Qwen 2.5 VL 72B
By Alibaba · China
Updated 2026-07-13
Overview
Frontier-class open vision-language model from Alibaba, scoring 70.2 on MMMU and 88.6 on MMBench. Uses the Qwen License rather than Apache, with a 100M MAU clause.
When to pick this model
- You need frontier visual reasoning and can run a 70B-class model
- You're processing complex documents, charts, or diagrams at scale
- You need 128K context for long multimodal sessions
- Your product is below the 100M MAU threshold of the Qwen License
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 42 GB |
| Q5_K_M | 50 GB |
| Q8_0 | 78 GB |
| FP16 (no quantization) | 144 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Qwen 2.5 VL 72B spills past single consumer GPUs even at Q4_K_M (42 GB) — think dual-GPU or workstation cards. Stepping up to Q8_0 nearly doubles the footprint to 78 GB, and unquantized FP16 weights take 144 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Qwen 2.5 VL 72B needs roughly 64 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1 tokens/sec on entry-level GPUs, on the order of 6 tokens/sec on a mid-range card, and up to 20 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Qwen 2.5 VL 72B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Qwen 2.5 VL 72B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 42 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 42 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 42 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 42 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 42 GB at Q4_K_M |
Which GPU should you buy to run Qwen 2.5 VL 72B?
To run Qwen 2.5 VL 72B locally at Q4, you need ~42 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| MMMU | 70.2 |
| MathVista | 74.8 |
| MMBench-EN | 88.6 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Qwen 2.5 VL 72B in context: its MMMU score of 70.2 ranks #2 of the 8 catalog models with a published MMMU result (catalog median 62.8). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- Frontier vision benchmarks (MMMU 70.2)
- 128K context window
- Strong OCR and grounding capabilities
Limitations
- Qwen License — not Apache, has a 100M MAU clause
- 40GB+ VRAM in Q4 — multi-GPU for full precision
- Tooling support varies vs the 7B variant
Typical workloads
In our catalog grid, Qwen 2.5 VL 72B is filed under Advanced Vision, Document Analysis — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the Qwen License license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: ViT + LLM · GQA · SwiGLU · RMSNorm
Training: 72B backbone + vision encoder.
The strongest open VLM available — check the MAU clause before betting your product on it.
Quick start
ollama run qwen2.5vl:72bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Qwen 2.5 VL 72B need?
At the recommended Q4_K_M quantization, Qwen 2.5 VL 72B needs about 42 GB of VRAM. Q8_0 takes 78 GB, and unquantized FP16 weights take 144 GB.
Can Qwen 2.5 VL 72B run without a GPU?
Yes — with roughly 64 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Qwen 2.5 VL 72B support?
Qwen 2.5 VL 72B supports a 125k-token context window (128,000 tokens).
Can I use Qwen 2.5 VL 72B commercially?
Qwen 2.5 VL 72B ships under the Qwen License license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Qwen 2.5 VL 72B on consumer hardware?
Our compatibility engine estimates on the order of 6 tokens/sec on a mid-range GPU and up to 20 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Qwen 2.5 VL 72B should I download first?
Start with Q4_K_M (42 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.