  We present Jamba, a new base large language model based on a novel hybrid
Transformer-Mamba mixture-of-experts (MoE) architecture. Specifically, Jamba
interleaves blocks of Transformer and Mamba layers, enjoying the benefits of
both model families. MoE is added in some of these layers to increase model
capacity while keeping active parameter usage manageable. This flexible
architecture allows resource- and objective-specific configurations. In the
particular configuration we have implemented, we end up with a powerful model
that fits in a single 80GB GPU. Built at large scale, Jamba provides high
throughput and small memory footprint compared to vanilla Transformers, and at
the same time state-of-the-art performance on standard language model
benchmarks and long-context evaluations. Remarkably, the model presents strong
results for up to 256K tokens context length. We study various architectural
decisions, such as how to combine Transformer and Mamba layers, and how to mix
experts, and show that some of them are crucial in large scale modeling. We
also describe several interesting properties of these architectures which the
training and evaluation of Jamba have revealed, and plan to release checkpoints
from various ablation runs, to encourage further exploration of this novel
architecture. We make the weights of our implementation of Jamba publicly
available under a permissive license.
