Qwen 3 30B-A3BvsDeepSeek R1 Distill 32B
Complete comparison between Qwen 3 30B-A3B (30B parameters, Alibaba) and DeepSeek R1 Distill 32B (32B, DeepSeek). 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 30B-A3B | DeepSeek R1 Distill 32B |
|---|---|---|
| Settings | 30B | 32B |
| Family | Qwen | DeepSeek |
| Author | Alibaba | DeepSeek |
| Origin | CN | CN |
| License | Apache 2.0 | MIT |
| Context | 131,072 tokens | 32 768 tokens |
| Output | April 2025 | January 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 30B-A3B | DeepSeek R1 Distill 32B |
|---|---|---|
| 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 | 64 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 30B-A3B | DeepSeek R1 Distill 32B |
|---|---|---|
| RTX 4090 (24 GB) | 100 tok/s · Q5_K_M | 30 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 | 12 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.
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- PDF + files
- Refunded within 30 days
Strengths and weaknesses
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
DeepSeek R1 Distill 32B
The best accessible open-weight reasoner.
- Explicit step-by-step reasoning
- Excellent at math and science
- 32k context
- MIT License
- Very verbose (thinking tokens)
- Slower to respond
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Qwen 3 30B-A3B or DeepSeek R1 Distill 32B?
On an RTX 4090, Qwen 3 30B-A3B runs at Q5_K_M (~100 tok/s), DeepSeek R1 Distill 32B in Q5_K_M (~30 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.
Can these models be used in commercial production?
Qwen 3 30B-A3B is under Apache 2.0 — free for commercial use. DeepSeek R1 Distill 32B is also free under the MIT license. For SaaS, favor 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: DeepSeek R1 Distill 32B (32B). 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.