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3236c6ee4a
Swap the annotation VLM from Qwen3.6-35B-A3B (sparse MoE, ~3B active) to Qwen3.6-27B (dense, 27B all-active). Per Scale's dense-captioning study, model capacity is the #1 lever and the dominant failure is visual grounding — both helped by ~9x more active params. Qwen3.6-27B is a vision-language model (vision encoder, image + video), same family so the chat template / video handling / enable_thinking=false flag are unchanged, and at 27B dense it still fits one H200 per server, so the two-parallel-server layout (TP=1, one per GPU) is preserved — no throughput-layout change, just a much stronger model. Kept: parallel_servers=2, num_gpus=2, max-model-len 32768 (the 32-frame embedded budget is ~10k tokens, well under), gpu-mem 0.8. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>