How much VRAM does Qwen 3 30B-A3B (MoE) need?

Qwen 3 30B-A3B (MoE) requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run Qwen 3 30B-A3B (MoE) on a consumer GPU?

Yes, Qwen 3 30B-A3B (MoE) can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 24 GB GPU. For best performance, use a 24 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Qwen 3 30B-A3B (MoE)'s context window?

Qwen 3 30B-A3B (MoE) supports a maximum context window of 131,072 tokens. The default context is 32,768 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for Qwen 3 30B-A3B (MoE)?

For Qwen 3 30B-A3B (MoE), start with Q4_K_M (~0.0 GB) if VRAM-limited. Q8_0 offers better quality at ~2× the VRAM. If you have enough VRAM, Q6_K (~1.5× Q4 VRAM) provides a good balance. For models this large, Q3_K_M may be necessary to fit on consumer hardware.

How does Qwen 3 30B-A3B (MoE) compare to similar models?

Qwen 3 30B-A3B (MoE) is a 30.0B MoE model from the Qwen family. Browse other Qwen models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

QwenMoEApache 2.0

Qwen 3 30B-A3B (MoE)

30.0B

Parameters

3.0B

Active

128K

Max Context

MoE

Architecture

Apr 29, 2025

Released

Text

Modality

About Qwen 3 30B-A3B (MoE)

Qwen 3 30B-A3B is an efficiency-oriented MoE model with 30B total but only 3B active parameters per token. It delivers quality comparable to dense 8B-14B models while running at the speed of a 3B model. At Q4_K_M it needs ~16 GB VRAM (all experts loaded). The extreme activation sparsity makes it ideal for batched inference, edge server deployments, and scenarios where throughput matters more than peak single-query quality. Apache 2.0 licensed.

High ThroughputEdge ServerBatched InferenceCommercial

Technical Specifications

Total Parameters30.0B

Active Parameters3.0B per token

ArchitectureMixture of Experts

Total Experts128

Active Experts8 per token

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 4,096

Transformer Layers48

Attention Heads32

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	128K ctx
Q4_K_M0.50 B/W ~97% of FP16	15.69Consumer GPU	39.51Datacenter GPU
Q8_01.00 B/W ~100% of FP16	31.20Datacenter GPU	55.01Datacenter GPU
F162.00 B/W Reference	62.21Datacenter GPU	86.03Cluster / Multi-GPU

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

Open VRAM Calculator Best GPU Guide VRAM Guide