural networks

Innovation

• Spiking Neural Networks
• Sparse Attention
• Long-Context Modeling
• Cross-Platform Inference
• Efficient Foundation Models