Mamba, leveraging efficient selective state space models, demonstrates remarkable speed performance, achieving 4-5 times higher inference throughput than traditional Transformer architectures of similar size.
Our experiments show that the SSM scan implementation is significantly faster than existing attention optimizations, providing up to 20-40 times performance improvement beyond sequence lengths of 2K.
#machine-learning #state-space-models #transformer-architecture #inference-performance #computational-efficiency
Collection
[
|
...
]