IBM SSM Transformer Speed Performance With Bamba Model
IBM Research, CMU, Princeton, and the University of Illinois created an open-source LLM that combines state-space model runtime speed with transformer expressiveness. IBM Granite 4.0 will receive key features
The transformer architecture that powers today’s massive language models has demonstrated an amazing capacity to produce text that is human-like
SSMs, transformers, and IBM SSM layers. IBM Research's first hybrid experiment, Bamba, can parse long sequences like a transformer and compute as fast as an SSM. It was recently made public
Bamba-9B can function at least twice as fast as transformers of equivalent size while preserving accuracy by dramatically reducing KV (key value) cache memory needs
An SSM keeps a compressed hidden state that compiles historical data, whereas a transformer attends to every word in the context window when generating a response
The Bamba team collaborated extensively with Red Hat to include the model into the “virtual” LLM, which has become the preferred open-source inference server for Large Language Models
Ganti stated that as vLLM adds additional support for SSM, it can reach one million tokens or more and operate up to five times faster than a transformer