Qwen2.5-72B-Instruct GGUF size and VRAM requirements
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.
Under the hood it uses 80 transformer layers, a hidden size of 8,192, 64 attention heads. It uses grouped-query attention (64 query heads sharing 8 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
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. Context length drives that memory directly: at 4,096 tokens the KV cache for Qwen2.5-72B-Instruct-GGUF is about 1.25 GB, rising to roughly 10.0 GB at its full 32,768-token context. Shorter prompts free up memory for a higher-quality quantization.
Available GGUF quantizations for bartowski/Qwen2.5-72B-Instruct-GGUF include IQ1_M, IQ2_XXS, IQ2_XS, IQ2_M, Q2_K, Q2_K_L, IQ3_XXS, Q3_K_S, IQ3_M, Q3_K_M, Q3_K_L, IQ4_XS, Q3_K_XL, Q4_0, Q4_K_M, Q5_K_M, Q6_K, Q8_0. The model supports a native context length of up to 32,768 tokens; a longer context grows the KV cache and the memory needed.
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 |
|---|---|---|---|---|---|---|---|
| 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
What kind of model is bartowski/Qwen2.5-72B-Instruct-GGUF?
bartowski/Qwen2.5-72B-Instruct-GGUF is an instruction-tuned chat model with 72.71 billion parameters, based on the qwen2 architecture. It is released under the other license and distributed as GGUF files for local inference.
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 context length does bartowski/Qwen2.5-72B-Instruct-GGUF support?
bartowski/Qwen2.5-72B-Instruct-GGUF supports a native context length of up to 32,768 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for bartowski/Qwen2.5-72B-Instruct-GGUF?
For bartowski/Qwen2.5-72B-Instruct-GGUF, a strong default is Q4_K_M, which needs about 46.21 GB and keeps most of the quality while roughly halving the memory versus 8-bit. With VRAM to spare, Q5_K_M or Q6_K add a little more quality; if you are tight on memory, a smaller quantization still runs. Pick the highest quantization that fits your VRAM.