Qwen 3 32B — 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

The largest dense Qwen 3 model. Exceptional reasoning with hybrid thinking. Rivals DeepSeek R1 671B distills at a fraction of the compute. For RTX 4090 or Apple M2/M3 Max users.

Qwen 3 32B needs about 21 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	32 Billion
Context window	128,000
Architecture	Dense
Provider	Alibaba Cloud
Licence	Apache 2.0
Specified at	Q4_K_M
System RAM	32 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	10.8 GB	13.7 GB	~56 tok/s (est.)	Fits comfortably
Q3_K_M	14.0 GB	16.9 GB	~45 tok/s (est.)	Fits comfortably
Q4_K_M	19.8 GB	22.8 GB	~33 tok/s (est.)	Tight fit
Q5_K_M	23.2 GB	26.2 GB	~4 tok/s (est.)	Offloads to system RAM (slow)
Q6_K	26.9 GB	29.8 GB	~4 tok/s (est.)	Offloads to system RAM (slow)
Q8_0	34.8 GB	37.8 GB	~3 tok/s (est.)	Offloads to system RAM (slow)
F16	65.6 GB	68.5 GB	—	Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Qwen 3 32B VRAM calculator.

Buy This HardwareAMD Radeon RX 7900 XTX 24GB — 24 GB VRAM · 355 W board power Deploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs Qwen 3 32B is the AMD Radeon RX 7900 XTX (24 GB).

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AMD Radeon RX 7900 XTX 24GB

24 GB VRAM · 355 W board power

2026年价格波动较大——请以当前商品页价格为准。

在亚马逊查看价格

How to Run Qwen 3 32B

Install Ollama, then run:

ollama run qwen3:32b

Weights on Hugging Face: Qwen/Qwen3-32B.

Best for: complex reasoning, coding, math, agents.

Can I Run Qwen 3 32B 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 30B-A3B (MoE) — about 19 GB VRAM at Q4_K_M
Qwen 3 235B-A22B (MoE) — about 143 GB VRAM at Q4_K_M

Qwen 3 32B — Frequently Asked Questions

How much VRAM does Qwen 3 32B need?

About 21 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 32B run on an RTX 4090 (24 GB)?

Yes. Qwen 3 32B needs about 21 GB at Q4_K_M, inside a 24 GB card, at an estimated 33 tokens/sec.

How do I run Qwen 3 32B locally?

Install Ollama and run `ollama run qwen3:32b`. 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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