Qwen3 30B A3B Instruct 2507 AWQ 4bit — Hardware Requirements & GPU Compatibility
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An AWQ 4-bit quantized version of Alibaba's Qwen3 30B A3B Instruct 2507 (July 2025 release), repackaged by cyankiwi. This general-purpose mixture-of-experts model has 30 billion total parameters with approximately 3 billion activated per token, yielding around 5.3 billion effective parameters. AWQ quantization is optimized for GPU inference and maintains strong output quality at 4-bit precision. The 2507 revision brings updated training from Alibaba, improving the model's instruction following, reasoning, and multilingual capabilities. Thanks to its sparse activation pattern and aggressive quantization, this model runs efficiently on GPUs with limited VRAM while providing versatile general-purpose performance for chat, writing, analysis, and reasoning tasks.
87.0K downloads
31 likesSpecifications
- Publisher
- cyankiwi
- Family
- Qwen
- Parameters
- 5.3B
- Architecture
- Qwen3MoeForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-01-13
- License
- Apache 2.0
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How Much VRAM Does Qwen3 30B A3B Instruct 2507 AWQ 4bit Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 1.9 GB | 14.6 GB | 1.46 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_XS | 2.40 | 2.0 GB | 14.8 GB | 1.59 GB | Importance-weighted 2-bit, extra small |
| IQ2_S | 2.50 | 2.1 GB | 14.8 GB | 1.66 GB | Importance-weighted 2-bit, small |
| IQ2_M | 2.70 | 2.2 GB | 15.0 GB | 1.79 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 2.5 GB | 15.2 GB | 2.06 GB | Importance-weighted 3-bit |
| IQ3_XS | 3.30 | 2.6 GB | 15.4 GB | 2.19 GB | Importance-weighted 3-bit, extra small |
| IQ3_S | 3.40 | 2.7 GB | 15.4 GB | 2.26 GB | Importance-weighted 3-bit, small |
| Q2_K | 3.40 | 2.7 GB | 15.4 GB | 2.26 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.7 GB | 15.5 GB | 2.32 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 2.8 GB | 15.6 GB | 2.39 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 3.0 GB | 15.8 GB | 2.59 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 3.0 GB | 15.8 GB | 2.65 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 3.1 GB | 15.9 GB | 2.72 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 3.3 GB | 16.0 GB | 2.85 GB | Importance-weighted 4-bit, compact |
| Q4_1 | 4.50 | 3.4 GB | 16.2 GB | 2.98 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 3.4 GB | 16.2 GB | 2.98 GB | 4-bit small quantization |
| IQ4_NL | 4.50 | 3.4 GB | 16.2 GB | 2.98 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 3.6 GB | 16.4 GB | 3.18 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 3.6 GB | 16.4 GB | 3.25 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 4.0 GB | 16.8 GB | 3.65 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 4.2 GB | 17.0 GB | 3.78 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 4.3 GB | 17.0 GB | 3.85 GB | 5-bit large quantization |
| Q6_K | 6.60 | 4.8 GB | 17.6 GB | 4.38 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 5.7 GB | 18.5 GB | 5.31 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 30B A3B Instruct 2507 AWQ 4bit?
Q4_K_M · 3.6 GBQwen3 30B A3B Instruct 2507 AWQ 4bit (Q4_K_M) requires 3.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 262K context window can add up to 12.8 GB, bringing total usage to 16.4 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3 30B A3B Instruct 2507 AWQ 4bit?
Q4_K_M · 3.6 GB33 devices with unified memory can run Qwen3 30B A3B Instruct 2507 AWQ 4bit, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Qwen3 30B A3B Instruct 2507 AWQ 4bit need?
Qwen3 30B A3B Instruct 2507 AWQ 4bit requires 3.6 GB of VRAM at Q4_K_M, or 5.7 GB at Q8_0. Full 262K context adds up to 12.8 GB (16.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 5.3B × 4.8 bits ÷ 8 = 3.2 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_M3.6 GBQ4_K_M + full context16.4 GB- What's the best quantization for Qwen3 30B A3B Instruct 2507 AWQ 4bit?
For Qwen3 30B A3B Instruct 2507 AWQ 4bit, Q4_K_M (3.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (3.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.9 GB.
VRAM requirement by quantization
IQ2_XXS1.9 GB~53%IQ3_S2.7 GB~75%Q3_K_L3.1 GB~86%Q4_K_M ★3.6 GB~89%Q4_K_L3.6 GB~90%Q8_05.7 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 30B A3B Instruct 2507 AWQ 4bit on a Mac?
Qwen3 30B A3B Instruct 2507 AWQ 4bit requires at least 1.9 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 AWQ 4bit locally?
Yes — Qwen3 30B A3B Instruct 2507 AWQ 4bit can run locally on consumer hardware. At Q4_K_M quantization it needs 3.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 30B A3B Instruct 2507 AWQ 4bit?
At Q4_K_M, Qwen3 30B A3B Instruct 2507 AWQ 4bit can reach ~814 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~183 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 ÷ 3.6 × 0.55 = ~814 tok/s
Estimated speed at Q4_K_M (3.6 GB)
AMD Instinct MI300X~814 tok/sNVIDIA GeForce RTX 4090~183 tok/sNVIDIA H100 SXM~609 tok/sAMD Instinct MI250X~503 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 30B A3B Instruct 2507 AWQ 4bit?
At Q4_K_M, the download is about 3.18 GB. The full-precision Q8_0 version is 5.31 GB. The smallest option (IQ2_XXS) is 1.46 GB.