Run bartowski/Qwen2.5-32B-Instruct-GGUF locally
Qwen2.5-32B-Instruct is a 32.76 billion parameter AI model from the Qwen family, designed for instruction-following tasks. It operates under the Apache-2.0 license, enabling open use and modification. The model is optimized for deployment via tools like LM Studio, leveraging quantization methods for accessibility without compromising core functionality.
To run bartowski/Qwen2.5-32B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 10.21 GB (IQ2_XXS, lowest quality) and 62.84 GB (F16, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q5_K_L, needing about 23.91 GB. That means bartowski/Qwen2.5-32B-Instruct-GGUF fits entirely in the VRAM of a 12 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| IQ2_XXS | 2.2 | Very low | 8.41 GB | 1.0 GB | 10.21 GB | 5.9 t/s | Offload |
| IQ2_XS | 2.43 | Very low | 9.27 GB | 1.0 GB | 11.07 GB | 5.4 t/s | Offload |
| IQ2_S | 2.54 | Very low | 9.67 GB | 1.0 GB | 11.47 GB | 5.2 t/s | Offload |
| IQ2_M | 2.75 | Low | 10.49 GB | 1.0 GB | 12.29 GB | 4.8 t/s | Offload |
| Q2_K | 3.01 | Low | 11.47 GB | 1.0 GB | 13.27 GB | 4.4 t/s | Offload |
| Q2_K_L | 3.19 | Low | 12.18 GB | 1.0 GB | 13.98 GB | 4.1 t/s | Offload |
| IQ3_XS | 3.35 | Fair | 12.76 GB | 1.0 GB | 14.56 GB | 3.9 t/s | Offload |
| Q3_K_S | 3.51 | Fair | 13.4 GB | 1.0 GB | 15.2 GB | 3.7 t/s | Offload |
| IQ3_M | 3.62 | Fair | 13.79 GB | 1.0 GB | 15.59 GB | 3.6 t/s | Offload |
| Q3_K_M | 3.89 | Fair | 14.84 GB | 1.0 GB | 16.64 GB | 3.4 t/s | Offload |
| Q3_K_L | 4.21 | Good | 16.06 GB | 1.0 GB | 17.86 GB | 3.1 t/s | Offload |
| IQ4_XS | 4.32 | Good | 16.48 GB | 1.0 GB | 18.28 GB | 3.0 t/s | Offload |
| Q3_K_XL | 4.38 | Good | 16.7 GB | 1.0 GB | 18.5 GB | 3.0 t/s | Offload |
| Q4_0_4_4 | 4.55 | Good | 17.36 GB | 1.0 GB | 19.16 GB | 2.9 t/s | Offload |
| Q4_0_4_8 | 4.55 | Good | 17.36 GB | 1.0 GB | 19.16 GB | 2.9 t/s | Offload |
| Q4_0_8_8 | 4.55 | Good | 17.36 GB | 1.0 GB | 19.16 GB | 2.9 t/s | Offload |
| Q4_0 | 4.57 | Good | 17.43 GB | 1.0 GB | 19.23 GB | 2.9 t/s | Offload |
| Q4_K_S | 4.59 | Good | 17.49 GB | 1.0 GB | 19.29 GB | 2.9 t/s | Offload |
| Q4_K_M | 4.85 | Good | 18.49 GB | 1.0 GB | 20.29 GB | 2.7 t/s | Offload |
| Q4_K_L | 4.99 | Good | 19.03 GB | 1.0 GB | 20.83 GB | 2.6 t/s | Offload |
| Q5_K_S | 5.53 | Very good | 21.08 GB | 1.0 GB | 22.88 GB | 2.4 t/s | Offload |
| Q5_K_M | 5.68 | Very good | 21.66 GB | 1.0 GB | 23.46 GB | 2.3 t/s | Offload |
| Q5_K_L | 5.8 | Very good | 22.11 GB | 1.0 GB | 23.91 GB | 2.3 t/s | Offload |
| Q6_K | 6.56 | Excellent | 25.04 GB | 1.0 GB | 26.84 GB | — | Insufficient |
| Q6_K_L | 6.66 | Excellent | 25.39 GB | 1.0 GB | 27.19 GB | — | Insufficient |
| Q8_0 | 8.5 | Excellent | 32.43 GB | 1.0 GB | 34.23 GB | — | Insufficient |
| F16 | 16.0 | Excellent | 61.04 GB | 1.0 GB | 62.84 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 bartowski/Qwen2.5-32B-Instruct-GGUF?
You need about 11.47 GB of VRAM to run bartowski/Qwen2.5-32B-Instruct-GGUF entirely on the GPU using the IQ2_S quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run bartowski/Qwen2.5-32B-Instruct-GGUF on an 8 GB GPU?
Partially. bartowski/Qwen2.5-32B-Instruct-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with Q5_K_L), which runs but is slower.
Can I run bartowski/Qwen2.5-32B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run bartowski/Qwen2.5-32B-Instruct-GGUF fully on the GPU using IQ3_M (about 15.59 GB).
Can I run bartowski/Qwen2.5-32B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run bartowski/Qwen2.5-32B-Instruct-GGUF fully on the GPU using Q5_K_L (about 23.91 GB).
What is the best quantization for bartowski/Qwen2.5-32B-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.