Qwen2.5 VL 32B Instruct — Hardware Requirements & GPU Compatibility
Vision
Qwen2.5 VL 32B Instruct is a 33.5-billion-parameter vision-language model from Alibaba's Qwen team, able to process images and text together for document parsing, chart reading, and visual question answering. It was tuned with reinforcement learning on top of the original Qwen2.5-VL release for more detailed, better-formatted answers and sharper accuracy on math and visual-logic problems. At this size, local inference needs quantization and a single high-end 24-32GB-class consumer or workstation GPU rather than lower-end hardware. The model supports a 128K token context window, enough for lengthy documents or multi-image inputs. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, and was published in March 2025 as a mid-sized addition to the Qwen2.5-VL lineup, between the smaller 7B model and the flagship 72B version.
1.1M downloads
501 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 33.5B
- Architecture
- Qwen2_5_VLForConditionalGeneration
- Context Length
- 128,000 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-03-21
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 VL 32B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 15.1 GB | 48.1 GB | 14.22 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 15.5 GB | 48.5 GB | 14.64 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 17.1 GB | 50.2 GB | 16.31 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 17.6 GB | 50.6 GB | 16.73 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 20.9 GB | 53.9 GB | 20.07 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 24.7 GB | 57.7 GB | 23.84 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 28.4 GB | 61.5 GB | 27.60 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 34.3 GB | 67.3 GB | 33.45 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen2.5 VL 32B Instruct?
Q4_K_M · 20.9 GBQwen2.5 VL 32B Instruct (Q4_K_M) requires 20.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ GB is recommended. Using the full 128K context window can add up to 33.0 GB, bringing total usage to 53.9 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Qwen2.5 VL 32B Instruct?
Q4_K_M · 20.9 GB41 devices with unified memory can run Qwen2.5 VL 32B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Qwen2.5 VL 32B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Qwen2.5 VL 32B Instruct need?
Qwen2.5 VL 32B Instruct requires 20.9 GB of VRAM at Q4_K_M, or 67.7 GB at BF16. Full 128K context adds up to 33.0 GB (53.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 33.5B × 4.8 bits ÷ 8 = 20.1 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 33.8 GB (at full 128K context)
VRAM usage by quantization
Q4_K_M20.9 GBQ4_K_M + full context53.9 GB- Can NVIDIA GeForce RTX 4090 run Qwen2.5 VL 32B Instruct?
Yes, at Q5_K_S (23.8 GB) or lower. Higher quantizations like Q5_K_M (24.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen2.5 VL 32B Instruct?
For Qwen2.5 VL 32B Instruct, Q4_K_M (20.9 GB) offers the best balance of quality and VRAM usage. Q4_K_L (21.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 10.0 GB.
VRAM requirement by quantization
IQ2_XXS10.0 GBQ2_K15.1 GBQ4_119.6 GBQ4_K_M ★20.9 GBQ5_123.8 GBBF1667.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 VL 32B Instruct on a Mac?
Qwen2.5 VL 32B Instruct requires at least 10.0 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Qwen2.5 VL 32B Instruct locally?
Yes — Qwen2.5 VL 32B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 20.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 VL 32B Instruct?
At Q4_K_M, Qwen2.5 VL 32B Instruct can reach ~230 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~31 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 20.9 × 0.65 = ~249 tok/s
Estimated speed at Q4_K_M (20.9 GB)
~249 tok/s~31 tok/s~249 tok/s~230 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen2.5 VL 32B Instruct?
At Q4_K_M, the download is about 20.07 GB. The full-precision BF16 version is 66.91 GB. The smallest option (IQ2_XXS) is 9.20 GB.
- Which GPUs can run Qwen2.5 VL 32B Instruct?
7 consumer GPUs can run Qwen2.5 VL 32B Instruct at Q4_K_M (20.9 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Qwen2.5 VL 32B Instruct?
41 devices with unified memory can run Qwen2.5 VL 32B Instruct at Q4_K_M (20.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.