Run MaziyarPanahi/Qwen3-32B-GGUF locally
MaziyarPanahi/Qwen3-32B-GGUF is a very large language model with 32.76 billion parameters, built on the qwen3 architecture. It has been downloaded 274,080 times.
To run MaziyarPanahi/Qwen3-32B-GGUF locally at a 4,096-token context, its quantized versions need between 12.92 GB (Q2_K, lowest quality) and 26.46 GB (Q6_K, 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 23.05 GB. That means MaziyarPanahi/Qwen3-32B-GGUF fits entirely in the VRAM of a 16 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q2_K | 3.01 | Low | 11.5 GB | 0.62 GB | 12.92 GB | 34.8 t/s | Fits in VRAM |
| Q3_K_M | 3.9 | Fair | 14.87 GB | 0.62 GB | 16.3 GB | 26.9 t/s | Fits in VRAM |
| Q3_K_L | 4.23 | Good | 16.14 GB | 0.62 GB | 17.57 GB | 24.8 t/s | Fits in VRAM |
| Q4_K_M | 4.83 | Good | 18.4 GB | 0.62 GB | 19.83 GB | 21.7 t/s | Fits in VRAM |
| Q5_K_M | 5.67 | Very good | 21.62 GB | 0.62 GB | 23.05 GB | 18.5 t/s | Fits in VRAM |
| Q6_K | 6.56 | Excellent | 25.04 GB | 0.62 GB | 26.46 GB | 2.0 t/s | Offload |
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/Qwen3-32B-GGUF?
You need about 12.92 GB of VRAM to run MaziyarPanahi/Qwen3-32B-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/Qwen3-32B-GGUF on an 8 GB GPU?
Partially. MaziyarPanahi/Qwen3-32B-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/Qwen3-32B-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/Qwen3-32B-GGUF fully on the GPU using Q2_K (about 12.92 GB).
Can I run MaziyarPanahi/Qwen3-32B-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Qwen3-32B-GGUF fully on the GPU using Q5_K_M (about 23.05 GB).
What is the best quantization for MaziyarPanahi/Qwen3-32B-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.