Ollama
🧩 Hash sum → aed4ed8d3e289b36d4d2b5253389f5b5 — Update date: 2026-07-21VerifyCPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: minimum 16 GB for stable 8B model loading Disk Space: required: fast PCIe 4.0…
📡 Hash Check: 07dede08196504e1eeb2aa47495df013 | 📅 Last Update: 2026-07-20VerifyCPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space:70 GB free…
📦 Hash-sum → 6d5359c94fa9eb5d7c960108ca74402d | 📌 Updated on 2026-07-17VerifyProcessor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 48 GB needed to prevent memory swapping to disk Storage: extra room…
🧮 Hash-code: 26b302eda26db1afac50f7874c9d16df • 📆 2026-07-13VerifyCPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space:70 GB free space for full FP16 weights storage…
📎 HASH: 0df4fb187fa705a0184aef322064326d | Updated: 2026-07-11VerifyCPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 64 GB to avoid OOM crashes on large contexts Disk: 150+ GB for high-context vector…
🔗 SHA sum: 67bac15cd0bd80455d0a66bbd4d9a179 | Updated: 2026-07-16VerifyProcessor: high single-core performance needed for token latency RAM: high-speed DDR5 memory preferred for CPU offloading Disk: 150+ GB for high-context vector database storage…
If you need a near-instant local setup, just fetch files via a basic curl request. Please adhere to the deployment steps listed below. The client handles the setup, pulling gigabytes…
The fastest way to get this model running locally is via Optional Features. Follow the straightforward walkthrough provided below. Be patient as the system self-retrieves massive model weights dynamically. The…
Homebrew offers the quickest path to setting up this model locally. Please adhere to the deployment steps listed below. The engine will automatically fetch large dependencies in the background. The…