I don’t think so, the HF weights are bf16 which means 24GB + cache/overhead.

It sounds like marketing spin where the performance claims are based on BF16 and the “runs in 16GB” claim is on a totally different quantized version.

The guide describes it as projection although there is apparently an extra step: "A factorized coordinate lookup (X and Y matrices) attaches spatial location information directly to the input."

12B at int8 would take up 12G memory, or 75% of the system memory which technically fits within 16GB but the OS will not like that. EDIT: On my 18G memory MacBook Pro, LM Studio reports a "partial GPU offload" for the int8 MLX weights. Can't test because the `gemma_unified" architecture is NYI.

Yeah and it’s pretty memory efficient with only 8 attention layers so at int8 in 16GB ram maybe you still get 64k-128k context.

The part I hate though is that I’d bet none of the performance claims are based on int8.

Why do we care about bf16 benchmarks when no one will be using that with this model.