Can NVIDIA GeForce RTX 4090 run Qwen3 30B A3B GPTQ Int4?

Yes, at Q5_K_L (22.5 GB) or lower. Higher quantizations like Q6_K (25.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Qwen3 30B A3B GPTQ Int4?

For Qwen3 30B A3B GPTQ Int4, Q4_K_M (18.7 GB) offers the best balance of quality and VRAM usage. Q4_K_L (19.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.8 GB.

Can I run Qwen3 30B A3B GPTQ Int4 on a Mac?

Qwen3 30B A3B GPTQ Int4 requires at least 8.8 GB at IQ2_XXS, 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 30B A3B GPTQ Int4 locally?

Yes — Qwen3 30B A3B GPTQ Int4 can run locally on consumer hardware. At Q4_K_M quantization it needs 18.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen3 30B A3B GPTQ Int4?

At Q4_K_M, Qwen3 30B A3B GPTQ Int4 can reach ~156 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~35 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

What's the download size of Qwen3 30B A3B GPTQ Int4?

At Q4_K_M, the download is about 18.32 GB. The full-precision Q8_0 version is 30.53 GB. The smallest option (IQ2_XXS) is 8.40 GB.

Alibaba·Qwen·Qwen3MoeForCausalLM

Qwen3 30B A3B GPTQ Int4 — Hardware Requirements & GPU Compatibility

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Qwen3 30B A3B GPTQ Int4 is a GPTQ INT4 quantized version of Alibaba's 30.5-billion-parameter mixture-of-experts model. The aggressive INT4 quantization combined with the MoE architecture's low active parameter count makes this one of the most memory-efficient ways to run a 30B-class model locally. With only about 3 billion parameters active per token and weights compressed to 4-bit precision, this model can fit comfortably on consumer GPUs with as little as 4 to 6 GB of VRAM. It is an excellent option for users who want to maximize model capability on budget hardware, though some quality degradation compared to higher-precision formats is expected.

104.7K downloads 49 likesMay 202541K context

Based on Qwen3 30B A3B

Specifications

Publisher: Alibaba
Family: Qwen
Parameters: 30.5B
Architecture: Qwen3MoeForCausalLM
Context Length: 40,960 tokens
Vocabulary Size: 151,936
Release Date: 2025-05-21
License: Apache 2.0

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HuggingFace

Qwen/Qwen3-30B-A3B-GPTQ-Int4

How Much VRAM Does Qwen3 30B A3B GPTQ Int4 Need?

Select a quantization to see compatible GPUs below.

Quantization	Bits	VRAM	+ Context	File Size	Quality
IQ2_XXS	2.20	8.8 GB	10.7 GB	8.40 GB	Importance-weighted 2-bit, extreme compression — significant quality loss
IQ2_XS	2.40	9.6 GB	11.5 GB	9.16 GB	Importance-weighted 2-bit, extra small
IQ2_S	2.50	9.9 GB	11.8 GB	9.54 GB	Importance-weighted 2-bit, small
IQ2_M	2.70	10.7 GB	12.6 GB	10.30 GB	Importance-weighted 2-bit, medium
IQ3_XXS	3.10	12.2 GB	14.1 GB	11.83 GB	Importance-weighted 3-bit
IQ3_XS	3.30	13 GB	14.9 GB	12.59 GB	Importance-weighted 3-bit, extra small
Q2_K	3.40	13.4 GB	15.3 GB	12.98 GB	2-bit quantization with K-quant improvements
Q3_K_S	3.50	13.8 GB	15.7 GB	13.36 GB	3-bit small quantization
IQ3_M	3.60	14.1 GB	16.1 GB	13.74 GB	Importance-weighted 3-bit, medium
Q3_K_M	3.90	15.3 GB	17.2 GB	14.88 GB	3-bit medium quantization
Q4_0	4.00	15.7 GB	17.6 GB	15.27 GB	4-bit legacy quantization
Q3_K_L	4.10	16.1 GB	18.0 GB	15.65 GB	3-bit large quantization
IQ4_XS	4.30	16.8 GB	18.7 GB	16.41 GB	Importance-weighted 4-bit, compact
Q4_1	4.50	17.6 GB	19.5 GB	17.17 GB	4-bit legacy quantization with offset
Q4_K_S	4.50	17.6 GB	19.5 GB	17.17 GB	4-bit small quantization
IQ4_NL	4.50	17.6 GB	19.5 GB	17.17 GB	Importance-weighted 4-bit, non-linear
Q4_K_M	4.80	18.7 GB	20.6 GB	18.32 GB	4-bit medium quantization — most popular sweet spot
Q4_K_L	4.90	19.1 GB	21.0 GB	18.70 GB	4-bit large quantization
Q5_K_S	5.50	21.4 GB	23.3 GB	20.99 GB	5-bit small quantization
Q5_K_M	5.70	22.1 GB	24.1 GB	21.75 GB	5-bit medium quantization — good quality/size tradeoff
Q5_K_L	5.80	22.5 GB	24.4 GB	22.14 GB	5-bit large quantization
Q6_K	6.60	25.6 GB	27.5 GB	25.19 GB	6-bit quantization, very good quality
Q8_0	8.00	30.9 GB	32.9 GB	30.53 GB	8-bit quantization, near-lossless

