Nemotron 3 33B vs Qwen 3 30B-A3B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Nemotron 3 33B | Qwen 3 30B-A3B |
|---|---|---|
| Parameters | 33B | 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 | 66 GB | 62 GB |
| Use cases | chat, code, reasoning | 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.
The two models at a glance
About Nemotron 3 33B
NVIDIA's dense 33B model targeting balanced chat, code, and reasoning workloads. Fits a single RTX 4090 at Q4 with a 128k context window. Strengths: Dense 33B sized to saturate a 24GB consumer GPU at Q4, 128k context handles long codebases and reports, RLHF tuned for reasoning and code, not just chat, Open weights backed by NVIDIA's research stack.
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 3 33B 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.
At 33B vs 30B parameters, Nemotron 3 33B is the larger of the two. 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 3 33B is tuned for chat, code, reasoning, while Qwen 3 30B-A3B leans toward chat, general, reasoning, multilingual, moe. Match the model to your dominant workload rather than to raw size.
On a typical mid-range GPU, Qwen 3 30B-A3B pushes roughly 40 tokens/sec versus 12, so it is the more responsive choice for interactive or high-volume use. For long-context work, Qwen 3 30B-A3B offers the bigger window (128k vs 125k tokens).
Memory, quantization & throughput
Across quantization levels, Nemotron 3 33B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 66 GB, while Qwen 3 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 GB. In practice Nemotron 3 33B 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 3 33B needs roughly 43 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 12 tokens/sec from Nemotron 3 33B and 40 from Qwen 3 30B-A3B, scaling up to 30 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 3 33B or Qwen 3 30B-A3B to the card you actually own:
- On a 24 GB GPU: Nemotron 3 33B 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 Qwen 3 30B-A3B for long-context work (up to 128k tokens).
- Pick Qwen 3 30B-A3B for lower VRAM and faster inference; pick Nemotron 3 33B for maximum headline quality.
- Pick Nemotron 3 33B if your workload is code.
- Pick Qwen 3 30B-A3B if your workload is general, moe, multilingual.
Which GPU should you buy to run Nemotron 3 33B?
To run Nemotron 3 33B 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 Nemotron 3 33B and Qwen 3 30B-A3B?
The headline differences: Nemotron 3 33B is a 33B model and Qwen 3 30B-A3B is 30B; their context windows differ (125k 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 3 33B and Qwen 3 30B-A3B run on a 24 GB GPU?
At a Q4 quantization, Nemotron 3 33B 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 is faster, Nemotron 3 33B or Qwen 3 30B-A3B?
Qwen 3 30B-A3B is the smaller model (30B vs 33B), 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, Nemotron 3 33B 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 3 33B or Qwen 3 30B-A3B?
Qwen 3 30B-A3B has the larger context window (128k vs 125k tokens), so it handles longer documents and codebases in a single prompt.