Can NVIDIA GeForce RTX 4090 run Qwen3 30B A3B Instruct 2507?

Yes, at Q5_K_L (22.1 GB) or lower. Higher quantizations like Q6_K (25.1 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Qwen3 30B A3B Instruct 2507?

For Qwen3 30B A3B Instruct 2507, Q4_K_M (18.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (18.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.7 GB.

Can I run Qwen3 30B A3B Instruct 2507 on a Mac?

Qwen3 30B A3B Instruct 2507 requires at least 8.7 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 Instruct 2507 locally?

Yes — Qwen3 30B A3B Instruct 2507 can run locally on consumer hardware. At Q4_K_M quantization it needs 18.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen3 30B A3B Instruct 2507?

At Q4_K_M, Qwen3 30B A3B Instruct 2507 can reach ~158 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~36 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

What's the download size of Qwen3 30B A3B Instruct 2507?

At Q4_K_M, the download is about 18.00 GB. The full-precision Q8_0 version is 30.00 GB. The smallest option (IQ2_XXS) is 8.25 GB.

Alibaba·Qwen·Qwen3MoeForCausalLM

Qwen3 30B A3B Instruct 2507 — Hardware Requirements & GPU Compatibility

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Qwen3 30B A3B Instruct 2507 is a July 2025 updated mixture-of-experts model from Alibaba with 30 billion total parameters but only around 3 billion active during inference. This MoE architecture gives it a remarkably small memory and compute footprint relative to its total parameter count, letting users run a model with broad knowledge on mid-range hardware. The 2507 instruct refresh improves alignment and instruction-following quality over the original release. Because only a fraction of the weights are active at any given time, this model can often run on a single consumer GPU with 8 GB or more of VRAM when quantized, making it an excellent choice for users who want strong chat performance without heavyweight hardware.

1.2M downloads 783 likesSep 2025262K context

Specifications

Publisher: Alibaba
Family: Qwen
Parameters: 30B
Architecture: Qwen3MoeForCausalLM
Context Length: 262,144 tokens
Vocabulary Size: 151,936
Release Date: 2025-09-17
License: Apache 2.0

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HuggingFace

Qwen/Qwen3-30B-A3B-Instruct-2507

How Much VRAM Does Qwen3 30B A3B Instruct 2507 Need?

Select a quantization to see compatible GPUs below.

Quantization	Bits	VRAM	+ Context	File Size	Quality
IQ2_XXS	2.20	8.7 GB	21.4 GB	8.25 GB	Importance-weighted 2-bit, extreme compression — significant quality loss
IQ2_XS	2.40	9.4 GB	22.2 GB	9.00 GB	Importance-weighted 2-bit, extra small
IQ2_S	2.50	9.8 GB	22.6 GB	9.38 GB	Importance-weighted 2-bit, small
IQ2_M	2.70	10.5 GB	23.3 GB	10.13 GB	Importance-weighted 2-bit, medium
IQ3_XXS	3.10	12.0 GB	24.8 GB	11.63 GB	Importance-weighted 3-bit
IQ3_XS	3.30	12.8 GB	25.6 GB	12.38 GB	Importance-weighted 3-bit, extra small
IQ3_S	3.40	13.2 GB	25.9 GB	12.75 GB	Importance-weighted 3-bit, small
Q2_K	3.40	13.2 GB	25.9 GB	12.75 GB	2-bit quantization with K-quant improvements
Q3_K_S	3.50	13.5 GB	26.3 GB	13.13 GB	3-bit small quantization
IQ3_M	3.60	13.9 GB	26.7 GB	13.50 GB	Importance-weighted 3-bit, medium
Q3_K_M	3.90	15.0 GB	27.8 GB	14.63 GB	3-bit medium quantization
Q4_0	4.00	15.4 GB	28.2 GB	15.00 GB	4-bit legacy quantization
Q3_K_L	4.10	15.8 GB	28.6 GB	15.37 GB	3-bit large quantization
IQ4_XS	4.30	16.5 GB	29.3 GB	16.13 GB	Importance-weighted 4-bit, compact
Q4_1	4.50	17.3 GB	30.1 GB	16.88 GB	4-bit legacy quantization with offset
Q4_K_S	4.50	17.3 GB	30.1 GB	16.88 GB	4-bit small quantization
IQ4_NL	4.50	17.3 GB	30.1 GB	16.88 GB	Importance-weighted 4-bit, non-linear
Q4_K_M	4.80	18.4 GB	31.2 GB	18.00 GB	4-bit medium quantization — most popular sweet spot
Q4_K_L	4.90	18.8 GB	31.6 GB	18.38 GB	4-bit large quantization
Q5_K_S	5.50	21.0 GB	33.8 GB	20.63 GB	5-bit small quantization
Q5_K_M	5.70	21.8 GB	34.6 GB	21.38 GB	5-bit medium quantization — good quality/size tradeoff
Q5_K_L	5.80	22.1 GB	34.9 GB	21.75 GB	5-bit large quantization
Q6_K	6.60	25.1 GB	37.9 GB	24.75 GB	6-bit quantization, very good quality
Q8_0	8.00	30.4 GB	43.2 GB	30.00 GB	8-bit quantization, near-lossless

