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

For Qwen3 30B A3B Thinking 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 Thinking 2507 on a Mac?

Qwen3 30B A3B Thinking 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 Thinking 2507 locally?

Yes — Qwen3 30B A3B Thinking 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 Thinking 2507?

At Q4_K_M, Qwen3 30B A3B Thinking 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 Thinking 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 Thinking 2507 — Hardware Requirements & GPU Compatibility

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Qwen3 30B A3B Thinking 2507 is the reasoning-focused variant of Alibaba's 30-billion-parameter mixture-of-experts model, updated in July 2025. Like its instruct sibling, it activates only about 3 billion parameters per token, keeping resource demands low while enabling multi-step reasoning and chain-of-thought problem solving. This thinking variant is designed for tasks that benefit from deliberate, step-by-step logic such as math, coding puzzles, and analytical questions. Its efficient MoE design means users with modest GPUs can still access strong reasoning capabilities without needing datacenter-class hardware.

1.1M downloads 368 likesAug 2025262K context

Specifications

Publisher: Alibaba
Family: Qwen
Parameters: 30B
Architecture: Qwen3MoeForCausalLM
Context Length: 262,144 tokens
Vocabulary Size: 151,936
Release Date: 2025-08-17
License: Apache 2.0

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HuggingFace

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

How Much VRAM Does Qwen3 30B A3B Thinking 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
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 Thinking 2507?

Q4_K_M · 18.4 GB

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Qwen3 30B A3B Thinking 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 Thinking 2507?

Q4_K_M · 18.4 GB

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

Related Models

Derivatives (1)

Qwen3 30B A3B Thinking 2507 GGUF

Frequently Asked Questions

How much VRAM does Qwen3 30B A3B Thinking 2507 need?: Qwen3 30B A3B Thinking 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 Thinking 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 Thinking 2507?: For Qwen3 30B A3B Thinking 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%
Q2_K
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 Thinking 2507 on a Mac?: Qwen3 30B A3B Thinking 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 Thinking 2507 locally?: Yes — Qwen3 30B A3B Thinking 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 Thinking 2507?: At Q4_K_M, Qwen3 30B A3B Thinking 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 Thinking 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.