Qwen3-30B-A3B-Instruct-2507 GGUF size and VRAM requirements
MaziyarPanahi/Qwen3-30B-A3B-Instruct-2507-GGUF is a very large instruction-tuned chat model with 30.53 billion parameters, built on the qwen3moe architecture. It has been downloaded 73,802 times.
To run MaziyarPanahi/Qwen3-30B-A3B-Instruct-2507-GGUF locally at a 4,096-token context, its quantized versions need between 11.47 GB (Q2_K, lowest quality) and 57.89 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q5_K_M, needing about 21.22 GB. That means MaziyarPanahi/Qwen3-30B-A3B-Instruct-2507-GGUF fits entirely in the VRAM of a 12 GB GPU or larger, running fully on the GPU.
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q2_K | 2.95 | Low | 10.49 GB | 0.19 GB | 11.47 GB | 4.8 t/s | Offload |
| Q3_K_M | 3.85 | Fair | 13.7 GB | 0.19 GB | 14.69 GB | 3.6 t/s | Offload |
| Q3_K_L | 4.17 | Fair | 14.81 GB | 0.19 GB | 15.8 GB | 3.4 t/s | Offload |
| Q4_K_M | 4.86 | Good | 17.28 GB | 0.19 GB | 18.27 GB | 2.9 t/s | Offload |
| Q5_K_M | 5.69 | Very good | 20.23 GB | 0.19 GB | 21.22 GB | 2.5 t/s | Offload |
| Q6_K | 6.57 | Excellent | 23.37 GB | 0.19 GB | 24.36 GB | — | Insufficient |
| GGUF | 16.01 | Excellent | 56.9 GB | 0.19 GB | 57.89 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run MaziyarPanahi/Qwen3-30B-A3B-Instruct-2507-GGUF?
You need about 11.47 GB of VRAM to run MaziyarPanahi/Qwen3-30B-A3B-Instruct-2507-GGUF entirely on the GPU using the Q2_K quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/Qwen3-30B-A3B-Instruct-2507-GGUF on an 8 GB GPU?
Partially. MaziyarPanahi/Qwen3-30B-A3B-Instruct-2507-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with Q5_K_M), which runs but is slower.
Can I run MaziyarPanahi/Qwen3-30B-A3B-Instruct-2507-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/Qwen3-30B-A3B-Instruct-2507-GGUF fully on the GPU using Q3_K_L (about 15.8 GB).
Can I run MaziyarPanahi/Qwen3-30B-A3B-Instruct-2507-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Qwen3-30B-A3B-Instruct-2507-GGUF fully on the GPU using Q5_K_M (about 21.22 GB).
What is the best quantization for MaziyarPanahi/Qwen3-30B-A3B-Instruct-2507-GGUF?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.