How much VRAM does Qwen 3 235B-A22B (MoE) need?

Qwen 3 235B-A22B (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 235B-A22B (MoE) on a consumer GPU?

Qwen 3 235B-A22B (MoE) is a 235B parameter model and requires datacenter or multi-GPU setups. At Q4_K_M, it needs approximately 0.0 GB VRAM, which exceeds consumer GPU limits. It can run on H100 (80 GB) or A100 (80 GB) datacenter GPUs, or across multiple consumer GPUs with tensor parallelism.

What is Qwen 3 235B-A22B (MoE)'s context window?

Qwen 3 235B-A22B (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 235B-A22B (MoE)?

For Qwen 3 235B-A22B (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 235B-A22B (MoE) compare to similar models?

Qwen 3 235B-A22B (MoE) is a 235.0B 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 235B-A22B (MoE)

235.0B

Parameters

22.0B

Active

128K

Max Context

MoE

Architecture

—

Released

Text

Modality

About Qwen 3 235B-A22B (MoE)

Qwen 3 235B-A22B (MoE) is a mixture-of-experts (MoE) transformer language model from the Qwen family, containing 235B parameters across 96 layers. It has 235B total parameters loaded into VRAM with 22B active per token. It supports up to 131K tokens of context with a hidden dimension of 8192 and 8 KV heads for efficient grouped-query attention (GQA). Apache 2.0. MoE flagship. Server class.

ResearchEnterprise

Technical Specifications

Total Parameters235.0B

Active Parameters22.0B per token

ArchitectureMixture of Experts

Total Experts22

Attention TypeGQA (MoE)

Hidden Dimensiond = 8,192

Transformer Layers96

Attention Heads64

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	121.8Cluster / Multi-GPU	169.5Cluster / Multi-GPU
Q8_01.00 B/W ~100% of FP16	243.3Cluster / Multi-GPU	290.9Cluster / Multi-GPU
F162.00 B/W Reference	486.2Cluster / Multi-GPU	533.9Cluster / Multi-GPU

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

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