AutoTrust Decider Pro 35B NVFP4

JEV

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

Released October 2026context N/A

AutoTrust Decider Pro 35B NVFP4 is a 35-billion-parameter open-weight text model from the JEV family, quantized to NVFP4 precision — a format that trades some numerical fidelity for reduced memory footprint. Details about its specific capabilities and intended use cases are limited beyond what the name and format suggest. The NVFP4 quantization makes it more accessible for deployment on constrained hardware compared to full-precision variants.

AutoTrust Decider Pro 35B NVFP4

JEV

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

Released October 2026context N/A

AutoTrust Decider Pro 35B NVFP4 is a 35-billion-parameter open-weight text model from the JEV family, quantized to NVFP4 precision — a format that trades some numerical fidelity for reduced memory footprint. Details about its specific capabilities and intended use cases are limited beyond what the name and format suggest. The NVFP4 quantization makes it more accessible for deployment on constrained hardware compared to full-precision variants.

Glossary

FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.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.NVFP4 PrecisionA low-precision numerical format optimized by NVIDIA that uses fewer bits per number than standard formats, enabling efficient inference on NVIDIA GPUs while maintaining reasonable accuracy.NVFP4 PrecisionA low-precision numerical format that uses 4 bits per weight, developed by NVIDIA to compress models for efficient inference on consumer hardware.Numerical FidelityThe accuracy and precision with which a model preserves mathematical calculations; lower fidelity means some precision is lost, often as a trade-off for smaller model size.ParameterA learned value inside a neural network that the model adjusts during training to improve its predictions.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.