  Efficiently modeling sequences with infinite context length has been a
long-standing problem. Past works suffer from either the quadratic computation
complexity or the limited extrapolation ability on length generalization. In
this work, we present Samba, a simple hybrid architecture that layer-wise
combines Mamba, a selective State Space Model (SSM), with Sliding Window
Attention (SWA). Samba selectively compresses a given sequence into recurrent
hidden states while still maintaining the ability to precisely recall memories
with the attention mechanism. We scale Samba up to 3.8B parameters with 3.2T
training tokens and show that Samba substantially outperforms the
state-of-the-art models based on pure attention or SSMs on a wide range of
benchmarks. When trained on 4K length sequences, Samba can be efficiently
extrapolated to 256K context length with perfect memory recall and show
improved token predictions up to 1M context length. As a linear-time sequence
model, Samba enjoys a 3.73x higher throughput compared to Transformers with
grouped-query attention when processing user prompts of 128K length, and 3.64x
speedup when generating 64K tokens with unlimited streaming. A sample
implementation of Samba is publicly available in
https://github.com/microsoft/Samba.
