Qwen3 30B A3B Instruct 2507 GGUF — Hardware Requirements & GPU Compatibility
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57.9K downloads
4 likesSpecifications
- Publisher
- MaziyarPanahi
- Family
- Qwen
- Parameters
- 30B
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How Much VRAM Does Qwen3 30B A3B Instruct 2507 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_M | 3.90 | 16.1 GB | — | 14.63 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 16.9 GB | — | 15.37 GB | 3-bit large 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 |
Which GPUs Can Run Qwen3 30B A3B Instruct 2507 GGUF?
Q4_K_M · 19.8 GBQwen3 30B A3B Instruct 2507 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 GGUF?
Q4_K_M · 19.8 GB21 devices with unified memory can run Qwen3 30B A3B Instruct 2507 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 GGUF need?
Qwen3 30B A3B Instruct 2507 GGUF requires 19.8 GB of VRAM at Q4_K_M, or 27.2 GB at Q6_K.
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 GGUF?
Yes, at Q5_K_M (23.5 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 GGUF?
For Qwen3 30B A3B Instruct 2507 GGUF, Q4_K_M (19.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (23.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.0 GB.
VRAM requirement by quantization
Q2_K14.0 GB~75%Q3_K_M16.1 GB~83%Q3_K_L16.9 GB~86%Q4_K_M ★19.8 GB~89%Q5_K_M23.5 GB~92%Q6_K27.2 GB~95%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 30B A3B Instruct 2507 GGUF on a Mac?
Qwen3 30B A3B Instruct 2507 GGUF requires at least 14.0 GB at Q2_K, 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 GGUF locally?
Yes — Qwen3 30B A3B Instruct 2507 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 GGUF?
At Q4_K_M, Qwen3 30B A3B Instruct 2507 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 GGUF?
At Q4_K_M, the download is about 18.00 GB. The full-precision Q6_K version is 24.75 GB. The smallest option (Q2_K) is 12.75 GB.