nvidia/Nemotron-3.5-Embed-8B-BF16
main boardmodel
Visual document retrieval model from nvidia (HF pipeline tag; the card has no one-line description). Dense, 8.4B params. Context: 256K tokens (config). License: openmdw-1.1. Vendor-reported: "Nemotron-3.5-Embed-8B-BF16 achieves state-of-the-art results across multiple multilingual and multimodal retrieval benchmarks."
- Vendor-reported: Nemotron-3.5-Embed-8B-BF16 achieves state-of-the-art results across multiple multilingual and multimodal retrieval benchmarks.
- Org
- nvidia
- Released
- 2026-10-07
- Status
- main board
- License
- openmdw-1.1 (permissive)
- Parameters
- 8.4B
- Architecture
- dense · Mistral3Model
- Context
- 262,144 tokens
- Task
- visual-document-retrieval · embedding
- Signal
- 29 HF likes · 2629 downloads
Runs on
real quant files; need = weights + FP16 KV cache for 8K tokens + 1GB. A sizing calculation, not tested.
- Q4 ~7.0 GB: 8GB GPU (4060 / 3070)
- Q8 ~10.6 GB: 12GB GPU (RTX 3060), 16GB GPU (5060 Ti / 4060 Ti / 4080 / 5080), 16GB GPU (7800 XT / 9070 XT), Mac 16GB, Mac 24GB
- FP16 ~18.9 GB: 24GB GPU (3090 / 4090), 32GB GPU (5090), 48GB GPU (RTX 6000 Ada / A6000), 96GB GPU (RTX Pro 6000 Blackwell), 1× DGX Spark (128GB), 2× DGX Spark (256GB), 24GB GPU (7900 XTX), Strix Halo 128GB unified (~96GB usable), Arc B60 24GB, Arc B70 32GB, Mac 36GB, Mac 48GB, Mac 64GB (M1 Max), Mac 96GB, Mac 128GB, Mac 192GB, Mac 256GB, Mac 512GB, CPU only, 32GB RAM (slow), CPU only, 64GB RAM (slow), CPU only, 128GB RAM (slow), CPU only, 256GB RAM (slow)
Links
First published here 2026-10-07 · updated 2026-10-07 · markdown · JSON