Nemotron Nano 3 30B-A3B vs Qwen 3 30B-A3B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-09-15
| Spec | Nemotron Nano 3 30B-A3B | Qwen 3 30B-A3B |
|---|---|---|
| Parameters | 30B | 30B |
| Author | NVIDIA | Alibaba |
| License | NVIDIA Open Model License | Apache 2.0 |
| 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 | chat, general, reasoning, moe | chat, general, reasoning, multilingual, 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 30B-A3B has the safer license (Apache 2.0) compared to NVIDIA Open Model License.
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The two models at a glance
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.
About Qwen 3 30B-A3B
Alibaba's Qwen 3 MoE with 30B total and just 3B active parameters, supporting hybrid thinking mode. MMLU 81.4, AIME24 80.4, 100+ languages, Apache 2.0. Strengths: 3B active parameters keeps inference fast and cheap, MMLU 81.4 and AIME24 80.4 — strong on both general and reasoning, Apache 2.0, Hybrid thinking toggle per request.
How they compare
Nemotron Nano 3 30B-A3B comes from NVIDIA and Qwen 3 30B-A3B from Alibaba, they belong to the Nemotron and Qwen 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.
Nemotron Nano 3 30B-A3B and Qwen 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: Nemotron Nano 3 30B-A3B is tuned for chat, general, reasoning, moe, while Qwen 3 30B-A3B leans toward chat, general, reasoning, multilingual, 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 128k tokens).
Memory, quantization & throughput
Across quantization levels, Nemotron Nano 3 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 GB, while Qwen 3 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 GB. In practice Nemotron Nano 3 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, Nemotron Nano 3 30B-A3B needs roughly 32 GB of system RAM to run on CPU and Qwen 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 Nemotron Nano 3 30B-A3B and 40 from Qwen 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 Nemotron Nano 3 30B-A3B or Qwen 3 30B-A3B to the card you actually own:
- On a 24 GB GPU: Nemotron Nano 3 30B-A3B runs at Q5 (23 GB); Qwen 3 30B-A3B runs at Q5 (23 GB).
Benchmark scores
Reported benchmarks for Qwen 3 30B-A3B: MMLU (base) 81.38, AIME 2024 80.4.
Bottom line: which should you pick?
- Pick Qwen 3 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 30B-A3B if your workload is multilingual.
Which hardware should you buy to run Nemotron Nano 3 30B-A3B?
To run Nemotron Nano 3 30B-A3B locally at Q4, you need ~19 GB for Q4 weights alone. Hardware option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395). 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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Frequently asked questions
What is the difference between Nemotron Nano 3 30B-A3B and Qwen 3 30B-A3B?
The headline differences: both are 30B models; their context windows differ (976k vs 128k tokens); they ship under different licenses (NVIDIA Open Model License vs Apache 2.0). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Nemotron Nano 3 30B-A3B and Qwen 3 30B-A3B run on a 24 GB GPU?
At a Q4 quantization, Nemotron Nano 3 30B-A3B needs about 19 GB of VRAM and fits comfortably on a 24 GB GPU; Qwen 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, Nemotron Nano 3 30B-A3B or Qwen 3 30B-A3B?
Qwen 3 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, Nemotron Nano 3 30B-A3B or Qwen 3 30B-A3B?
Nemotron Nano 3 30B-A3B has the larger context window (976k vs 128k tokens), so it handles longer documents and codebases in a single prompt.