Qwen3 30B A3B Instruct 2507 GGUF — Hardware Requirements & GPU Compatibility
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50.4K downloads
297 likesSpecifications
- Publisher
- Unsloth
- Family
- Qwen
- Parameters
- 30B
- License
- Apache 2.0
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HuggingFace
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 |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 9.1 GB | — | 8.25 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 11.1 GB | — | 10.13 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 12.8 GB | — | 11.63 GB | Importance-weighted 3-bit |
| 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 |
| IQ4_XS | 4.30 | 17.7 GB | — | 16.13 GB | Importance-weighted 4-bit, compact |
| Q4_1 | 4.50 | 18.6 GB | — | 16.88 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 18.6 GB | — | 16.88 GB | 4-bit small quantization |
| IQ4_NL | 4.50 | 18.6 GB | — | 16.88 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 19.8 GB | — | 18.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 22.7 GB | — | 20.63 GB | 5-bit small quantization |
| 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 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 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 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_S (22.7 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%Q3_K_S14.4 GB~77%Q4_118.6 GB~88%Q4_K_M ★19.8 GB~89%Q5_K_S22.7 GB~92%Q8_033.0 GB~99%★ 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 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 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 Q8_0 version is 30.00 GB. The smallest option (IQ2_XXS) is 8.25 GB.