Ornith 1.5 122B NVFP4

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

Released October 2026context N/A

Ornith 1.5 122B NVFP4 is a large open-weight text model from ornith-ai, quantized to NVIDIA's FP4 format for efficient deployment on compatible hardware. At 122 billion parameters, it sits in the heavyweight category, though the FP4 quantization means memory footprint is reduced compared to full-precision equivalents. Specific capability details beyond its text-in, text-out architecture are limited.

Capabilities

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

Factual Knowledge

Strong

Reasoning & Logic

Strong

Instruction Following

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Ornith 1.5 122B NVFP4

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

Released October 2026context N/A

Ornith 1.5 122B NVFP4 is a large open-weight text model from ornith-ai, quantized to NVIDIA's FP4 format for efficient deployment on compatible hardware. At 122 billion parameters, it sits in the heavyweight category, though the FP4 quantization means memory footprint is reduced compared to full-precision equivalents. Specific capability details beyond its text-in, text-out architecture are limited.

Capabilities

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

Factual Knowledge

Strong

Reasoning & Logic

Strong

Instruction Following

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.FP4 FormatA 4-bit floating-point number format that represents model weights with very low precision, enabling extremely efficient inference on compatible hardware.FP4 QuantizationA compression technique that represents model weights using only 4-bit floating-point numbers instead of larger formats, reducing memory usage and speeding up inference.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.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.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 ModelA language model that processes and generates only text, without support for images, audio, or other media types.Text-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.