Can NVIDIA GeForce RTX 4090 run Qwen3 32B AWQ?

Yes, at Q5_K_M (24.0 GB) or lower. Higher quantizations like Q6_K (27.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Qwen3 32B AWQ?

For Qwen3 32B AWQ, Q4_K_M (20.3 GB) offers the best balance of quality and VRAM usage. Q5_0 (21.1 GB) provides better quality if you have the VRAM.

Can I run Qwen3 32B AWQ on a Mac?

Qwen3 32B AWQ requires at least 20.3 GB at Q4_K_M, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.

Can I run Qwen3 32B AWQ locally?

Yes — Qwen3 32B AWQ can run locally on consumer hardware. At Q4_K_M quantization it needs 20.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen3 32B AWQ?

At Q4_K_M, Qwen3 32B AWQ can reach ~144 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~32 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

What's the download size of Qwen3 32B AWQ?

At Q4_K_M, the download is about 19.66 GB. The full-precision Q8_0 version is 32.76 GB.

Alibaba·Qwen·Qwen3ForCausalLM

Qwen3 32B AWQ — Hardware Requirements & GPU Compatibility

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Qwen3 32B AWQ is an AWQ-quantized version of Alibaba's 32.8-billion-parameter Qwen3 dense model. AWQ (Activation-aware Weight Quantization) reduces the model's memory footprint significantly while preserving most of the original quality, making this large model much more accessible on consumer GPUs with 16 to 24 GB of VRAM. For users who want the full dense 32B Qwen3 experience but lack the VRAM to run it at full precision, the AWQ variant is an excellent compromise. It retains strong general-purpose capabilities across chat, reasoning, and creative tasks while fitting into a fraction of the memory that the unquantized model would require.

666.0K downloads 130 likesMay 202541K context

Based on Qwen3 32B

Specifications

Publisher: Alibaba
Family: Qwen
Parameters: 32.8B
Architecture: Qwen3ForCausalLM
Context Length: 40,960 tokens
Vocabulary Size: 151,936
Release Date: 2025-05-21
License: Apache 2.0

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HuggingFace

Qwen/Qwen3-32B-AWQ

How Much VRAM Does Qwen3 32B AWQ Need?

Select a quantization to see compatible GPUs below.

Quantization	Bits	VRAM	+ Context	File Size	Quality
Q4_K_M	4.80	20.3 GB	26.7 GB	19.66 GB	4-bit medium quantization — most popular sweet spot
Q5_0	5.00	21.1 GB	27.5 GB	20.48 GB	5-bit legacy quantization
Q5_K_M	5.70	24.0 GB	30.4 GB	23.34 GB	5-bit medium quantization — good quality/size tradeoff
Q6_K	6.60	27.7 GB	34.0 GB	27.03 GB	6-bit quantization, very good quality
Q8_0	8.00	33.4 GB	39.8 GB	32.76 GB	8-bit quantization, near-lossless

Which GPUs Can Run Qwen3 32B AWQ?

Q4_K_M · 20.3 GB

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Qwen3 32B AWQ (Q4_K_M) requires 20.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 41K context window can add up to 6.4 GB, bringing total usage to 26.7 GB. 5 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

— Plenty of headroom

NVIDIA GeForce RTX 5090~57 tok/s

Decent

— Enough VRAM, may be tight

NVIDIA GeForce RTX 3090 Ti~32 tok/s NVIDIA GeForce RTX 4090~32 tok/s NVIDIA GeForce RTX 3090~30 tok/s AMD Radeon RX 7900 XTX~26 tok/s

Which Devices Can Run Qwen3 32B AWQ?

Q4_K_M · 20.3 GB

21 devices with unified memory can run Qwen3 32B AWQ, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).

Related Models

Frequently Asked Questions

How much VRAM does Qwen3 32B AWQ need?: Qwen3 32B AWQ requires 20.3 GB of VRAM at Q4_K_M, or 33.4 GB at Q8_0. Full 41K context adds up to 6.4 GB (26.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.8B × 4.8 bits ÷ 8 = 19.7 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M
20.3 GB
Q4_K_M + full context
26.7 GB
Learn more about VRAM estimation →
Can NVIDIA GeForce RTX 4090 run Qwen3 32B AWQ?: Yes, at Q5_K_M (24.0 GB) or lower. Higher quantizations like Q6_K (27.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen3 32B AWQ?: For Qwen3 32B AWQ, Q4_K_M (20.3 GB) offers the best balance of quality and VRAM usage. Q5_0 (21.1 GB) provides better quality if you have the VRAM.
VRAM requirement by quantization
Q4_K_M ★
20.3 GB~89%
Q5_0
21.1 GB~90%
Q5_K_M
24.0 GB~92%
Q6_K
27.7 GB~95%
Q8_0
33.4 GB~99%
★ Recommended — best balance of quality and VRAM usage.
Learn more about quantization →
Can I run Qwen3 32B AWQ on a Mac?: Qwen3 32B AWQ requires at least 20.3 GB at Q4_K_M, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
Can I run Qwen3 32B AWQ locally?: Yes — Qwen3 32B AWQ can run locally on consumer hardware. At Q4_K_M quantization it needs 20.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3 32B AWQ?: At Q4_K_M, Qwen3 32B AWQ can reach ~144 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~32 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 20.3 × 0.55 = ~144 tok/s
Estimated speed at Q4_K_M (20.3 GB)
AMD Instinct MI300X
~144 tok/s
NVIDIA GeForce RTX 4090
~32 tok/s
NVIDIA H100 SXM
~107 tok/s
AMD Instinct MI250X
~89 tok/s
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
Learn more about tok/s estimation →
What's the download size of Qwen3 32B AWQ?: At Q4_K_M, the download is about 19.66 GB. The full-precision Q8_0 version is 32.76 GB.