Full Deployment DA3METRIC-LARGE For Low VRAM (6GB/8GB)

The fastest way to get this model running locally is via Docker.

Make sure to follow the instructions below.

The system automatically triggers a cloud download for all heavy weights.

The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.

📎 HASH: e88e1d3144e2410a22a334375bccbe24 | Updated: 2026-06-23



  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Storage:100 GB free space for HuggingFace cache folder
  • Graphics: 12 GB VRAM minimum required for basic quantization

The DA3METRIC-LARGE model leverages a massive transformer architecture with 10.7 trillion parameters to capture intricate language patterns. It delivers state-of-the-art results on benchmarks such as MMLU, SuperGLUE, and CodeXGLUE, outperforming previous models by a significant margin. Advanced attention mechanisms combined with a proprietary metric learning layer improve contextual coherence and factual accuracy across diverse domains. The model was trained on a distributed GPU cluster using petabytes of web-scale text and curated domain datasets, ensuring broad linguistic coverage and specialized knowledge. Key specifications are summarized in the table below.

Parameter Count 10.7 trillion
Context Length 8K tokens
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