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

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

Running this model locally is fastest when deployed through Docker.

Use the instructions provided below to complete the setup.

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

During setup, the script automatically determines and applies the best settings tailored to your machine.

🔍 Hash-sum: c8b09e610ba8a5a74b354edda255ce6b | 🕓 Last update: 2026-06-27



  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Disk: high-speed SSD 120 GB to cache model layers
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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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