Qwen2.5 VL 7B Instruct — Hardware Requirements & GPU Compatibility
Vision
Qwen2.5 VL 7B Instruct is Alibaba's 8.3-billion-parameter vision-language model in the Qwen 2.5 lineup, built to handle images and video alongside text in a single pass. It can read documents, charts, and screenshots, describe and reason about visual content, and point out object locations within an image, useful for document understanding, visual question answering, and lightweight visual-agent tasks. At this parameter count, local inference is practical on a single mainstream or high-end consumer GPU once the weights are quantized. The model supports a 128K token context window, enough for long documents or extended multi-turn visual conversations. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, and was published in January 2025 as part of Qwen's second-generation vision-language line, later succeeded by Qwen3-VL.
6.8M downloads
1.7K likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 8.3B
- Architecture
- Qwen2_5_VLForConditionalGeneration
- Context Length
- 128,000 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-01-26
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 VL 7B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 17 GB | 24.2 GB | 16.58 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Qwen2.5 VL 7B Instruct?
BF16 · 17 GBQwen2.5 VL 7B Instruct (BF16) requires 17 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. Using the full 128K context window can add up to 7.2 GB, bringing total usage to 24.2 GB. 8 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 7B Instruct?
BF16 · 17 GB41 devices with unified memory can run Qwen2.5 VL 7B 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 7B 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 7B Instruct need?
Qwen2.5 VL 7B Instruct requires 17 GB of VRAM at BF16. Full 128K context adds up to 7.2 GB (24.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.3B × 16 bits ÷ 8 = 16.6 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7.6 GB (at full 128K context)
VRAM usage by quantization
BF1617.0 GBBF16 + full context24.2 GB- Can I run Qwen2.5 VL 7B Instruct on a Mac?
Qwen2.5 VL 7B Instruct requires at least 17 GB at BF16, 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 7B Instruct locally?
Yes — Qwen2.5 VL 7B Instruct can run locally on consumer hardware. At BF16 quantization it needs 17 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 VL 7B Instruct?
At BF16, Qwen2.5 VL 7B Instruct can reach ~282 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~39 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 ÷ 17.0 × 0.65 = ~306 tok/s
Estimated speed at BF16 (17 GB)
~306 tok/s~39 tok/s~306 tok/s~282 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 7B Instruct?
At BF16, the download is about 16.58 GB.
- Which GPUs can run Qwen2.5 VL 7B Instruct?
8 consumer GPUs can run Qwen2.5 VL 7B Instruct at BF16 (17 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Qwen2.5 VL 7B Instruct?
41 devices with unified memory can run Qwen2.5 VL 7B Instruct at BF16 (17 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.