Embeddings
🧩 Hash sum → 2030c3c589f8367b12a909186e29b7ad — Update date: 2026-07-17 Verify Processor: next-gen chip for heavy context processing RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: 100 GB for multi-modal model vision components Graphics: stable 30+ tk/s at…
🔒 Hash checksum: 5a1c6b0ce081e6b08624b0290a960c6a • 📆 Last updated: 2026-07-15 Verify Processor: next-gen chip for heavy context processing RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space:70 GB free space for full FP16 weights storage GPU: RTX 4080 /…
🧾 Hash-sum — 0f5f3d9081e09de29f2451265649289c • 🗓 Updated on: 2026-07-20 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space:70 GB free space for full FP16 weights storage GPU: RTX 4080 / RTX…
🔗 SHA sum: 3f8ba7b5bbb222aed7376d2affb19f7f | Updated: 2026-07-16 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB highly recommended for 26B+ GGUF models Disk: 150+ GB for high-context vector database storage Graphic Processor: RTX 3060 or RX 6600 for…
📦 Hash-sum → c698488db6ed96c57822795a3b142ccf | 📌 Updated on 2026-07-19 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 64 GB to avoid OOM crashes on large contexts Disk: high-speed SSD 120 GB to cache model layers GPU: modern…
🔧 Digest: 9e5bce51ecf1e7eb3d8589c32a27eed6 • 🕒 Updated: 2026-07-12 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: required: 16 GB absolute minimum for small models Disk: high-speed SSD 120 GB to cache model layers Graphics: CUDA Compute Capability 8.0+…