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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 21.4 GB at its Q4_K_M build vs 23.9 GB for Qwen3 32B's Q4_K_M, fitting on 84 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)

FP32
Qwen3 30B-A3B (MoE)
135.3 GB
Qwen3 32B
148.4 GB
BF16
Qwen3 30B-A3B (MoE)
68.1 GB
Qwen3 32B
75.0 GB
FP16
Qwen3 30B-A3B (MoE)
68.1 GB
Qwen3 32B
75.0 GB
Q8_0
Qwen3 30B-A3B (MoE)
36.6 GB
Qwen3 32B
40.5 GB
Q6_K
Qwen3 30B-A3B (MoE)
28.5 GB
Qwen3 32B
31.7 GB
Q5_K_M
Qwen3 30B-A3B (MoE)
24.8 GB
Qwen3 32B
27.7 GB
Q4_K_M
Qwen3 30B-A3B (MoE)
21.4 GB
Qwen3 32B
23.9 GB
Q3_K_M
Qwen3 30B-A3B (MoE)
17.1 GB
Qwen3 32B
19.2 GB
Q2_K
Qwen3 30B-A3B (MoE)
13.7 GB
Qwen3 32B
15.5 GB
NVFP4
Qwen3 30B-A3B (MoE)
17.7 GB
Qwen3 32B
19.9 GB
QuantQwen3 30B-A3B (MoE)Qwen3 32BDiff
FP32135.3 GB148.4 GB-9%
BF1668.1 GB75.0 GB-9%
FP1668.1 GB75.0 GB-9%
Q8_036.6 GB40.5 GB-10%
Q6_K28.5 GB31.7 GB-10%
Q5_K_M24.8 GB27.7 GB-10%
Q4_K_M21.4 GB23.9 GB-11%
Q3_K_M17.1 GB19.2 GB-11%
Q2_K13.7 GB15.5 GB-12%
NVFP417.7 GB19.9 GB-11%

Diff is Qwen3 30B-A3B (MoE) relative to Qwen3 32B. Green = lower VRAM (fits more GPUs).

Model specifications

SpecQwen3 30B-A3B (MoE)Qwen3 32B
OrgAlibabaAlibaba
Parameters30B32.8B
ArchitectureMoE (3B active)Dense
Context128k tokens128k tokens
Modalitiestexttext
LicenseApache 2.0Apache 2.0
CommercialYesYes
Released2025-04-292025-04-29
GPUs (native)84 / 11974 / 119

Benchmark scores

BenchmarkQwen3 30B-A3B (MoE)Qwen3 32B
MMLU-Pro61.565.5

Green = higher score (better). N/A = not yet available. ~ = inherited from a base model, not independently reported for that release itself.

GPUs that run only Qwen3 30B-A3B (MoE)(10)

GPUs that run only Qwen3 32B(0)

Every GPU that runs Qwen3 32B also runs Qwen3 30B-A3B (MoE).

GPUs that run both natively(74)

Which should you use?

Choose Qwen3 30B-A3B (MoE) if:
  • • You have limited VRAM: it's a smaller model needing 21.4 GB vs 23.9 GB
  • • You want fast inference: MoE only activates 3B params per token
Choose Qwen3 32B if:
  • • You want maximum capability and have a 24 GB+ GPU
  • • Benchmark quality matters: scores 65.5 vs 61.5 on MMLU-Pro

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 21.4 GB at its Q4_K_M build vs 23.9 GB for Qwen3 32B's Q4_K_M. Qwen3 30B-A3B (MoE) runs on more GPUs natively (84 vs 74). On MMLU-Pro, Qwen3 32B scores higher (65.5 vs 61.5).
How much VRAM does Qwen3 30B-A3B (MoE) need vs Qwen3 32B?
At 8k context, Qwen3 30B-A3B (MoE) needs approximately 21.4 GB of VRAM at its Q4_K_M build, while Qwen3 32B needs 23.9 GB at its Q4_K_M build. At the largest build each ships, Qwen3 30B-A3B (MoE) requires 68.1 GB (FP16) vs 75.0 GB (FP16) for Qwen3 32B.
Can you run Qwen3 30B-A3B (MoE) on the same GPUs as Qwen3 32B?
Yes, 74 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA RTX 4090, NVIDIA RTX 3090. 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?
Only Qwen3 30B-A3B (MoE) fits in 24 GB, at its Q4_K_M build (21.4 GB). Qwen3 32B needs 23.9 GB at Q4_K_M, requiring a larger GPU.
Full Qwen3 30B-A3B (MoE) page →Full Qwen3 32B page →Check your hardware →