Qwen2.5-VL-7B-Instruct-NVFP4
The NVIDIA Qwen2.5-VL-7B-Instruct-FP4 model is the quantized version of Alibaba's Qwen2.5-VL-7B-Instruct model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen2.5-VL-7B-Instruct-FP4 model is quantized with TensorRT Model Optimizer.
Params
5.0 B
Context
128,000
Downloads 30d
1.2 M
Likes
28
Download history
daily snapshots · 54 days
▲ 253 K in the last 30 days (17.1%)
1.7 M1.2 M
Oct 12Oct 22Nov 1Nov 10
1.7 M219 K
Sep 18Oct 6Oct 24Nov 10
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | u8 | 7.2 GB | 9.2 GB | ✅ Runs comfortably |
| model.safetensors (bf16, full) | bf16 + 125K ctx | 7.2 GB | 20.2 GB | ⚠️ Tight — reduce context |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Run it
copy-paste, exact tags checked against the Hub$ curl -s https://aimodelscomparison.com/api/v1/models/qwen2-5-vl-7b-instruct-nvfp4
{
"hf_id": "nvidia/Qwen2.5-VL-7B-Instruct-NVFP4",
"params_b": 5.03,
"context_length": 128000,
"license": { "id": "other", "commercial": "unknown" },
"downloads_30d": 1227301,
"vram_estimates": [
{ "quant": "u8", "gb": 9.2 }
],
"updated_at": "2026-09-18T01:00:30Z"
}
Specifications
- Architecture
- Qwen2_5_VLForConditionalGeneration
- Parameters
- 5.0 B
- Tensor type
- U8
- Context length
- 128,000
- Vocabulary
- 152,064
- Layers / heads
- 28 / 28
- Licence
- other
- First seen on the Hub
- 2025-09-10
- Base model
- Qwen2.5-VL-7B-Instruct
- Training datasets
- undisclosed
- Added to our catalog
- 2026-09-18
Family
Base model and the most-downloaded derivatives in the catalog.
Compare with any text-generation model