Run Qwen/Qwen2.5-Coder-32B-Instruct-GGUF locally
Qwen/Qwen2.5-Coder-32B-Instruct-GGUF is a very large code-focused language model with 32.76 billion parameters, built on the qwen2 architecture. It is released under the apache-2.0 license and has been downloaded 257,209 times.
To run Qwen/Qwen2.5-Coder-32B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 24.73 GB (Q2_K, lowest quality) and 66.66 GB (Q8_0, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q6_K, needing about 51.88 GB. That means Qwen/Qwen2.5-Coder-32B-Instruct-GGUF fits entirely in the VRAM of a 32 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q2_K | 6.01 | Very good | 22.93 GB | 1.0 GB | 24.73 GB | 2.2 t/s | Offload |
| Q3_K_M | 7.78 | Excellent | 29.68 GB | 1.0 GB | 31.48 GB | 1.7 t/s | Offload |
| Q4_0 | 9.1 | Excellent | 34.72 GB | 1.0 GB | 36.52 GB | 1.4 t/s | Offload |
| Q4_K_M | 9.69 | Excellent | 36.98 GB | 1.0 GB | 38.78 GB | 1.4 t/s | Offload |
| Q5_0 | 11.06 | Excellent | 42.17 GB | 1.0 GB | 43.97 GB | 1.2 t/s | Offload |
| Q5_K_M | 11.36 | Excellent | 43.33 GB | 1.0 GB | 45.13 GB | 1.2 t/s | Offload |
| Q6_K | 13.13 | Excellent | 50.08 GB | 1.0 GB | 51.88 GB | 1.0 t/s | Offload |
| GGUF | 16.0 | Excellent | 61.04 GB | 1.0 GB | 62.84 GB | — | Insufficient |
| Q8_0 | 17.0 | Excellent | 64.86 GB | 1.0 GB | 66.66 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 Qwen/Qwen2.5-Coder-32B-Instruct-GGUF?
You need about 31.48 GB of VRAM to run Qwen/Qwen2.5-Coder-32B-Instruct-GGUF entirely on the GPU using the Q3_K_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run Qwen/Qwen2.5-Coder-32B-Instruct-GGUF on an 8 GB GPU?
No. Qwen/Qwen2.5-Coder-32B-Instruct-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run Qwen/Qwen2.5-Coder-32B-Instruct-GGUF on a 16 GB GPU?
Partially. Qwen/Qwen2.5-Coder-32B-Instruct-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q5_K_M), which runs but is slower.
Can I run Qwen/Qwen2.5-Coder-32B-Instruct-GGUF on a 24 GB GPU?
Partially. Qwen/Qwen2.5-Coder-32B-Instruct-GGUF only fits on a 24 GB GPU by offloading part of it to system RAM (with Q8_0), which runs but is slower.
What is the best quantization for Qwen/Qwen2.5-Coder-32B-Instruct-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.