model compression

PrismML has released Ternary Bonsai 2 27B, a 27B-parameter reasoning model derived from Qwen3.8-27B that uses ternary weight compression to shrink to roughly 8.5 GB. The model supports a 262K-token context window, image understanding, tool calling, and thinks by default at 'xhigh' reasoning effort.

PrismML released Bonsai 2 27B, a compressed version of Alibaba's Qwen3.8 27B model that shrinks memory footprint by 9x to 10x down to 5.9GB. The startup claims 98% aggregate benchmark parity with the original, up from 95% in its first release, using a ternary weight compression technique.

NVIDIA has released Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super that reduces the model from 120.7B total/12.8B active parameters to 75.3B total/9.3B active parameters. According to NVIDIA, the model achieves approximately 2× higher server throughput on a single 8×B200 node and increases sustainable 1M-token single-H100 concurrency from 1 request to 8 requests while maintaining strong accuracy across benchmarks.