What Changed
Unsloth has introduced unsloth/Qwen3.6-27B-NVFP4, a new NVFP4 quantized checkpoint of the Qwen3.6-27B model. This release emphasizes performance enhancements, particularly a reported 2.5x faster throughput compared to other NVFP4 quantizations. The model is calibrated on a mixture of Unsloth's proprietary dataset and the UltraChat dataset. It is designed to operate on GPUs with 24GB of VRAM.
Beyond performance, Qwen3.6-27B-NVFP4 incorporates substantial upgrades in its core capabilities. Key among these are improved Agentic Coding, enabling the model to handle frontend workflows and repository-level reasoning with greater fluency. Additionally, a new feature called Thinking Preservation allows the model to retain reasoning context from historical messages, aiming to streamline iterative development and reduce overhead. This release follows the Qwen3.5 series and represents the first open-weight variant of Qwen3.6, built on community feedback to prioritize stability and practical utility.
Technical Details
The unsloth/Qwen3.6-27B-NVFP4 model is a Causal Language Model with a Vision Encoder. It has 27 billion parameters, a hidden dimension of 5120, and 64 layers. The architecture includes Gated DeltaNet and Gated Attention mechanisms. The Gated DeltaNet features 48 linear attention heads for V and 16 for QK, each with a head dimension of 128. The Gated Attention has 24 attention heads for Q and 4 for KV, with a head dimension of 256. Rotary Position Embedding is used with a dimension of 64. The Feed Forward Network has an intermediate dimension of 17408.
