Solar Open2 250B Nota NVFP4

Solar

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

Released July 20261049K context≈ 786,432 words

A massive 250B parameter open-weight model from Nota AI, Solar Open2 250B Nota NVFP4 is quantized using NVIDIA's FP4 format, making it more practical to deploy despite its enormous size. It handles text-in, text-out tasks across a very long context window of over one million tokens — useful for processing large documents or extended conversations. Details about its specific capability profile beyond scale and quantization format are limited.

Capabilities

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

Long Context

Exceptional

Reasoning & Logic

Solar Open2 250B Nota NVFP4

Solar

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

Released July 20261049K context≈ 786,432 words

A massive 250B parameter open-weight model from Nota AI, Solar Open2 250B Nota NVFP4 is quantized using NVIDIA's FP4 format, making it more practical to deploy despite its enormous size. It handles text-in, text-out tasks across a very long context window of over one million tokens — useful for processing large documents or extended conversations. Details about its specific capability profile beyond scale and quantization format are limited.

Capabilities

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

Long Context

Exceptional

Reasoning & Logic

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.FP4 FormatA 4-bit floating-point number format that represents model weights with very low precision, enabling extremely efficient inference on compatible hardware.Open-Weight ModelA model whose trained weights are publicly released, allowing anyone to download and run it locally.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.Text-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.TokensThe basic units of text that a language model processes, typically representing words or word fragments.

Capabilities

Capabilities

Use Case Fit

Glossary