Run bartowski/Qwen2.5-72B-Instruct-GGUF locally
Qwen2.5-72B-Instruct is a large language model from the Qwen family with 72.71 billion parameters, designed for instruction-following tasks. It is optimized for general-purpose text generation and interaction, leveraging the foundational architecture of the Qwen series. The model is available through Hugging Face for research and application use.
To run bartowski/Qwen2.5-72B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 24.16 GB (IQ1_M, lowest quality) and 74.01 GB (Q8_0, highest quality) of memory, weights plus KV cache and a system margin included.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| IQ1_M | 2.61 | Low | 22.11 GB | 1.25 GB | 24.16 GB | — | Insufficient |
| IQ2_XXS | 2.8 | Low | 23.74 GB | 1.25 GB | 25.79 GB | — | Insufficient |
| IQ2_XS | 2.98 | Low | 25.2 GB | 1.25 GB | 27.25 GB | — | Insufficient |
| IQ2_M | 3.23 | Low | 27.32 GB | 1.25 GB | 29.37 GB | — | Insufficient |
| Q2_K | 3.28 | Low | 27.76 GB | 1.25 GB | 29.81 GB | — | Insufficient |
| Q2_K_L | 3.41 | Fair | 28.9 GB | 1.25 GB | 30.95 GB | — | Insufficient |
| IQ3_XXS | 3.5 | Fair | 29.66 GB | 1.25 GB | 31.71 GB | — | Insufficient |
| Q3_K_S | 3.79 | Fair | 32.12 GB | 1.25 GB | 34.17 GB | — | Insufficient |
| IQ3_M | 3.91 | Fair | 33.07 GB | 1.25 GB | 35.12 GB | — | Insufficient |
| Q3_K_M | 4.15 | Fair | 35.11 GB | 1.25 GB | 37.16 GB | — | Insufficient |
| Q3_K_L | 4.35 | Good | 36.79 GB | 1.25 GB | 38.84 GB | — | Insufficient |
| IQ4_XS | 4.37 | Good | 36.98 GB | 1.25 GB | 39.03 GB | — | Insufficient |
| Q3_K_XL | 4.47 | Good | 37.81 GB | 1.25 GB | 39.86 GB | — | Insufficient |
| Q4_0 | 4.55 | Good | 38.54 GB | 1.25 GB | 40.59 GB | — | Insufficient |
| Q4_K_M | 5.22 | Very good | 44.16 GB | 1.25 GB | 46.21 GB | — | Insufficient |
| Q5_K_M | 5.99 | Very good | 50.71 GB | 1.25 GB | 52.76 GB | — | Insufficient |
| Q6_K | 7.08 | Excellent | 59.93 GB | 1.25 GB | 61.98 GB | — | Insufficient |
| Q8_0 | 8.5 | Excellent | 71.96 GB | 1.25 GB | 74.01 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/Qwen2.5-72B-Instruct-GGUF?
You need about 31.71 GB of VRAM to run bartowski/Qwen2.5-72B-Instruct-GGUF entirely on the GPU using the IQ3_XXS quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run bartowski/Qwen2.5-72B-Instruct-GGUF on an 8 GB GPU?
No. bartowski/Qwen2.5-72B-Instruct-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run bartowski/Qwen2.5-72B-Instruct-GGUF on a 16 GB GPU?
Partially. bartowski/Qwen2.5-72B-Instruct-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q4_K_M), which runs but is slower.
Can I run bartowski/Qwen2.5-72B-Instruct-GGUF on a 24 GB GPU?
Partially. bartowski/Qwen2.5-72B-Instruct-GGUF only fits on a 24 GB GPU by offloading part of it to system RAM (with Q6_K), which runs but is slower.
What is the best quantization for bartowski/Qwen2.5-72B-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.