Our chart this week covers intelligence density, a measure of how much model capability fits into each gigabyte of memory. PrismML’s new Bonsai 27B leads, scoring 0.530 per GB, ahead of its ternary sibling at 0.400 and more than ten times the density of several full-precision models on the chart.
The result comes from pushing low-bit representation through the whole model, rather than keeping expensive components at higher precision. The binary version compresses 27.8 billion parameters into just 3.9GB, small enough to run on a phone while retaining close to 90% of the benchmark performance of its full-precision parent.
If this approach works across a full range of uses, it could have real implications for cost, data centre capacity and global demand. A 27B model would typically require enterprise hardware; Bonsai turns it into a capability level that could run on a consumer graphics card.
“Intelligence density” is still PrismML’s own composite metric, not a settled scientific measure. Benchmark selection matters, and fitting model weights into RAM does not prove that long-running agents will work smoothly on a phone. But this suggests varied approaches to compression could extract far more performance than a blanket approach.