Which GPUs Can Run Qwen3 30B A3B GPTQ Int4?

Q4_K_M · 18.7 GB

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Qwen3 30B A3B GPTQ Int4 (Q4_K_M) requires 18.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 25+ GB is recommended. Using the full 41K context window can add up to 1.9 GB, bringing total usage to 20.6 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

— Plenty of headroom

NVIDIA GeForce RTX 5090~62 tok/s

Decent

— Enough VRAM, may be tight

NVIDIA GeForce RTX 3090 Ti~35 tok/s NVIDIA GeForce RTX 4090~35 tok/s NVIDIA GeForce RTX 3090~33 tok/s AMD Radeon RX 7900 XTX~28 tok/s AMD Radeon RX 7900 XT~24 tok/s

Which Devices Can Run Qwen3 30B A3B GPTQ Int4?

Q4_K_M · 18.7 GB

21 devices with unified memory can run Qwen3 30B A3B GPTQ Int4, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).

Related Models

Frequently Asked Questions

How much VRAM does Qwen3 30B A3B GPTQ Int4 need?: Qwen3 30B A3B GPTQ Int4 requires 18.7 GB of VRAM at Q4_K_M, or 30.9 GB at Q8_0. Full 41K context adds up to 1.9 GB (20.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 30.5B × 4.8 bits ÷ 8 = 18.3 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.3 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M
18.7 GB
Q4_K_M + full context
20.6 GB
Learn more about VRAM estimation →
Can NVIDIA GeForce RTX 4090 run Qwen3 30B A3B GPTQ Int4?: Yes, at Q5_K_L (22.5 GB) or lower. Higher quantizations like Q6_K (25.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen3 30B A3B GPTQ Int4?: For Qwen3 30B A3B GPTQ Int4, Q4_K_M (18.7 GB) offers the best balance of quality and VRAM usage. Q4_K_L (19.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.8 GB.
VRAM requirement by quantization
IQ2_XXS
8.8 GB~53%
Q2_K
13.4 GB~75%
Q3_K_L
16.1 GB~86%
Q4_K_M ★
18.7 GB~89%
Q4_K_L
19.1 GB~90%
Q8_0
30.9 GB~99%
★ Recommended — best balance of quality and VRAM usage.
Learn more about quantization →
Can I run Qwen3 30B A3B GPTQ Int4 on a Mac?: Qwen3 30B A3B GPTQ Int4 requires at least 8.8 GB at IQ2_XXS, 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 30B A3B GPTQ Int4 locally?: Yes — Qwen3 30B A3B GPTQ Int4 can run locally on consumer hardware. At Q4_K_M quantization it needs 18.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3 30B A3B GPTQ Int4?: At Q4_K_M, Qwen3 30B A3B GPTQ Int4 can reach ~156 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~35 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 ÷ 18.7 × 0.55 = ~156 tok/s
Estimated speed at Q4_K_M (18.7 GB)
AMD Instinct MI300X
~156 tok/s
NVIDIA GeForce RTX 4090
~35 tok/s
NVIDIA H100 SXM
~116 tok/s
AMD Instinct MI250X
~96 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 30B A3B GPTQ Int4?: At Q4_K_M, the download is about 18.32 GB. The full-precision Q8_0 version is 30.53 GB. The smallest option (IQ2_XXS) is 8.40 GB.