Qwen3 30B A3B Instruct 2507 GGUF — Hardware Requirements & GPU Compatibility
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5.8K downloads
77 likesSpecifications
- Publisher
- byteshape
- 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 |
|---|---|---|---|---|---|
| IQ3_S | 3.40 | 14.0 GB | — | 12.75 GB | Importance-weighted 3-bit, small |
| Q3_K_S | 3.50 | 14.4 GB | — | 13.13 GB | 3-bit small quantization |
| IQ4_XS | 4.30 | 17.7 GB | — | 16.13 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 18.6 GB | — | 16.88 GB | 4-bit small quantization |
Which GPUs Can Run Qwen3 30B A3B Instruct 2507 GGUF?
Q4_K_S · 18.6 GBQwen3 30B A3B Instruct 2507 GGUF (Q4_K_S) requires 18.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 25+ 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_S · 18.6 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 14.0 GB of VRAM at IQ3_S, or 18.6 GB at Q4_K_S.
VRAM = Weights + KV Cache + Overhead
Weights = 30B × 3.4 bits ÷ 8 = 12.8 GB
KV Cache + Overhead ≈ 1.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
IQ3_S14.0 GB- What's the best quantization for Qwen3 30B A3B Instruct 2507 GGUF?
For Qwen3 30B A3B Instruct 2507 GGUF, IQ4_XS (17.7 GB) offers the best balance of quality and VRAM usage. Q4_K_S (18.6 GB) provides better quality if you have the VRAM. The smallest option is IQ3_S at 14.0 GB.
VRAM requirement by quantization
IQ3_S14.0 GB~75%Q3_K_S14.4 GB~77%IQ4_XS ★17.7 GB~87%Q4_K_S18.6 GB~88%★ 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 IQ3_S, 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 IQ3_S quantization it needs 14.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 30B A3B Instruct 2507 GGUF?
At IQ3_S, Qwen3 30B A3B Instruct 2507 GGUF can reach ~208 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~47 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 ÷ 14.0 × 0.55 = ~208 tok/s
Estimated speed at IQ3_S (14.0 GB)
AMD Instinct MI300X~208 tok/sNVIDIA GeForce RTX 4090~47 tok/sNVIDIA H100 SXM~155 tok/sAMD Instinct MI250X~129 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 IQ3_S, the download is about 12.75 GB. The full-precision Q4_K_S version is 16.88 GB.
- Which GPUs can run Qwen3 30B A3B Instruct 2507 GGUF?
17 consumer GPUs can run Qwen3 30B A3B Instruct 2507 GGUF at IQ3_S (14.0 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 5 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 30B A3B Instruct 2507 GGUF?
27 devices with unified memory can run Qwen3 30B A3B Instruct 2507 GGUF at IQ3_S (14.0 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.