Nemotron Nano v2 VL 12B
NVIDIA's 12.6B enterprise VLM with strong DocVQA and ChartQA scores, tuned for professional document extraction workflows.
By NVIDIA · United States
Updated 2026-09-15
When to pick this model
- Enterprise document extraction and DocVQA pipelines
- Chart and table understanding at production scale
- Single-GPU multimodal deployments
- Long-context multimodal tasks up to 128k tokens
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 8 GB |
| Q5_K_M | 10 GB |
| Q8_0 | 14 GB |
| FP16 (no quantization) | 25 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Nemotron Nano v2 VL 12B fits an 8 GB consumer card at Q4_K_M (8 GB). Stepping up to Q8_0 nearly doubles the footprint to 14 GB, and unquantized FP16 weights take 25 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Nemotron Nano v2 VL 12B needs roughly 14 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 7 tokens/sec on entry-level GPUs, on the order of 22 tokens/sec on a mid-range card, and up to 60 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Nemotron Nano v2 VL 12B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Nemotron Nano v2 VL 12B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q4_K_M (8 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q5_K_M (10 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q8_0 (14 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q8_0 (14 GB used) |
| 32 GB | RTX 5090 | FP16 (25 GB used) |
Which hardware should you buy to run Nemotron Nano v2 VL 12B?
To run Nemotron Nano v2 VL 12B locally at Q4, you need ~8 GB for Q4 weights alone. Hardware option to compare: RTX 5060 Ti 16GB (ASUS Prime). Leave memory for the system and context; verify inference-engine support. A mini PC does not provide CUDA or macOS/MLX.
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Strengths
- Combined vision and text in a 12B footprint
- 128k context window
- Strong DocVQA and ChartQA benchmark scores
- NVIDIA Open Model license
Limitations
- Trails Qwen3-VL 30B on complex visual reasoning
- NVIDIA license terms differ from Apache or MIT
- Smaller community than Qwen or LLaVA families
Typical workloads
In our catalog grid, Nemotron Nano v2 VL 12B is filed under Enterprise OCR, Complex Documents — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the NVIDIA Open Model License license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Dense vision · 12.6B · Nemotron-Nano-v2 VL · 128k context
Training: NVIDIA Nemotron Nano v2 multimodal — text + images in 12B.
A focused enterprise VLM that punches above its weight on documents and charts — the right call when extraction is the job.
Quick start
Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.
Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.
ollama run nemotron3-v2:12bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- 30-day refund
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Frequently asked questions
How much VRAM does Nemotron Nano v2 VL 12B need?
At the recommended Q4_K_M quantization, Nemotron Nano v2 VL 12B needs about 8 GB of VRAM. Q8_0 takes 14 GB, and unquantized FP16 weights take 25 GB.
Can Nemotron Nano v2 VL 12B run without a GPU?
Yes — with roughly 14 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Nemotron Nano v2 VL 12B support?
Nemotron Nano v2 VL 12B supports a 125k-token context window (128,000 tokens).
Can I use Nemotron Nano v2 VL 12B commercially?
Nemotron Nano v2 VL 12B ships under the NVIDIA Open Model License license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Nemotron Nano v2 VL 12B on consumer hardware?
Our compatibility engine estimates on the order of 22 tokens/sec on a mid-range GPU and up to 60 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Nemotron Nano v2 VL 12B should I download first?
Start with Q4_K_M (8 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q4_K_M.