Alibaba · Qwen2.5-VL
Qwen2.5-VL-32B-Instruct
An Apache-2.0 vision-language model that reads scanned documents, forms and IDs; the usual pick when OCR-free extraction is required.
Apache-2.0Dense128K contextVision
1,121,845 downloads · 501 likes · Model card · GitHub · synced 2026-09-19
Specifications
- Parameters
- 33.5B
- Architecture
- dense · qwen2_5_vl
- Layers
- 64
- Hidden size
- 5,120
- KV heads · head dim
- 8 · 128
- Vocabulary
- 152,064
- Native dtype
- bfloat16
- Context window
- 128,000 tokens
- Released
- 2025-03-21
- Last modified on Hub
- 2025-04-14
Features & licensing
- License
- Apache-2.0
- Commercial use
- Yes
- Modalities
- text, vision
- Tool / function calling
- No
- Reasoning mode
- No
- Languages
- EN, ZH, AR, FR, DE, ES
- Quantised variants on Hub
- AWQ, EXL2, FP8, GGUF, bitsandbytes
- Pipeline
- image-text-to-text
transformerssafetensorsqwen2_5_vlimage-text-to-textmultimodalconversationaleneval-resultstext-generation-inferenceendpoints_compatible
Task fit
Editorial scores (0–100) used by the recommendation engine.
- Chat80
- RAG / Q&A78
- Code70
- Summarisation78
- Extraction88
- Agents72
Hardware to run Qwen2.5-VL-32B-Instruct
Weights need about 74 GB at FP16, 39 GB at INT8 and 22 GB at INT4. The KV cache adds roughly 262 MB per 1,000 tokens per request. For a reference workload of 5 requests per second with 1,500 input and 300 output tokens, total GPU memory is around 36.2 GB, which fits on 1 × NVIDIA A100 (80 GB).
| Configuration | VRAM | Utilisation | Est. first token | Cloud / month |
|---|---|---|---|---|
| 2 × NVIDIA L4 (24 GB) | 48 GB | 75 % | ~0.5 s | $1,168 |
| 1 × NVIDIA L40S (48 GB) | 48 GB | 75 % | ~0.2 s | $1,387 |
| 1 × NVIDIA A100 (80 GB)recommended | 80 GB | 45 % | ~0.1 s | $2,336 |
| 1 × NVIDIA H100 (80 GB) | 80 GB | 45 % | ~0.0 s | $3,285 |
Estimates only. Run an assessment for your own traffic.
Use a vision-language model when inputs are images or scans. Budget extra memory for image tokens in the KV cache.