JEV 35B NVFP4

JEV

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

Released October 2026context N/A

JEV 35B NVFP4 is a text-in, text-out open-weight model published by autotrust, quantized to NVFP4 precision — a format optimized for efficient inference on NVIDIA hardware. The NVFP4 quantization means it trades some numerical precision for reduced memory footprint and faster throughput. Details about its specific capabilities and intended use cases are limited beyond these technical characteristics.

Capabilities

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

Reasoning & Logic

Strong

Factual Knowledge

Strong

Creative Writing

Use Case Fit

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

JEV 35B NVFP4

JEV

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

Released October 2026context N/A

JEV 35B NVFP4 is a text-in, text-out open-weight model published by autotrust, quantized to NVFP4 precision — a format optimized for efficient inference on NVIDIA hardware. The NVFP4 quantization means it trades some numerical precision for reduced memory footprint and faster throughput. Details about its specific capabilities and intended use cases are limited beyond these technical characteristics.

Capabilities

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

Reasoning & Logic

Strong

Factual Knowledge

Strong

Creative Writing

Use Case Fit

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

Glossary

InferenceThe process of running a trained model to generate predictions or outputs from new inputs.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 that uses 4 bits per weight, developed by NVIDIA to compress models for efficient inference on consumer hardware.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.Open-Weight ModelA model whose trained weights are publicly released, allowing anyone to download and run it locally.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-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.ThroughputThe number of tokens a model can generate per second, measuring its processing speed.