24GB VRAM is enough to run MOSS-VL locally.@Open_MOSS We’ve released FP8 and NF4 versions for both MOSS-VL-Instruct and MOSS-VL-Realtime — covering image, video, and real-time streaming understanding. 🖼️ MOSS-VL-Instruct — image & video understanding Built for local inference, batch processing, and serving. 🎥 MOSS-VL-Realtime — understand video as it happens Built for cameras, livestreams, and continuous video analysis, with timestamp-aware streaming understanding that continuously responds as the video unfolds. Each model now comes in two quantization options: ⚡ FP8 — balances model capability and inference performance ⚡ NF4 — pushes VRAM usage even lower and can run stably within 24GB VRAM For real-time video understanding, we recommend MOSS-VL-Realtime-NF4 for more VRAM headroom and a more stable long-context experience. FP8 is a good fit when context length is more controlled. We also compared all four quantized checkpoints with their original BF16 models across selected image, video, and real-time streaming benchmarks. Overall, performance remains close to BF16 while VRAM usage drops substantially — making MOSS-VL much easier to deploy on 24GB consumer GPUs. All 4 checkpoints are available now ↓ 🖼️ MOSS-VL-Instruct-0708-FP8 🤗 Hugging Face: 🌐 ModelScope: 🖼️ MOSS-VL-Instruct-0708-NF4 🤗 Hugging Face: 🌐 ModelScope: 🎥 MOSS-VL-Realtime-FP8 🤗 Hugging Face: 🌐 ModelScope: 🎥 MOSS-VL-Realtime-NF4 🤗 Hugging Face: 🌐 ModelScope: