Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF — Hardware Requirements & GPU Compatibility
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40.1K downloadsSpecifications
- Publisher
- cyberjuju
- Family
- Qwen
- Parameters
- 30B
- License
- Apache 2.0
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How Much VRAM Does Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 14.0 GB | — | 12.75 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 14.4 GB | — | 13.13 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 16.1 GB | — | 14.63 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 16.5 GB | — | 15.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 19.8 GB | — | 18.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 23.5 GB | — | 21.38 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 27.2 GB | — | 24.75 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 33 GB | — | 30.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF?
Q4_K_M · 19.8 GBQwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF (Q4_K_M) requires 19.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 26+ GB is recommended. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF?
Q4_K_M · 19.8 GB21 devices with unified memory can run Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF need?
Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF requires 19.8 GB of VRAM at Q4_K_M, or 33 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 30B × 4.8 bits ÷ 8 = 18 GB
KV Cache + Overhead ≈ 1.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M19.8 GB- Can NVIDIA GeForce RTX 4090 run Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF?
Yes, at Q5_K_L (23.9 GB) or lower. Higher quantizations like Q6_K (27.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF?
For Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF, Q4_K_M (19.8 GB) offers the best balance of quality and VRAM usage. Q4_K_L (20.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.1 GB.
VRAM requirement by quantization
IQ2_XXS9.1 GB~53%IQ3_S14.0 GB~75%Q3_K_L16.9 GB~86%Q4_K_M ★19.8 GB~89%Q4_K_L20.2 GB~90%Q8_033.0 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF on a Mac?
Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF requires at least 9.1 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 Q4 K M GGUF locally?
Yes — Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 19.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF?
At Q4_K_M, Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF can reach ~147 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~33 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 ÷ 19.8 × 0.55 = ~147 tok/s
Estimated speed at Q4_K_M (19.8 GB)
AMD Instinct MI300X~147 tok/sNVIDIA GeForce RTX 4090~33 tok/sNVIDIA H100 SXM~110 tok/sAMD Instinct MI250X~91 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 Q4 K M GGUF?
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.
- Which GPUs can run Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF?
6 consumer GPUs can run Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF at Q4_K_M (19.8 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF?
21 devices with unified memory can run Qwen3 30B A3B Instruct 2507 AWQ 4bit Q4 K M GGUF at Q4_K_M (19.8 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.