Qwen2.5-3B-Instruct GGUF size and VRAM requirements
Qwen/Qwen2.5-3B-Instruct-GGUF is a mid-size instruction-tuned chat model with 3.09 billion parameters, built on the qwen2 architecture. It is released under the other license and has been downloaded 214,298 times.
Under the hood it uses 36 transformer layers, a hidden size of 2,048, 16 attention heads. It uses grouped-query attention (16 query heads sharing 2 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
To run Qwen/Qwen2.5-3B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 2.22 GB (Q2_K, lowest quality) and 7.27 GB (GGUF, 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 Qwen2.5-3B-Instruct-GGUF is about 0.14 GB, rising to roughly 1.12 GB at its full 32,768-token context. Shorter prompts free up memory for a higher-quality quantization.
For most users the best balance is GGUF, needing about 7.27 GB. That means Qwen/Qwen2.5-3B-Instruct-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
Available GGUF quantizations for Qwen/Qwen2.5-3B-Instruct-GGUF include Q2_K, Q3_K_M, Q4_0, Q4_K_M, Q5_0, Q5_K_M, Q6_K, Q8_0, GGUF. 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 |
|---|---|---|---|---|---|---|---|
| Q2_K | 3.57 | Fair | 1.28 GB | 0.14 GB | 2.22 GB | 311.9 t/s | Fits in VRAM |
| Q3_K_M | 4.47 | Good | 1.61 GB | 0.14 GB | 2.55 GB | 249.1 t/s | Fits in VRAM |
| Q4_0 | 5.18 | Very good | 1.86 GB | 0.14 GB | 2.8 GB | 215.0 t/s | Fits in VRAM |
| Q4_K_M | 5.46 | Very good | 1.96 GB | 0.14 GB | 2.9 GB | 204.0 t/s | Fits in VRAM |
| Q5_0 | 6.18 | Very good | 2.22 GB | 0.14 GB | 3.16 GB | 180.2 t/s | Fits in VRAM |
| Q5_K_M | 6.32 | Very good | 2.27 GB | 0.14 GB | 3.21 GB | 176.1 t/s | Fits in VRAM |
| Q6_K | 7.24 | Excellent | 2.6 GB | 0.14 GB | 3.54 GB | 153.8 t/s | Fits in VRAM |
| Q8_0 | 9.37 | Excellent | 3.37 GB | 0.14 GB | 4.31 GB | 118.8 t/s | Fits in VRAM |
| GGUF | 17.63 | Excellent | 6.33 GB | 0.14 GB | 7.27 GB | 63.2 t/s | Fits in VRAM |
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 Qwen/Qwen2.5-3B-Instruct-GGUF?
Qwen/Qwen2.5-3B-Instruct-GGUF is an instruction-tuned chat model with 3.09 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 Qwen/Qwen2.5-3B-Instruct-GGUF?
You need about 4.31 GB of VRAM to run Qwen/Qwen2.5-3B-Instruct-GGUF entirely on the GPU using the Q8_0 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run Qwen/Qwen2.5-3B-Instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run Qwen/Qwen2.5-3B-Instruct-GGUF fully on the GPU using GGUF (about 7.27 GB).
Can I run Qwen/Qwen2.5-3B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run Qwen/Qwen2.5-3B-Instruct-GGUF fully on the GPU using GGUF (about 7.27 GB).
Can I run Qwen/Qwen2.5-3B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run Qwen/Qwen2.5-3B-Instruct-GGUF fully on the GPU using GGUF (about 7.27 GB).
What context length does Qwen/Qwen2.5-3B-Instruct-GGUF support?
Qwen/Qwen2.5-3B-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 Qwen/Qwen2.5-3B-Instruct-GGUF?
For Qwen/Qwen2.5-3B-Instruct-GGUF, a strong default is Q4_K_M, which needs about 2.9 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.