Qwen 3 235B-A22B (MoE) — VRAM, Speed & Local Setup

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

作者： Jakub Rusinowski · 最后更新： 2025年4月28日

Model library → Qwen 3 → Qwen 3 235B-A22B (MoE)

The flagship Qwen 3 model. 235B total with only 22B active per token — comparable compute cost to a dense 22B model. Tops open-source leaderboards. Requires Apple Silicon 128GB+ or multi-GPU.

Qwen 3 235B-A22B (MoE) needs about 143 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters	235 Billion (22B active)
Context window	128,000
Architecture	MoE
Provider	Alibaba Cloud
Licence	Apache 2.0
Specified at	Q4_K_M
System RAM	128 GB
Record updated	2025-04-28

Licence

Apache-2.0 — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), at 8K context. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

Quant	Weights	VRAM needed	Est. speed	Fit on 24 GB
Q2_K	77.3 GB	79.6 GB	—	Won't fit
Q3_K_M	100.2 GB	102.5 GB	—	Won't fit
Q4_K_M	141.9 GB	144.3 GB	—	Won't fit
Q5_K_M	166.6 GB	168.9 GB	—	Won't fit
Q6_K	192.7 GB	195.1 GB	—	Won't fit
Q8_0	249.7 GB	252.1 GB	—	Won't fit
F16	470.0 GB	472.4 GB	—	Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Qwen 3 235B-A22B (MoE) VRAM calculator.

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Recommended GPU

The cheapest catalogued GPU that runs Qwen 3 235B-A22B (MoE) is the Apple M2 Ultra (192 GB).

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Apple Mac Studio M2 Ultra

192 GB VRAM · 60 W board power

2026年价格波动较大——请以当前商品页价格为准。

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How to Run Qwen 3 235B-A22B (MoE)

Install Ollama, then run:

ollama run qwen3:235b-a22b

Weights on Hugging Face: Qwen/Qwen3-235B-A22B.

Best for: frontier reasoning, complex coding, research, enterprise.

Can I Run Qwen 3 235B-A22B (MoE) on My GPU?

Other Qwen 3 Sizes

Qwen 3 8B — about 6 GB VRAM at Q4_K_M
Qwen 3 14B — about 10 GB VRAM at Q4_K_M
Qwen 3 32B — about 21 GB VRAM at Q4_K_M
Qwen 3 30B-A3B (MoE) — about 19 GB VRAM at Q4_K_M

Qwen 3 235B-A22B (MoE) — Frequently Asked Questions

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

About 143 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does Qwen 3 235B-A22B (MoE) run on an RTX 4090 (24 GB)?

No. Qwen 3 235B-A22B (MoE) needs about 143 GB at Q4_K_M, more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.

How do I run Qwen 3 235B-A22B (MoE) locally?

Install Ollama and run `ollama run qwen3:235b-a22b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does Qwen 3 come in?

Qwen 3 8B (6 GB), Qwen 3 14B (10 GB), Qwen 3 32B (21 GB), Qwen 3 30B-A3B (MoE) (19 GB), Qwen 3 235B-A22B (MoE) (143 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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