PrismML's Bonsai 2 27B claims near-lossless compression in a footprint nine times smaller, betting efficiency beats scale.
PrismML, a lab still under most radars, released Bonsai 2 27B, claiming near-lossless performance in a model nine times smaller than comparable offerings. The pitch is direct: cut inference costs and hardware demands without sacrificing output quality.
The launch comes as enterprises grow more cost-conscious about running large models in production, favoring efficiency gains over headline benchmark wins.
If the compression claims hold, smaller models like this undercut the assumption that bigger always wins, giving cost-sensitive operators a real alternative to frontier-scale deployments. Watch whether this pressures larger labs to compete on efficiency rather than just capability.
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