Kimi K3 NVFP4

Name: Kimi K3 NVFP4
Author: NVIDIA

by NVIDIA

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

Released July 2026context N/A

Kimi K3 NVFP4 is a quantized text-to-text model optimized for NVIDIA hardware, using FP4 precision to reduce memory footprint while preserving much of the original model's capability. It trades some numerical precision for faster inference and lower VRAM requirements, making it practical for deployment on constrained GPU setups. The safetensors format signals a focus on safe, efficient loading in production environments.

Capabilities

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

Coding

Strong

Reasoning & Logic

Strong

Long Context

Use Case Fit

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

Kimi K3 NVFP4

by NVIDIA

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

Released July 2026context N/A

Kimi K3 NVFP4 is a quantized text-to-text model optimized for NVIDIA hardware, using FP4 precision to reduce memory footprint while preserving much of the original model's capability. It trades some numerical precision for faster inference and lower VRAM requirements, making it practical for deployment on constrained GPU setups. The safetensors format signals a focus on safe, efficient loading in production environments.

Capabilities

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

Coding

Strong

Reasoning & Logic

Strong

Long Context

Use Case Fit

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

Glossary

FP4 PrecisionA ultra-low precision format using 4-bit floating-point numbers to represent model weights, enabling extreme compression.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.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.SafetensorsA safe, fast file format for storing model weights, designed to prevent code execution vulnerabilities.Safetensors FormatA secure and efficient file format for storing model weights that prioritizes safety and speed when loading models.Text ModelA language model that processes and generates only text, without support for images, audio, or other media types.Text-to-TextA framework where all NLP tasks are treated as converting input text into output text, so translation, summarization, and classification use the same model structure.Text-to-Text ModelA machine learning model that takes text as input and produces text as output, useful for tasks like translation, summarization, or question answering.VRAMVideo RAM — the memory on a GPU that stores model weights and intermediate computations during inference.