Large models are increasingly used as teachers to train smaller, deployable versions. The approach shifts cost and complexity from inference deployment to the distillation phase.
Nanbeige4.3-33B is not just a benchmark: it is a teacher for a distillation pipeline. For self-hosted inference, the signal is clear: cost shifts from deployment to distillation. The 3B can run on modest hardware, but the teacher requires resources. ...
Nanbeige presents the 33B model as stronger than Qwen3.8-27B on several benchmarks, but the core message is the distillation pipeline: it will feed compact versions such as a 3B. For self-hosted deployments, a 33B is VRAM-heavy; the path to a 3B chan...
