Qwen Qwen 2.5 VL 32B Instruct GGUF Size & VRAM Requirements
Qwen_Qwen2.5-VL-32B-Instruct-GGUF is a very large instruction-tuned chat model focused on vision & multimodal analysis. With approximately 33.45 billion parameters, it balances local hardware requirements with reasoning and generation fidelity. It is built on the qwen2vl architecture with a native context window of up to 128,000 tokens. The model is primarily recommended for document parsing (pdfs, invoices, receipts), diagram interpretation, screenshot inspection, and visual q&a. Released under the apache-2.0 license, it can be executed fully offline without sending data to external APIs.
Under the hood it uses 64 transformer layers, a hidden size of 5,120, 40 attention heads. It uses grouped-query attention (40 query heads sharing 8 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
To run bartowski/Qwen_Qwen2.5-VL-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 (BF16, 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 Qwen_Qwen2.5-VL-32B-Instruct-GGUF is about 1.0 GB, rising to roughly 31.25 GB at its full 128,000-token context. Shorter prompts free up memory for a higher-quality quantization.
For most users the best balance is Q5_K_L, needing about 23.91 GB. That means bartowski/Qwen_Qwen2.5-VL-32B-Instruct-GGUF fits entirely in the VRAM of a 12 GB GPU or larger, running fully on the GPU.
Available GGUF quantizations for bartowski/Qwen_Qwen2.5-VL-32B-Instruct-GGUF include IQ2_XXS, IQ2_XS, IQ2_S, IQ2_M, Q2_K, IQ3_XXS, Q2_K_L, IQ3_XS, Q3_K_S, IQ3_M, Q3_K_M, Q3_K_L, IQ4_XS, Q3_K_XL, IQ4_NL, Q4_0, Q4_K_S, Q4_K_M, Q4_K_L, Q4_1, Q5_K_S, Q5_K_M, Q5_K_L, Q6_K, Q6_K_L, Q8_0, BF16. The model supports a native context length of up to 128,000 tokens; a longer context grows the KV cache and the memory needed.
Recommended Use Cases & Local Setup
Ideal for: Document parsing (PDFs, invoices, receipts), diagram interpretation, screenshot inspection, and visual Q&A.
Desktop interface with drag-and-drop image and screenshot support
Production-grade multi-modal server using mmproj companion weights
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 |
|---|---|---|---|---|---|---|---|
| IQ2_XXS | 2.16 | Very low | 8.41 GB | 1.0 GB | 10.21 GB | 5.9 t/s | Offload |
| IQ2_XS | 2.38 | Very low | 9.27 GB | 1.0 GB | 11.07 GB | 5.4 t/s | Offload |
| IQ2_S | 2.48 | Very low | 9.67 GB | 1.0 GB | 11.47 GB | 5.2 t/s | Offload |
| IQ2_M | 2.69 | Low | 10.49 GB | 1.0 GB | 12.29 GB | 4.8 t/s | Offload |
| Q2_K | 2.94 | Low | 11.47 GB | 1.0 GB | 13.27 GB | 4.4 t/s | Offload |
| IQ3_XXS | 3.07 | Low | 11.96 GB | 1.0 GB | 13.76 GB | 4.2 t/s | Offload |
| Q2_K_L | 3.13 | Low | 12.18 GB | 1.0 GB | 13.98 GB | 4.1 t/s | Offload |
| IQ3_XS | 3.28 | Low | 12.76 GB | 1.0 GB | 14.56 GB | 3.9 t/s | Offload |
| Q3_K_S | 3.44 | Fair | 13.4 GB | 1.0 GB | 15.2 GB | 3.7 t/s | Offload |
| IQ3_M | 3.54 | Fair | 13.79 GB | 1.0 GB | 15.59 GB | 3.6 t/s | Offload |
| Q3_K_M | 3.81 | Fair | 14.84 GB | 1.0 GB | 16.64 GB | 3.4 t/s | Offload |
| Q3_K_L | 4.12 | Fair | 16.06 GB | 1.0 GB | 17.86 GB | 3.1 t/s | Offload |
| IQ4_XS | 4.23 | Good | 16.48 GB | 1.0 GB | 18.28 GB | 3.0 t/s | Offload |
| Q3_K_XL | 4.29 | Good | 16.7 GB | 1.0 GB | 18.5 GB | 3.0 t/s | Offload |
| IQ4_NL | 4.47 | Good | 17.4 GB | 1.0 GB | 19.2 GB | 2.9 t/s | Offload |
| Q4_0 | 4.47 | Good | 17.43 GB | 1.0 GB | 19.23 GB | 2.9 t/s | Offload |
| Q4_K_S | 4.49 | Good | 17.49 GB | 1.0 GB | 19.29 GB | 2.9 t/s | Offload |
| Q4_K_M | 4.75 | Good | 18.49 GB | 1.0 GB | 20.29 GB | 2.7 t/s | Offload |
| Q4_K_L | 4.89 | Good | 19.03 GB | 1.0 GB | 20.83 GB | 2.6 t/s | Offload |
| Q4_1 | 4.94 | Good | 19.22 GB | 1.0 GB | 21.02 GB | 2.6 t/s | Offload |
| Q5_K_S | 5.41 | Very good | 21.08 GB | 1.0 GB | 22.88 GB | 2.4 t/s | Offload |
| Q5_K_M | 5.56 | Very good | 21.66 GB | 1.0 GB | 23.46 GB | 2.3 t/s | Offload |
| Q5_K_L | 5.68 | Very good | 22.11 GB | 1.0 GB | 23.91 GB | 2.3 t/s | Offload |
| Q6_K | 6.43 | Very good | 25.04 GB | 1.0 GB | 26.84 GB | — | Insufficient |
| Q6_K_L | 6.52 | Excellent | 25.39 GB | 1.0 GB | 27.19 GB | — | Insufficient |
| Q8_0 | 8.33 | Excellent | 32.43 GB | 1.0 GB | 34.23 GB | — | Insufficient |
| BF16 | 15.67 | 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
What kind of model is bartowski/Qwen_Qwen2.5-VL-32B-Instruct-GGUF?
bartowski/Qwen_Qwen2.5-VL-32B-Instruct-GGUF is an instruction-tuned chat model with 33.45 billion parameters, based on the qwen2vl architecture. It is released under the apache-2.0 license and distributed as GGUF files for local inference.
How much VRAM do you need to run bartowski/Qwen_Qwen2.5-VL-32B-Instruct-GGUF?
You need about 11.47 GB of VRAM to run bartowski/Qwen_Qwen2.5-VL-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/Qwen_Qwen2.5-VL-32B-Instruct-GGUF on an 8 GB GPU?
Partially. bartowski/Qwen_Qwen2.5-VL-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/Qwen_Qwen2.5-VL-32B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run bartowski/Qwen_Qwen2.5-VL-32B-Instruct-GGUF fully on the GPU using IQ3_M (about 15.59 GB).
Can I run bartowski/Qwen_Qwen2.5-VL-32B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run bartowski/Qwen_Qwen2.5-VL-32B-Instruct-GGUF fully on the GPU using Q5_K_L (about 23.91 GB).
What context length does bartowski/Qwen_Qwen2.5-VL-32B-Instruct-GGUF support?
bartowski/Qwen_Qwen2.5-VL-32B-Instruct-GGUF supports a native context length of up to 128,000 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/Qwen_Qwen2.5-VL-32B-Instruct-GGUF?
For bartowski/Qwen_Qwen2.5-VL-32B-Instruct-GGUF, a strong default is Q4_K_M, which needs about 20.29 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.