Ollama

🧩 Hash sum → f8f6214efbd17f2e2640a4a451ca5a96 — Update date: 2026-07-20 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: enough space for background apps and OS overhead Disk Space: free: 80 GB on system drive for scratch space Graphics: CUDA Compute Capability 8.0+ required for flash-attention Unlocking the Power of Qwen3.6-27B-MLX-4bit Our team has […]

📘 Build Hash: a7ad24a7664b5e774464185eaf2e7471 • 🗓 2026-07-16 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: enough space for background apps and OS overhead Disk: 150+ GB for high-context vector database storage Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Unlocking the Power of High-Fidelity Image Generation The diffusiongemma-26B-A4B-it-NVFP4

📘 Build Hash: eb66f0651882863dd676925503095d56 • 🗓 2026-07-15 Verify Processor: high single-core performance needed for token latency RAM: 32 GB or higher for smooth 32k context lengths Disk Space: free: 80 GB on system drive for scratch space GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Unlocking the Potential of Gemma-4-31B-it: A Revolutionary

📤 Release Hash: 50308490262de834d723125f4b8633b1 • 📅 Date: 2026-07-13 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: required: 16 GB absolute minimum for small models Disk Space: 100 GB for multi-modal model vision components Graphics: CUDA Compute Capability 8.0+ required for flash-attention Dynamics of LFM2.5-VL-450M The LFM2.5-VL-450M model is a groundbreaking achievement in multimodal language

🔗 SHA sum: a8ea68f024d02ff6db66aafa1a965c27 | Updated: 2026-07-12 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 32 GB or higher for smooth 32k context lengths Disk: high-speed SSD 120 GB to cache model layers Graphics: CUDA Compute Capability 8.0+ required for flash-attention Advancements in Large Language Models The latest advancements in large language

📄 Hash Value: c13401e5325b1492683b0e11ba59f5d3 | 📆 Update: 2026-07-14 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: minimum 16 GB for stable 8B model loading Disk: 150+ GB for high-context vector database storage Graphics: CUDA Compute Capability 8.0+ required for flash-attention The Breakthrough in Efficient Inference for Large Language Tasks The Kimi-K2.5-NVFP4 model marks a

🧩 Hash sum → 8c0c608ed1f9bd1adc27603bca8ae8c6 — Update date: 2026-07-14 Verify Processor: 6-core 3.5 GHz minimum required RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: 100 GB for multi-modal model vision components Graphics: 12 GB VRAM minimum required for basic quantization Unlocking the Power of Optical Character Recognition with chandra-ocr-2 The **chandra-ocr-2** model

For the fastest local setup of this model, enabling Windows Features is best. Execute the commands and steps outlined below. All large files and heavy weights are downloaded automatically by the script. Once launched, the wizard detects your specs to configure the model for maximum efficiency. 🗂 Hash: 8fa4771cf15a28ca87744757a0afc468 • Last Updated: 2026-07-14 Verify Processor: