Qwen 3 VL 30B-A3B vs Nemotron Nano 3 30B-A3B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Qwen 3 VL 30B-A3B | Nemotron Nano 3 30B-A3B |
|---|---|---|
| Parameters | 30B | 30B |
| Author | Alibaba | NVIDIA |
| License | Apache 2.0 | NVIDIA Open Model License |
| Context window | 0k | 0k |
| VRAM at Q4 | 19 GB | 19 GB |
| VRAM at Q5 | 23 GB | 23 GB |
| VRAM at Q8 | 35 GB | 35 GB |
| VRAM at FP16 | 62 GB | 62 GB |
| Use cases | vision, chat, general, moe, multilingual | chat, general, reasoning, moe |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
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 3 30B-A3B
NVIDIA's Mamba-2 + Transformer hybrid MoE with 3B active out of 30B total parameters. A native 1M-token context with roughly 4× the throughput of Nemotron 2. Strengths: Native 1M-token context window, Ultra-efficient MoE with only 3B active parameters, Roughly 4× throughput improvement over Nemotron 2, Permissive NVIDIA Open Model license.
How they compare
Qwen 3 VL 30B-A3B comes from Alibaba and Nemotron Nano 3 30B-A3B 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.
Qwen 3 VL 30B-A3B and Nemotron Nano 3 30B-A3B share the same 30B parameter class. Both need about 19 GB of VRAM at a Q4 quantization, so they fit the same GPU tier.
The two models target different sweet spots: Qwen 3 VL 30B-A3B is tuned for vision, chat, general, moe, multilingual, while Nemotron Nano 3 30B-A3B leans toward chat, general, reasoning, moe. Match the model to your dominant workload rather than to raw size.
For long-context work, Nemotron Nano 3 30B-A3B offers the bigger window (976k vs 256k 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 3 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 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 3 30B-A3B about 32 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 40 from Nemotron Nano 3 30B-A3B, scaling up to 100 and 100 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 3 30B-A3B to the card you actually own:
- On a 24 GB GPU: Qwen 3 VL 30B-A3B runs at Q5 (23 GB); Nemotron Nano 3 30B-A3B runs at Q5 (23 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 Nemotron Nano 3 30B-A3B for long-context work (up to 976k tokens).
- Pick Qwen 3 VL 30B-A3B if your workload is multilingual, vision.
- Pick Nemotron Nano 3 30B-A3B if your workload is reasoning.
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 3 30B-A3B?
The headline differences: both are 30B models; their context windows differ (256k vs 976k 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 3 30B-A3B 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 3 30B-A3B needs about 19 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.
Which license is safer for commercial use, Qwen 3 VL 30B-A3B or Nemotron Nano 3 30B-A3B?
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 3 30B-A3B?
Nemotron Nano 3 30B-A3B has the larger context window (976k vs 256k tokens), so it handles longer documents and codebases in a single prompt.