Nemotron Nano v2 VL 12BvsQwen 2.5 VL 7B
Complete comparison between Nemotron Nano v2 VL 12B (12.6B parameters, NVIDIA) and Qwen 2.5 VL 7B (7B, Alibaba). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Nemotron Nano v2 VL 12B | Qwen 2.5 VL 7B |
|---|---|---|
| Settings | 12.6B | 7B |
| Family | Nemotron | Qwen |
| Author | NVIDIA | Alibaba |
| Origin | US | CN |
| License | NVIDIA Open Model License | Apache 2.0 |
| Context | 128,000 tokens | 128,000 tokens |
| Output | May 2025 | January 2025 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Nemotron Nano v2 VL 12B | Qwen 2.5 VL 7B |
|---|---|---|
| Q4_K_M (lightweight) | 8 GB | 6 GB |
| Q5_K_M (balanced) | 10 GB | 7 GB |
| Q8 (near-lossless) | 14 GB | 10 GB |
| FP16 (max quality) | 25 GB | 18 GB |
| CPU-only RAM | 14 GB | 10 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Nemotron Nano v2 VL 12B | Qwen 2.5 VL 7B |
|---|---|---|
| RTX 4090 (24 GB) | 60 tok/s · Q8 | 60 tok/s · FP16 |
| RTX 4080 (16 GB) | 22 tok/s · Q8 | 25 tok/s · Q8 |
| RTX 3060 12GB (12 GB) | 7 tok/s · Q5_K_M | 8 tok/s · Q8 |
| Apple M4 Pro (48 GB) (36 GB) | 22 tok/s · FP16 | 25 tok/s · FP16 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Nemotron Nano v2 VL 12B
12.6B enterprise VLM. Strong DocVQA/ChartQA. Professional document extraction.
- Vision + text in a 12B model
- 128k context
- NVIDIA Open Model License
- Worse than Qwen3-VL 30B for complex vision
Installation
Qwen 2.5 VL 7B
State-of-the-art 7B vision. Video >1h, bbox grounding, multilingual OCR. DocVQA 95.7.
- SOTA vision at 7B
- Excellent multilingual OCR
- Long video
- Apache 2.0
- Requires a VLM-compatible Ollama backend ≥ 0.5
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Nemotron Nano v2 VL 12B or Qwen 2.5 VL 7B?
On an RTX 4090, Nemotron Nano v2 VL 12B runs at Q8 (~60 tok/s), Qwen 2.5 VL 7B in FP16 (~60 tok/s). In pure throughput, Qwen 2.5 VL 7B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Qwen 2.5 VL 7B fits in 6 GB versus 8 GB for its rival—a 2 GB difference, significant if you're targeting an RTX 3060 or an 8 GB 4060 Ti.
Can these models be used in commercial production?
Nemotron Nano v2 VL 12B is under the NVIDIA Open Model License — check the restrictions, particularly the monthly user thresholds. Qwen 2.5 VL 7B is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Qwen 2.5 VL 7B (7B). More capable: Nemotron Nano v2 VL 12B (12.6B). If you are unsure, run the configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.