Which GPUs Can Run Qwen3 30B A3B Instruct 2507?

Q4_K_M · 18.4 GB

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Qwen3 30B A3B Instruct 2507 (Q4_K_M) requires 18.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ GB is recommended. Using the full 262K context window can add up to 12.8 GB, bringing total usage to 31.2 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

— Plenty of headroom

NVIDIA GeForce RTX 5090~63 tok/s

Decent

— Enough VRAM, may be tight

NVIDIA GeForce RTX 3090 Ti~36 tok/s NVIDIA GeForce RTX 4090~36 tok/s NVIDIA GeForce RTX 3090~33 tok/s AMD Radeon RX 7900 XTX~29 tok/s AMD Radeon RX 7900 XT~24 tok/s

Which Devices Can Run Qwen3 30B A3B Instruct 2507?

Q4_K_M · 18.4 GB

21 devices with unified memory can run Qwen3 30B A3B Instruct 2507, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).

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Frequently Asked Questions

How much VRAM does Qwen3 30B A3B Instruct 2507 need?: Qwen3 30B A3B Instruct 2507 requires 18.4 GB of VRAM at Q4_K_M, or 30.4 GB at Q8_0. Full 262K context adds up to 12.8 GB (31.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 30B × 4.8 bits ÷ 8 = 18 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 13.2 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M
18.4 GB
Q4_K_M + full context
31.2 GB
Learn more about VRAM estimation →
Can NVIDIA GeForce RTX 4090 run Qwen3 30B A3B Instruct 2507?: Yes, at Q5_K_L (22.1 GB) or lower. Higher quantizations like Q6_K (25.1 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen3 30B A3B Instruct 2507?: For Qwen3 30B A3B Instruct 2507, Q4_K_M (18.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (18.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.7 GB.
VRAM requirement by quantization
IQ2_XXS
8.7 GB~53%
IQ3_S
13.2 GB~75%
Q3_K_L
15.8 GB~86%
Q4_K_M ★
18.4 GB~89%
Q4_K_L
18.8 GB~90%
Q8_0
30.4 GB~99%
★ Recommended — best balance of quality and VRAM usage.
Learn more about quantization →
Can I run Qwen3 30B A3B Instruct 2507 on a Mac?: Qwen3 30B A3B Instruct 2507 requires at least 8.7 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 Instruct 2507 locally?: Yes — Qwen3 30B A3B Instruct 2507 can run locally on consumer hardware. At Q4_K_M quantization it needs 18.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3 30B A3B Instruct 2507?: At Q4_K_M, Qwen3 30B A3B Instruct 2507 can reach ~158 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~36 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 ÷ 18.4 × 0.55 = ~158 tok/s
Estimated speed at Q4_K_M (18.4 GB)
AMD Instinct MI300X
~158 tok/s
NVIDIA GeForce RTX 4090
~36 tok/s
NVIDIA H100 SXM
~118 tok/s
AMD Instinct MI250X
~98 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 Instruct 2507?: At Q4_K_M, the download is about 18.00 GB. The full-precision Q8_0 version is 30.00 GB. The smallest option (IQ2_XXS) is 8.25 GB.