PrismML Bonsai debuts 1-bit models

This is a serious compression story, not just a quantization stunt. If PrismML's kernels and benchmarks hold up in the wild, 1-bit weights could make private, low-cost inference viable on older GPUs and smaller servers.

• –The Mi50 example matters: 32GB VRAM is enough to make the 8B model practical for local serving, which broadens the audience beyond bleeding-edge NVIDIA rigs.
• –PrismML's fork of llama.cpp is the enabling layer here; without custom kernels, the model family would be much harder to use outside the lab.
• –The lack of vLLM support is the main production gap, because most teams want batching, serving controls, and ecosystem maturity more than raw novelty.
• –For commercial use, the pitch is deployment economics: smaller footprints mean cheaper hosting, easier privacy-preserving inference, and more room for concurrent users.
• –The caution flag is generalization: vendor benchmarks and demo setups do not guarantee the same quality or throughput once context length, batching, and real workloads show up.