Qwen 3 VL 30B-A3B vs Nemotron Nano v2 VL 12B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Qwen 3 VL 30B-A3B | Nemotron Nano v2 VL 12B |
|---|---|---|
| Parameters | 30B | 12.6B |
| Author | Alibaba | NVIDIA |
| License | Apache 2.0 | NVIDIA Open Model License |
| Context window | 0k | 0k |
| VRAM at Q4 | 19 GB | 8 GB |
| VRAM at Q5 | 23 GB | 10 GB |
| VRAM at Q8 | 35 GB | 14 GB |
| VRAM at FP16 | 62 GB | 25 GB |
| Use cases | vision, chat, general, moe, multilingual | vision, chat |
Verdict
Qwen 3 VL 30B-A3B is significantly larger (30B vs 12.6B), so expect higher quality but heavier VRAM and slower throughput.
For unambiguous commercial use, Qwen 3 VL 30B-A3B has the safer license (Apache 2.0) compared to NVIDIA Open Model License.
The two models at a glance
About Qwen 3 VL 30B-A3B
Qwen 3 VL's sweet spot: a 30B MoE with 3B active parameters and 256k context. Delivers most of the 235B's quality at a fraction of the hardware cost. Strengths: Around 19 GB VRAM at Q4 — fits a single 24 GB card, Native 262k multimodal context, Efficient MoE with only 3B active parameters, Apache 2.0.
About Nemotron Nano v2 VL 12B
NVIDIA's 12.6B enterprise VLM with strong DocVQA and ChartQA scores, tuned for professional document extraction workflows. Strengths: Combined vision and text in a 12B footprint, 128k context window, Strong DocVQA and ChartQA benchmark scores, NVIDIA Open Model license.
How they compare
Qwen 3 VL 30B-A3B comes from Alibaba and Nemotron Nano v2 VL 12B from NVIDIA, they belong to the Qwen and Nemotron families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.
At 30B vs 12.6B parameters, Qwen 3 VL 30B-A3B is the larger of the two. At Q4, Nemotron Nano v2 VL 12B fits in about 8 GB of VRAM versus 19 GB for the other — a 11 GB difference that matters on consumer GPUs.
The two models target different sweet spots: Qwen 3 VL 30B-A3B is tuned for vision, chat, general, moe, multilingual, while Nemotron Nano v2 VL 12B leans toward vision, chat. Match the model to your dominant workload rather than to raw size.
On a typical mid-range GPU, Qwen 3 VL 30B-A3B pushes roughly 40 tokens/sec versus 22, so it is the more responsive choice for interactive or high-volume use. For long-context work, Qwen 3 VL 30B-A3B offers the bigger window (256k vs 125k tokens).
Memory, quantization & throughput
Across quantization levels, Qwen 3 VL 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 GB, while Nemotron Nano v2 VL 12B requires Q4 ≈ 8 GB, Q5 ≈ 10 GB, Q8 ≈ 14 GB, FP16 ≈ 25 GB. In practice Qwen 3 VL 30B-A3B wants a 24 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.
Without a GPU, Qwen 3 VL 30B-A3B needs roughly 32 GB of system RAM to run on CPU and Nemotron Nano v2 VL 12B about 14 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 40 tokens/sec from Qwen 3 VL 30B-A3B and 22 from Nemotron Nano v2 VL 12B, scaling up to 100 and 60 tokens/sec on high-end hardware.
Which fits your GPU
Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Qwen 3 VL 30B-A3B or Nemotron Nano v2 VL 12B to the card you actually own:
- On a 8 GB GPU: Qwen 3 VL 30B-A3B does not fit; Nemotron Nano v2 VL 12B runs at Q4 (8 GB).
- On a 12 GB GPU: Qwen 3 VL 30B-A3B does not fit; Nemotron Nano v2 VL 12B runs at Q5 (10 GB).
- On a 16 GB GPU: Qwen 3 VL 30B-A3B does not fit; Nemotron Nano v2 VL 12B runs at Q8 (14 GB).
- On a 24 GB GPU: Qwen 3 VL 30B-A3B runs at Q5 (23 GB); Nemotron Nano v2 VL 12B runs at Q8 (14 GB).
Bottom line: which should you pick?
- Pick Qwen 3 VL 30B-A3B if you need a permissive (Apache 2.0) license for commercial deployment.
- Pick Qwen 3 VL 30B-A3B for long-context work (up to 256k tokens).
- Pick Nemotron Nano v2 VL 12B for lower VRAM and faster inference; pick Qwen 3 VL 30B-A3B for maximum headline quality.
- Pick Qwen 3 VL 30B-A3B if your workload is general, moe, multilingual.
Which GPU should you buy to run Qwen 3 VL 30B-A3B?
To run Qwen 3 VL 30B-A3B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Frequently asked questions
What is the difference between Qwen 3 VL 30B-A3B and Nemotron Nano v2 VL 12B?
The headline differences: Qwen 3 VL 30B-A3B is a 30B model and Nemotron Nano v2 VL 12B is 12.6B; their context windows differ (256k vs 125k tokens); they ship under different licenses (Apache 2.0 vs NVIDIA Open Model License). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Qwen 3 VL 30B-A3B and Nemotron Nano v2 VL 12B run on a 24 GB GPU?
At a Q4 quantization, Qwen 3 VL 30B-A3B needs about 19 GB of VRAM and fits comfortably on a 24 GB GPU; Nemotron Nano v2 VL 12B needs about 8 GB and fits comfortably on a 24 GB GPU. Nemotron Nano v2 VL 12B is the lighter option for tight VRAM budgets.
Which is faster, Qwen 3 VL 30B-A3B or Nemotron Nano v2 VL 12B?
Nemotron Nano v2 VL 12B is the smaller model (12.6B vs 30B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
Which license is safer for commercial use, Qwen 3 VL 30B-A3B or Nemotron Nano v2 VL 12B?
Qwen 3 VL 30B-A3B ships under Apache 2.0, a permissive license with no usage restrictions, whereas the other is under NVIDIA Open Model License — check its terms before commercial deployment.
Which has the longer context window, Qwen 3 VL 30B-A3B or Nemotron Nano v2 VL 12B?
Qwen 3 VL 30B-A3B has the larger context window (256k vs 125k tokens), so it handles longer documents and codebases in a single prompt.