Ollama

🗂 Hash: da14f734d522b714d526318d68b19541 • Last Updated: 2026-07-19VerifyCPU: multi-threading optimized for fast prompt processing RAM: 48 GB needed to prevent memory swapping to disk Disk Space: at least 100 GB for multiple local LLM variants Graphics: TensorRT-LLM / vLLM inference engine compatible chip The Qwen3-Omni-30B-A3B-Instruct: Unlocking the Power of Large Language ModelsThe Qwen3-Omni-30B-A3B-Instruct is a state-of-the-art [...]

📎 HASH: f8783938c573fadc125d0b2df3e7f69f | Updated: 2026-07-17VerifyCPU: 8-core / 16-thread recommended for orchestration RAM: 48 GB needed to prevent memory swapping to disk Disk Space: required: fast PCIe 4.0 drive for instant boots Graphics: 12 GB VRAM minimum required for basic quantization The Next Generation of Language ModelsThe Qwen3.5-35B-A3B is a revolutionary language model that redefines [...]

🧾 Hash-sum — 983f24c26adefeef49355200a91814bd • 🗓 Updated on: 2026-07-15VerifyProcessor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: high-speed DDR5 memory preferred for CPU offloading Storage: extra room for future model updates and datasets GPU: modern architecture (Ada Lovelace / Ampere minimum) Unlocking Unprecedented Efficiency in Large Language ModelsThe Qwen3.6-27B-FP8 model represents a [...]

🔧 Digest: c16583aa80d5ea661d3b24fb8995645d • 🕒 Updated: 2026-07-16VerifyProcessor: next-gen chip for heavy context processing RAM: high-speed DDR5 memory preferred for CPU offloading Storage: extra room for future model updates and datasets GPU: high memory bandwidth GPU for next-gen local AI pipeline The Qwen3.5-35B-A3B-GPTQ-Int4 Model: A Cutting-Edge Language CompanionThe Qwen3.5-35B-A3B-GPTQ-Int4 model is an advanced language companion, leveraging [...]

🔍 Hash-sum: ecabad91011559af60d84389b1134cfd | 🕓 Last update: 2026-07-19VerifyCPU: 8-core / 16-thread recommended for orchestration RAM: minimum 16 GB for stable 8B model loading 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 Depths of DeepSeek-V4-ProDeepSeek-V4-Pro, a revolutionary breakthrough [...]

📊 File Hash: a73c0cf8b86b58e21a0aaf33d065c33a — Last update: 2026-07-14VerifyProcessor: next-gen chip for heavy context processing RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Digital Visionary: Empowering Real-Time Multimodal UnderstandingThe MiniCPM-V-4.6 represents a groundbreaking achievement [...]

📘 Build Hash: 99cf911821024dbd107cb1a81c9d91ea • 🗓 2026-07-19VerifyCPU: 8-core / 16-thread recommended for orchestration RAM: required: 16 GB absolute minimum for small models Storage:100 GB free space for HuggingFace cache folder GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Performance Overview for WanVideo_comfy_fp8_scaled ModelThe WanVideo_comfy_fp8_scaled model is designed to deliver high-fidelity video generation [...]

💾 File hash: 5293fe4c90135244029059b0a372da5f (Update date: 2026-07-17)VerifyProcessor: high single-core performance needed for token latency RAM: 64 GB to avoid OOM crashes on large contexts Storage:100 GB free space for HuggingFace cache folder GPU: modern architecture (Ada Lovelace / Ampere minimum) Unlocking Real-Time Multimodal Understanding with MiniCPM-V-4.6The MiniCPM-V-4.6 is a cutting-edge vision-language model designed to bridge [...]

The shortest path to running this model is by activating Hyper-V features. Please follow the instructions listed below to get started. The client handles the setup, pulling gigabytes of data automatically. The initial setup handles the heavy lifting, fine-tuning the environment for your device. 📘 Build Hash: f10aef7ec25dab6eec15491facde67d1 • 🗓 2026-07-14VerifyCPU: 8-core / 16-thread recommended [...]