canirunaimodel
NVIDIA · 9B · Dense
Hybrid Mamba2 architecture for reasoning
245.2K downloads
482 likes
2025-06 128K context
Use Cases
reasoning
| Quant | Bits | VRAM | Quality | Status |
|---|---|---|---|---|
| Q2_K | 2 | 3.4 GB | low | — |
| Q3_K_M | 3 | 4.5 GB | moderate | — |
| Q4_K_M | 4 | 5.1 GB | good | — |
| Q5_K_M | 5 | 6.3 GB | good | — |
| Q6_K | 6 | 7.4 GB | excellent | — |
| Q8_0 | 8 | 9.7 GB | excellent | — |
| F16 | 16 | 18.9 GB | lossless | — |
About this model
HF model card
Nemotron
NVIDIA
9B
Dense model for reasoning workloads.
Context
128K
Q4 VRAM
~5.1 GB
Dense Thinking
Nemotron Nano 9B v2 on your hardware
Hybrid Mamba2 architecture for reasoning. This page turns the Hugging Face model card into practical local-run numbers, so you can compare quantized VRAM, system RAM, and expected fit before downloading a large checkpoint.
Model shape
9B total parameters. Dense models use the whole network for each token.
Local fit target
Start with Q4_K_M: about 4.4 GB on disk and 5.1 GB VRAM before extra context and runtime overhead.
Best use cases
reasoning
License and source
Listed as NVIDIA Open from huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2.
Want the real verdict? Pick your GPU or edit the specs on this page and compare the quant table below.
Open HF repo