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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 19.8 GB at Q4_K_M vs 22.2 GB for Qwen3 32B, fitting on 85 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)

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_034.5 GB38.2 GB-10%
Q6_K28.5 GB31.6 GB-10%
Q5_K_M22.5 GB25.2 GB-10%
Q4_K_M19.8 GB22.2 GB-11%
Q3_K_M15.3 GB17.3 GB-11%
Q2_K12.0 GB13.6 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)85 / 10776 / 107

Benchmark scores

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

Green = higher score (better). — = not yet available.

GPUs that run only Qwen3 30B-A3B (MoE)(9)

GPUs that run only Qwen3 32B(0)

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

GPUs that run both natively(76)

Which should you use?

Choose Qwen3 30B-A3B (MoE) if:
  • • You have limited VRAM — it's a smaller model needing 19.8 GB vs 22.2 GB
  • • You want fast inference — MoE only activates 3B params per token
Choose Qwen3 32B if:
  • • You want maximum capability and have a 23 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 19.8 GB at Q4_K_M vs 22.2 GB. Qwen3 30B-A3B (MoE) runs on more GPUs natively (85 vs 76). 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 Q4_K_M quantization with 8k context, Qwen3 30B-A3B (MoE) needs approximately 19.8 GB of VRAM, while Qwen3 32B needs 22.2 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, 76 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA RTX 5080, NVIDIA RTX 5070 Ti. However, 9 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 19.8 GB and Qwen3 32B needs 22.2 GB.
Full Qwen3 30B-A3B (MoE) page →Full Qwen3 32B page →Check your hardware →