How to Deploy z_image_turbo PC with NPU

How to Deploy z_image_turbo PC with NPU

Running this model locally is fastest when deployed through Docker.

Follow the step-by-step instructions below.

The deployment tool scans your environment and automatically chooses the ideal parameters for your OS.

🗂 Hash: a4a671bab75d351f15a1276e4b97f379 • Last Updated: 2026-06-23
  • Processor: next-gen chip for heavy context processing
  • RAM: enough space for background apps and OS overhead
  • Disk Space:70 GB free space for full FP16 weights storage
  • Graphics: CUDA Compute Capability 8.0+ required for flash-attention

The z_image_turbo model leverages a deep residual architecture to deliver real‑time image generation with unprecedented speed. It supports up to 4K resolution while maintaining high fidelity through advanced denoising techniques. The model’s parameter count of 1.5 B enables deployment on consumer GPUs without sacrificing quality. A dedicated tensor core optimization reduces inference latency to under 50 ms per image. The integrated adaptive scaling ensures consistent performance across diverse input styles and resolutions.

Parameter Count 1.5 B
Inference Latency <50 ms
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