How to Run Anima Step-by-Step

How to Run Anima Step-by-Step

Using Docker is the absolute quickest way to install this model on your local machine.

Follow the sequence of steps detailed below.

The setup auto-downloads all needed files (several GBs).

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

📘 Build Hash: 2e39cf1c651983a65ccce88f23cf91bb • 🗓 2026-06-23



  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • RAM: fast 5600MHz+ required to avoid memory bottlenecks
  • Storage:100 GB free space for HuggingFace cache folder
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

Anima is a next‑generation AI model designed to deliver ultra‑low latency inference across a wide range of applications. Built on a scalable neural architecture, it combines deep contextual understanding with real‑time processing capabilities. The model excels in multimodal tasks, seamlessly handling text, images, and audio with a unified representation space. Its training pipeline leverages massive curated datasets and advanced optimization techniques to achieve state‑of‑the‑art performance while maintaining energy efficiency. Anima’s modular design enables developers to fine‑tune and deploy the system on diverse hardware platforms, from edge devices to cloud infrastructures.

Technical specifications
Parameter Value
Model size 12 B parameters
Training data 1.5 trillion tokens
Inference latency <5 ms
Supported modalities Text, Image, Audio
  • Downloader pulling high-quality voice profiles for local Fish-Speech setups
  • How to Launch Anima on Your PC For Beginners Windows
  • Script fetching optimized Qwen model variants for terminal-based chat
  • Full Deployment Anima Offline on PC
  • Script fetching specialized agent orchestration base weights
  • How to Setup Anima PC with NPU For Beginners
  • Installer deploying local face-swapping model scripts and core assets
  • Zero-Click Run Anima on Your PC No Python Required No-Code Guide FREE

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