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.
Decent on a 24 GB GPU
Runs from 16 GB (IQ2_M · 16K)Best on 24 GB (Q4_K_M · ~31 tok/s · 8K)Macs from 18 GB (IQ2_XXS · ~11 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen2.5 VL 32B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/bartowski/Qwen_Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_MQ4_K_M · needs ~24.7 GB · 16K context
Download: Qwen_Qwen2.5-VL-32B-Instruct-Q4_K_M.gguf19.85 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 32 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 5090 (32 GB) (7 GB headroom) · Q4_K_M
- Generation speed
- ~56 tok/s
- generation speed
- Cost per 1M output tokens
- $2.48
- per 1M output tokens
How Much VRAM Does Qwen2.5 VL 32B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 15.1 GB | 23.1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~44 tok/s | |
| 15.5 GB | 23.5 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~42 tok/s | |
| 17.1 GB | 25.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~38 tok/s | |
| 17.6 GB | 25.6 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 16K context · ~37 tok/s | |
| 20.9 GB | 29 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~31 tok/s | |
| 24.7 GB | 32.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 28.4 GB | |
| 28.4 GB | 36.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 32.2 GB | |
| 34.3 GB | 42.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 38.1 GB |
Selected Q4_K_M
4-bit medium quantization24.7GBat 16K context
- Weights
- 20.1 GB
- KV cache
- 4.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · fits up to 8K context · ~31 tok/s
≥50 tok/s needs ≥1.61 TB/s · RTX 5090 ~56
Which Hardware 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. 100 devices with unified memory can run Qwen2.5 VL 32B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
Q4_K_M · best fit IQ3_M (19.7 GB)
1 GPU
RX 7900 XT~30Simulate output on RX 7900 XT32–40 GB
fits · 7.3 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~56Simulate output on RTX 5090M5 Max36 GB~15Simulate output on Mac Studio M5 Max (36 GB)M532 GB~5Simulate output on MacBook Air 13" M5 (32 GB)Orin~6Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~4Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen2.5 VL 32B Instruct
Qwen 2.5 family · 44 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Nanbeige4.3 33BNanbeige | — | 23.7 GB | ~32 | same size |
| Sage A1.5InternScience | — | 22 GB | ~151 | same size |
| MiniCPM V 4.7 35B A3BOpenBMB | — | 22.1 GB | ~169 | same size |
| Llm Jp 4.1 32B A3b Thinkingllm-jp | — | 20.1 GB | ~188 | same size |
| Llm Jp 4.1 33B Thinkingllm-jp | — | 22.5 GB | ~35 | same size |
| Agnes 3.0 FlashAgnes-AI | — | 22.1 GB | ~35 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 33.8 GB (at full 128K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Qwen2.5 VL 32B Instruct?
Yes, at Q5_1 (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
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen2.5 VL 32B Instruct on a Mac?
Yes — MacBook Pro 14" M3 Pro (18 GB) and 108 other Macs can run Qwen2.5 VL 32B Instruct, the smallest at IQ2_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 20.9 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M1 Max (32 GB).
Can I run Qwen2.5 VL 32B Instruct locally?
Yes — Qwen2.5 VL 32B Instruct can run locally on consumer hardware. 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)
Real-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. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Qwen2.5 VL 32B Instruct?
100 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.