# Bamba: Inference-Efficient Hybrid Mamba2 Model

DevFeed: [Bamba: Inference-Efficient Hybrid Mamba2 Model](<https://devfeed.tech/articles/bamba-inference-efficient-hybrid-mamba2-model-7119.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/bamba>)

Author: LINSONG CHU; Divya Kumari; Tri Dao; Albert Gu; Raghu Ganti; Dakshi Agrawal; Mudhakar Srivatsa; Davis Wertheimer; Yu Chin Fabian Lim; Antoni Viros

Published: 2024-12-18T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Mamba](<https://devfeed.tech/topics/mamba.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [ibm](<https://devfeed.tech/topics/ibm.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [community](<https://devfeed.tech/tags/community.md>), [data](<https://devfeed.tech/tags/data.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [ibm](<https://devfeed.tech/tags/ibm.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [mamba](<https://devfeed.tech/tags/mamba.md>), [model](<https://devfeed.tech/tags/model.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [reproducibility](<https://devfeed.tech/tags/reproducibility.md>), [research](<https://devfeed.tech/tags/research.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

## AI overview

The article introduces Bamba-9B, an inference-efficient hybrid Mamba2 model trained by IBM, Princeton, CMU, and UIUC on open data. It reports higher throughput and lower latency than standard transformers in vLLM, and releases training resources, checkpoints, and reproducibility materials for community experimentation.

## Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.