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DiffuMamba: High-Throughput Diffusion LMs with Mamba Backbone
arXiv – CS AI|Vaibhav Singh, Oleksiy Ostapenko, Pierre-Andr\'e No\"el, Eugene Belilovsky, Torsten Scholak||16 views
🤖AI Summary
Researchers introduce DiffuMamba, a new diffusion language model using Mamba backbone architecture that achieves up to 8.2x higher inference throughput than Transformer-based models while maintaining comparable performance. The model demonstrates linear scaling with sequence length and represents a significant advancement in efficient AI text generation systems.
Key Takeaways
- →DiffuMamba achieves up to 8.2x higher inference throughput compared to Transformer-based diffusion models on long sequences.
- →The model uses bidirectional Mamba backbone with linear-time complexity instead of quadratic attention mechanisms.
- →Performance matches Transformer-based diffusion models across scales up to 1.3B parameters.
- →Cache-efficient block diffusion with Mamba mixers is the only strategy that scales linearly with sequence length.
- →The hybrid variant DiffuMamba-H with interleaved attention achieves 4.3x throughput improvement.
#diffusion-models#mamba-architecture#language-models#inference-optimization#transformer-alternative#linear-scaling#ai-efficiency
Read Original →via arXiv – CS AI
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