NVIDIA-Nemotron-Nano-9B-v2
Q4 GGUF download: bartowski/nvidia_NVIDIA-Nemotron-Nano-9B-v2-GGUF - 6.53 GB on disk, read 2026-09-27. VRAM figures above are estimates; this is the actual file size.
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
7.4
AA Index
Intelligence Index - NVIDIA-Nemotron-Nano-9B-v2 vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Benchmark data from Artificial Analysis · updated 2026-10-19.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $0.04 |
| Output (per 1M tokens) | $0.16 |
| Median speed (hosted API) | 195 tok/s |
Median across hosted providers, measured by Artificial Analysis - updated 2026-10-19. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| MMLU-Pro | 74.2 |
| GPQA Diamond | 57 |
| AIME | 69.7 |
| HLE | 4.9 |
| LiveCodeBench | 72.4 |
| IFBench | 27.6 |
| Terminal-Bench | 1.5 |
Source: Artificial Analysis component evals - updated 2026-10-19.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve nvidia/NVIDIA-Nemotron-Nano-9B-v2New to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
NVIDIA-Nemotron-Nano-9B-v2
NVIDIA-Nemotron-Nano-9B-v2 is a 8.9B-parameter other model from nvidia. It scores 7.4 on the Artificial Analysis Intelligence Index (coding 7.5). At Q4_K_M it needs roughly 5 GB of VRAM, placing it in the 8–12 GB GPU hardware tier.
Specifications
| Spec | Value |
|---|---|
| Parameters | 8.9B |
| Context length | 131K tokens |
| License | other |
| Modalities | text |
| Released | 2025-08-12 |
| Weights | nvidia/NVIDIA-Nemotron-Nano-9B-v2 |
Benchmarks
Artificial Analysis Intelligence Index - NVIDIA-Nemotron-Nano-9B-v2 vs. leading closed models:
| Model | Intelligence | Coding | GPQA |
|---|---|---|---|
| NVIDIA-Nemotron-Nano-9B-v2 | 13.2 | 7.5 | 55.7 |
| GPT-5.5 (xhigh) | 60.2 | 59.1 | 93.5 |
| Claude Opus 4.8 (max) | 61.4 | 56.7 | 92 |
| Gemini 3.1 Pro Preview | 57.2 | 55.5 | 94.1 |
| Grok 4.3 (high) | 53.2 | 41 | 90.1 |
Source: Artificial Analysis (2026-06-04).
VRAM requirements
| Quant | VRAM | Runs on |
|---|---|---|
| Q4_K_M | ~5 GB | RTX 4060, RTX 3060 8GB |
| Q5_K_M | ~6 GB | RTX 4060, RTX 3060 8GB |
| Q8_0 | ~10 GB | RTX 3060 12GB, RTX 4070 |
| FP16 | ~18 GB | RTX 3090, RTX 4090 |
VRAM is estimated from parameter count; MoE models still need all weights resident.
How to run
vLLM:
vllm serve nvidia/NVIDIA-Nemotron-Nano-9B-v2Popularity
NVIDIA-Nemotron-Nano-9B-v2 has 469,996 downloads in the last month on HuggingFace and 493 likes.
Frequently asked
Quick answers to common questions
How much VRAM does NVIDIA-Nemotron-Nano-9B-v2 need?
NVIDIA-Nemotron-Nano-9B-v2 with 8.9B parameters needs approximately 5 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is NVIDIA-Nemotron-Nano-9B-v2 better than other nvidia models?
NVIDIA-Nemotron-Nano-9B-v2 has 8.9B parameters with 131,072 context - a strong choice for general use.
What license is NVIDIA-Nemotron-Nano-9B-v2 under?
NVIDIA-Nemotron-Nano-9B-v2 is released under the other license, making it suitable for most commercial and personal projects.
What hardware runs NVIDIA-Nemotron-Nano-9B-v2 well?
With 8.9B parameters, NVIDIA-Nemotron-Nano-9B-v2 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for NVIDIA-Nemotron-Nano-9B-v2?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~6 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can NVIDIA-Nemotron-Nano-9B-v2's context window handle?
NVIDIA-Nemotron-Nano-9B-v2 supports a 131,072-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with NVIDIA-Nemotron-Nano-9B-v2?
NVIDIA-Nemotron-Nano-9B-v2 competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec