Qwen 3 VL 30B-A3BvsNemotron Nano 3 30B-A3B
Complete comparison between Qwen 3 VL 30B-A3B (30B parameters, Alibaba) and Nemotron Nano 3 30B-A3B (30B, NVIDIA). 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 | Qwen 3 VL 30B-A3B | Nemotron Nano 3 30B-A3B |
|---|---|---|
| Settings | 30B | 30B |
| Family | Qwen | Nemotron |
| Author | Alibaba | NVIDIA |
| Origin | CN | US |
| License | Apache 2.0 | NVIDIA Open Model License |
| Context | 262,144 tokens | 1,000,000 tokens |
| Output | May 2025 | May 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 | Qwen 3 VL 30B-A3B | Nemotron Nano 3 30B-A3B |
|---|---|---|
| Q4_K_M (lightweight) | 19 GB | 19 GB |
| Q5_K_M (balanced) | 23 GB | 23 GB |
| Q8 (near-lossless) | 35 GB | 35 GB |
| FP16 (max quality) | 62 GB | 62 GB |
| CPU-only RAM | 32 GB | 32 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 | Qwen 3 VL 30B-A3B | Nemotron Nano 3 30B-A3B |
|---|---|---|
| RTX 4090 (24 GB) | 100 tok/s · Q5_K_M | 100 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | ✗ too heavy | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 40 tok/s · Q8 | 40 tok/s · Q8 |
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.
Your private, free ChatGPT on your machine in 1 hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- 30-day money-back guarantee
Strengths and weaknesses
Qwen 3 VL 30B-A3B
Vision MoE with 30B/3B active. Vision sweet spot Qwen 3. 256k ctx.
- 262k multimodal context
- 19 GB VRAM Q4
- Efficient MoE
- Apache 2.0
- Worse vision than the 235B
- Fewer fine-tunes than Qwen2-VL
Installation
Nemotron Nano 3 30B-A3B
Hybrid Mamba-2 + Transformer MoE with 30B/3B active parameters. 1M ctx. 4× the throughput of Nemotron 2.
- 1 million tokens of native context
- Ultra-efficient MoE (3B active)
- NVIDIA Open Model License
- 1M ctx uses a lot of VRAM if fully utilized
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Qwen 3 VL 30B-A3B or Nemotron Nano 3 30B-A3B?
On an RTX 4090, Qwen 3 VL 30B-A3B runs at Q5_K_M (~100 tok/s), Nemotron Nano 3 30B-A3B in Q5_K_M (~100 tok/s). In pure throughput, Nemotron Nano 3 30B-A3B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
Both require the same VRAM in Q4 (19 GB). The choice then comes down to the license or perceived quality.
Can these models be used in commercial production?
Qwen 3 VL 30B-A3B is under Apache 2.0 — free for commercial use. Nemotron Nano 3 30B-A3B is under the NVIDIA Open Model License—also check the terms. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Qwen 3 VL 30B-A3B (30B). More capable: Qwen 3 VL 30B-A3B (30B). If you're unsure, run it 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.