Kimi-K2.6-DFlash

Model Overview Description: The NVIDIA Kimi-K2.6 DFlash model is the DFlash draft head of Moonshot AI's Kimi-K2.6 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Kimi-K2.6 DFlash model incorporates DFlash speculative decoding with Model Optimizer. This model is ready for commercial/non-commercial use. License/Terms of Use: Governing Terms: Use of this model is governed by the NVIDIA Open Model License. ADDITIONAL INFORMATION** : Modified MIT License. **Kimi-K2.6** . Deployment Geography: Global Use Case: Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks where latency-optimized inference via speculative decoding is desirable. Release Date: Hugging Face 06/30/2026 via https://huggingface.co/nvidia/Kimi-K2.6-DFlash Reference(s): Kimi-K2.6 release notes Model Architecture: Architecture Type:** Transformers Network Architecture:** DeepSeek V3 Number of Model Parameters:** 1T in total and 32B activated Input: Input Type(s):** Text, Image, Video Input Format(s):** String, Binary(Base64 encoded), Binary(Base64 encoded) Input Parameters:** One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D) Other Properties Related to Input:** Context length: 256k Output: Output Type(s):** Text Output Format:** String Output Parameters:** One Dimensional(1D): Sequences Other Properties Related to Output:** Outputs may include natural-language responses, code, structured JSON, tool-call requests, agent coordination instructions, and generated artifacts depending on serving configuration and application-level tooling. Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only soluti