English(EN) SAM 3.1 Quantized to INT8 and INT4

SAM 3.1 量化至 INT8 和 INT4 可显著节省 VRAM

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-31 12:02

新发布的 Segment Anything Model (SAM) 3.1 的量化版本提供了 INT8 和 INT4 格式。这些量化版本显著减小了模型尺寸，其中 INT4 版本比其 fp16 对等版本小近 40%，从而节省了大量 VRAM。虽然推理速度提升不大，但掩码质量与原始模型几乎相同。 AI

影响 SAM 3.1 INT4 等量化模型可以在 VRAM 有限的设备上实现更广泛的部署，从而可能提高 AI 图像分割任务的可访问性。

排序理由现有模型的量化版本发布。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 现有模型的量化版本发布。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: model release, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 69 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

r/StableDiffusion TIER_2 English(EN) · /u/External_Quarter · 2026-07-31 12:02

SAM 3.1 量化至 INT8 和 INT4

<div class="md"><p>Compatible with native loaders. INT4 is almost 40% smaller than ComfyOrg's fp16 checkpoint, i.e. about 600 MB in VRAM savings. Mask quality is nearly identical. Inference speed is only marginally improved, but SAM is already quite fast.</p> </div…