Qwen2.5 VL 7B Instruct NVFP4

Name: Qwen2.5 VL 7B Instruct NVFP4
Author: NVIDIA

by NVIDIAQwen 2.5

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released February 2025128K context≈ 96,000 words7B params

A compact vision-language model that handles both text and image inputs within a generous 128k token context window. At 7B parameters, it sits in the efficient tier — capable enough for many real-world tasks while remaining deployable on modest hardware. The NVFP4 designation indicates NVIDIA's 4-bit floating point quantization, which trades a small amount of precision for significantly reduced memory footprint and faster inference.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Reasoning & Logic

Moderate

Long Context

Qwen2.5 VL 7B Instruct NVFP4

by NVIDIAQwen 2.5

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released February 2025128K context≈ 96,000 words7B params

A compact vision-language model that handles both text and image inputs within a generous 128k token context window. At 7B parameters, it sits in the efficient tier — capable enough for many real-world tasks while remaining deployable on modest hardware. The NVFP4 designation indicates NVIDIA's 4-bit floating point quantization, which trades a small amount of precision for significantly reduced memory footprint and faster inference.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Reasoning & Logic

Moderate

Long Context

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Language ModelAn AI model trained to predict and generate text by learning patterns from large amounts of written data.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.Vision-LanguageA model designed to understand and reason about both visual content (images) and natural language text together.Vision-Language ModelAn AI model that understands both images and text, allowing it to answer questions about images or describe what it sees.

Capabilities

Capabilities

Use Case Fit

Glossary