Kimi K2.6 NVFP4

Name: Kimi K2.6 NVFP4
Author: NVIDIA

by NVIDIA

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

Released May 2026context N/A

Kimi K2.6 NVFP4 is a quantized text-in, text-out model optimized by NVIDIA using FP4 precision, which reduces memory footprint and can accelerate inference on compatible hardware. The trade-off is that FP4 quantization may introduce minor quality degradation compared to full-precision variants. It carries open weights distributed in safetensors format, making it accessible for local deployment.

Capabilities

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

Tool Use

Strong

Coding

Strong

Long Context

Use Case Fit

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

Kimi K2.6 NVFP4

by NVIDIA

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

Released May 2026context N/A

Kimi K2.6 NVFP4 is a quantized text-in, text-out model optimized by NVIDIA using FP4 precision, which reduces memory footprint and can accelerate inference on compatible hardware. The trade-off is that FP4 quantization may introduce minor quality degradation compared to full-precision variants. It carries open weights distributed in safetensors format, making it accessible for local deployment.

Capabilities

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

Tool Use

Strong

Coding

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.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.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Local DeploymentRunning a model directly on your own computer or server instead of sending requests to a remote service.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.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.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-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.