Qwen3 30B-A3B (MoE) vs Qwen3 32B
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
Qwen3 30B-A3B (MoE) is more hardware-efficient — it needs 17.7 GB at Q4_K_M vs 19.9 GB for Qwen3 32B, fitting on 61 GPUs natively. Qwen3 30B-A3B (MoE) is a Mixture of Experts model — it has 30B total parameters but only 3B are active per token, making inference faster than its total size suggests.
VRAM at each quantization (8k context)
| Quant | Qwen3 30B-A3B (MoE) | Qwen3 32B | Diff |
|---|---|---|---|
| FP16 | 68.1 GB | 75.0 GB | -9% |
| Q8 | 34.5 GB | 38.2 GB | -10% |
| Q6_K | 26.1 GB | 29.1 GB | -10% |
| Q5_K_M | 21.9 GB | 24.5 GB | -10% |
| Q4_K_M | 17.7 GB | 19.9 GB | -11% |
| Q3_K_M | 14.3 GB | 16.2 GB | -11% |
| Q2_K | 11.0 GB | 12.5 GB | -12% |
Diff is Qwen3 30B-A3B (MoE) relative to Qwen3 32B. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Qwen3 30B-A3B (MoE) | Qwen3 32B |
|---|---|---|
| Org | Alibaba | Alibaba |
| Parameters | 30B | 32.8B |
| Architecture | MoE (3B active) | Dense |
| Context | 128k tokens | 128k tokens |
| Modalities | text | text |
| License | Apache 2.0 | Apache 2.0 |
| Commercial | Yes | Yes |
| Released | 2025-04-29 | 2025-04-29 |
| GPUs (native) | 61 / 67 | 51 / 67 |
GPUs that run only Qwen3 30B-A3B (MoE)(10)
- NVIDIA RTX 4070 Ti12 GB
- NVIDIA RTX 407012 GB
- NVIDIA RTX 3060 12GB12 GB
- Apple M5 (16GB)16 GB
- Apple M4 (16GB)16 GB
- Apple M3 (16GB)16 GB
- Apple M2 Pro (16GB)16 GB
- Apple M2 (16GB)16 GB
- Apple M1 Pro (16GB)16 GB
- Apple M1 (16GB)16 GB
GPUs that run only Qwen3 32B(0)
Every GPU that runs Qwen3 32B also runs Qwen3 30B-A3B (MoE).
GPUs that run both natively(51)
- NVIDIA RTX 509032 GB
- NVIDIA RTX 409024 GB
- NVIDIA RTX 408016 GB
- NVIDIA RTX 4060 Ti 16GB16 GB
- NVIDIA RTX 309024 GB
- NVIDIA RTX 3090 Ti24 GB
- NVIDIA H100 80GB80 GB
- NVIDIA A100 80GB80 GB
- NVIDIA A100 40GB40 GB
- NVIDIA L40S48 GB
- NVIDIA RTX A600048 GB
- NVIDIA RTX 6000 Ada48 GB
- +39 more GPUs run both
Which should you use?
Choose Qwen3 30B-A3B (MoE) if:
- • You have limited VRAM — it's a smaller model needing 17.7 GB vs 19.9 GB
- • You want fast inference — MoE only activates 3B params per token
Choose Qwen3 32B if:
- • You want maximum capability and have a 20 GB+ GPU
Frequently asked questions
- Which is better, Qwen3 30B-A3B (MoE) or Qwen3 32B?
- Qwen3 30B-A3B (MoE) has 30B parameters vs 32.8B for Qwen3 32B, so Qwen3 32B is the larger model. Qwen3 30B-A3B (MoE) is more hardware-efficient, needing 17.7 GB at Q4_K_M vs 19.9 GB. Qwen3 30B-A3B (MoE) runs on more GPUs natively (61 vs 51).
- How much VRAM does Qwen3 30B-A3B (MoE) need vs Qwen3 32B?
- At Q4_K_M quantization with 8k context, Qwen3 30B-A3B (MoE) needs approximately 17.7 GB of VRAM, while Qwen3 32B needs 19.9 GB. At FP16, Qwen3 30B-A3B (MoE) requires 68.1 GB vs 75.0 GB for Qwen3 32B.
- Can you run Qwen3 30B-A3B (MoE) on the same GPUs as Qwen3 32B?
- Yes, 51 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA RTX 4090, NVIDIA RTX 4080. However, 10 GPUs can run Qwen3 30B-A3B (MoE) but not Qwen3 32B, and no GPU can run Qwen3 32B without also fitting Qwen3 30B-A3B (MoE).
- What is the difference between Qwen3 30B-A3B (MoE) and Qwen3 32B?
- Qwen3 30B-A3B (MoE) has 30B parameters (3B active, MoE) with a 128k context window. Qwen3 32B has 32.8B parameters (dense) with a 128k context window.
- Which model fits in 24 GB of VRAM, Qwen3 30B-A3B (MoE) or Qwen3 32B?
- Both fit in 24 GB of VRAM at Q4_K_M — Qwen3 30B-A3B (MoE) needs 17.7 GB and Qwen3 32B needs 19.9 GB.