TRELLIS.2-4B Offline on PC with Native FP4
29 czerwca 2026

TRELLIS.2-4B Offline on PC with Native FP4

Running this model locally is fastest when deployed through Docker.

Make sure to follow the instructions below.

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

🧾 Hash-sum — 5ab0784efa7d79c2a2f024962c9fc3c4 • 🗓 Updated on: 2026-06-24
  • CPU: multi-threading optimized for fast prompt processing
  • RAM: fast 5600MHz+ required to avoid memory bottlenecks
  • Storage: extra room for future model updates and datasets
  • GPU: high memory bandwidth GPU for next-gen local AI pipeline

The TRELLIS.2-4B model represents a significant advancement in open‑source language models, delivering state‑of‑the‑art performance while maintaining a manageable parameter count of 2.4 billion. Built on a transformer‑based architecture with enhanced attention mechanisms, it achieves superior comprehension of both textual and multimodal inputs. Trained on a diverse corpus spanning code, scientific literature, and conversational data, the model exhibits robust generalization across a wide range of downstream tasks. Its efficient design enables deployment on standard GPU clusters, making advanced AI capabilities accessible to developers and researchers worldwide. A dedicated

with key technical specifications is provided below for quick reference.

Specification Value
Parameter Count 2.4 B
Context Length 8 K tokens
Training Data Types Code, scientific, conversational
Primary Use Cases Text generation, summarization, Q&A, multimodal tasks
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