Qwen 3 30B-A3BvsQwen 3.6 27B
Complete comparison between Qwen 3 30B-A3B (30B parameters, Alibaba) and Qwen 3.6 27B (27B, Alibaba). VRAM required by quantization, tokens/second measured on 4 reference GPUs, use-case verdict, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Qwen 3 30B-A3B | Qwen 3.6 27B |
|---|---|---|
| Settings | 30B | 27B |
| Family | Qwen | Qwen |
| Author | Alibaba | Alibaba |
| Origin | CN | CN |
| License | Apache 2.0 | Apache 2.0 |
| Context | 131,072 tokens | 262,144 tokens |
| Output | April 2025 | April 2026 |
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 | Qwen 3.6 27B |
|---|---|---|
| Q4_K_M (lightweight) | 19 GB | 16 GB |
| Q5_K_M (balanced) | 23 GB | 19 GB |
| Q8 (near-lossless) | 35 GB | 29 GB |
| FP16 (max quality) | 62 GB | 54 GB |
| CPU-only RAM | 32 GB | 28 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 | Qwen 3.6 27B |
|---|---|---|
| RTX 4090 (24 GB) | 100 tok/s · Q5_K_M | 32 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | ✗ too heavy | 13 tok/s · Q4_K_M |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 40 tok/s · Q8 | 13 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
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
Qwen 3.6 27B
Dense multimodal 27B released April 22, 2026. 262k ctx (1M YaRN). SWE-bench Verified 77.2%.
- SWE-bench Verified 77.2%
- Native multimodal
- 262k context (1M YaRN)
- Apache 2.0
- 16+ GB in Q4
- Hybrid architecture (recent llama.cpp support required)
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Qwen 3 30B-A3B or Qwen 3.6 27B?
On an RTX 4090, Qwen 3 30B-A3B runs at Q5_K_M (~100 tok/s), Qwen 3.6 27B in Q5_K_M (~32 tok/s). In pure throughput terms, Qwen 3 30B-A3B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Qwen 3.6 27B fits in 16 GB versus 19 GB for its rival—a 3 GB gap, significant if you're targeting an RTX 3060 or an 8 GB 4060 Ti.
Qwen 3 30B-A3B or Qwen 3.6 27B for coding?
Qwen 3.6 27B 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?
Qwen 3 30B-A3B is under Apache 2.0 — free for commercial use. Qwen 3.6 27B 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 3.6 27B (27B). More capable: Qwen 3 30B-A3B (30B). 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.