Qwen2.5-Coder-32B GGUF size and VRAM requirements
lmstudio-community/Qwen2.5-Coder-32B-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 11,565 times.
To run lmstudio-community/Qwen2.5-Coder-32B-GGUF locally at a 4,096-token context, its quantized versions need between 17.86 GB (Q3_K_L, lowest quality) and 34.23 GB (Q8_0, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q4_K_M, needing about 20.29 GB. That means lmstudio-community/Qwen2.5-Coder-32B-GGUF fits entirely in the VRAM of a 24 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 |
|---|---|---|---|---|---|---|---|
| Q3_K_L | 4.21 | Good | 16.06 GB | 1.0 GB | 17.86 GB | 3.1 t/s | Offload |
| Q4_K_M | 4.85 | Good | 18.49 GB | 1.0 GB | 20.29 GB | 2.7 t/s | Offload |
| Q6_K | 6.56 | Excellent | 25.04 GB | 1.0 GB | 26.84 GB | — | Insufficient |
| Q8_0 | 8.5 | Excellent | 32.43 GB | 1.0 GB | 34.23 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 lmstudio-community/Qwen2.5-Coder-32B-GGUF?
You need about 20.29 GB of VRAM to run lmstudio-community/Qwen2.5-Coder-32B-GGUF entirely on the GPU using the Q4_K_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run lmstudio-community/Qwen2.5-Coder-32B-GGUF on an 8 GB GPU?
Partially. lmstudio-community/Qwen2.5-Coder-32B-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with Q4_K_M), which runs but is slower.
Can I run lmstudio-community/Qwen2.5-Coder-32B-GGUF on a 16 GB GPU?
Partially. lmstudio-community/Qwen2.5-Coder-32B-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q8_0), which runs but is slower.
Can I run lmstudio-community/Qwen2.5-Coder-32B-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run lmstudio-community/Qwen2.5-Coder-32B-GGUF fully on the GPU using Q4_K_M (about 20.29 GB).
What is the best quantization for lmstudio-community/Qwen2.5-Coder-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.