HNHacker News·
暂不在当前实时榜单
Shapelearn Qwen 3.8 27B (13.1 GB VRAM)
Shapelearn's Qwen 3.8 27B model, requiring 13.1 GB VRAM, was evaluated on RTX Pro 6000 and RTX 4080 GPUs against competing quantization methods. Data compared various models like ByteShape, Unsloth, ISTA-DASLab, Bartowski, and AtomicChat for accuracy (Acc), tokens per second (TPS), and bits per weight (BPW). For instance, on the RTX Pro 6000, ByteShape's IQ4_XS-3.84bpw achieved 0.9963 accuracy and 90.42 TPS, while on the RTX 4080, it reached 0.9963 accuracy and 45.74 TPS.
时间与来源
时间显示为 UTC
显示时区:UTC
本地时区尚不可用,暂时显示 UTC。
收录当时偏移:UTC+02026年9月18日 05:00 UTC
- 收录
- 2026年9月18日 05:00
- 来源类型
- 未分类
本站未收录正文。
前往源站阅读 →