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torch 2.14 is used (<2.14 will cause torch compile exception), H200 with 140G or A100 with 80g x denote exceptions

Model AR Version Device ram vram time cost comment
Qwen/Qwen3.6-35B-A3B 0.15 A100 25GB 25GB ~200m
Qwen/Qwen3.6-35B-A3B 0.16 A100 30G 28G ~60m
nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 0.15 A100 >124G '_ExpertContainer' object has no attribute 'gate_proj'
nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 0.16 A100 21G 40G ~50m vllm inference issue, probably not related to AutoRound
Qwen3.8-Flash-Next 180B 0.15 H200 >256G group_size issue
Qwen3.8-Flash-Next 180B 0.16 H200 ~120G ~100G 9h 95GB ngrams are on cpu, full attention layer is much slower
zai-org/GLM-5.3-Flash-BF16 320B 0.15 H200 X 2 50 (100,120) ~8H
zai-org/GLM-5.3-Flash-BF16 320B 0.16 H200 X 2 40 (110,120) ~2.5h vllm inference issue, probably not related to AutoRound