Nemotron 3 33BvsQwen 3 30B-A3B
Complete comparison between Nemotron 3 33B (33B parameters, NVIDIA) and Qwen 3 30B-A3B (30B, Alibaba). 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 | Nemotron 3 33B | Qwen 3 30B-A3B |
|---|---|---|
| Settings | 33B | 30B |
| Family | Nemotron | Qwen |
| Author | NVIDIA | Alibaba |
| Origin | US | CN |
| License | NVIDIA Open Model License | Apache 2.0 |
| Context | 128,000 tokens | 131,072 tokens |
| Output | May 4, 2026 | April 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 | Nemotron 3 33B | Qwen 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) | 66 GB | 62 GB |
| CPU-only RAM | 43 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 | Nemotron 3 33B | Qwen 3 30B-A3B |
|---|---|---|
| RTX 4090 (24 GB) | 30 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) | 12 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.
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Strengths and weaknesses
Nemotron 3 33B
Dense 33B NVIDIA for chat, code, and reasoning. 128k context, NVIDIA open license. Fits on RTX 4090 in Q4. Released May 2026.
- Balanced 33B for chat/code/reasoning
- 128k context
- Fits on RTX 4090 in Q4
- Open NVIDIA license
- NVIDIA Open Model license (read the commercial terms)
- Gated model on Hugging Face (click-through)
Installation
Qwen 3 30B-A3B
MoE 30B/3B active hybrid thinking. MMLU 81.4, AIME24 80.4. 100+ languages.
- Fast thanks to the 3B active parameters
- MMLU 81.4
- AIME24 80.4
- Apache 2.0
- 19 GB in Q4
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Nemotron 3 33B or Qwen 3 30B-A3B?
On an RTX 4090, Nemotron 3 33B runs at Q5_K_M (~30 tok/s), Qwen 3 30B-A3B in Q5_K_M (~100 tok/s). In pure throughput, Qwen 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.
Nemotron 3 33B or Qwen 3 30B-A3B for coding?
Nemotron 3 33B is the best fit (code-specialized, top HumanEval scores). For an IDE copilot, pair it with Continue or Aider.
Can these models be used in commercial production?
Nemotron 3 33B is under the NVIDIA Open Model License — check the restrictions, particularly the monthly user thresholds. Qwen 3 30B-A3B is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Qwen 3 30B-A3B (30B). More capable: Nemotron 3 33B (33B). If you're unsure, run the 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.