por velcomex | Jul 24, 2026 | GPTQ
📎 HASH: c82799b44a49a61e3a8a77f9fd20fdc1 | Updated: 2026-07-19VerifyProcessor: high single-core performance needed for token latency RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: 16 GB+...
por velcomex | Jul 24, 2026 | GPTQ
📎 HASH: c82799b44a49a61e3a8a77f9fd20fdc1 | Updated: 2026-07-19VerifyProcessor: high single-core performance needed for token latency RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: 16 GB+...
por velcomex | Jul 23, 2026 | GPTQ
🛡️ Checksum: c54e277277b1838dcea34c3b438d97c7 — ⏰ Updated on: 2026-07-20VerifyCPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 32 GB highly recommended for 26B+ GGUF models Storage:100 GB free space for HuggingFace cache folder Graphics: 12 GB VRAM minimum...
por velcomex | Jul 22, 2026 | GPTQ
💾 File hash: 84437b74d555ca995699958c34b91a91 (Update date: 2026-07-17)VerifyProcessor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: 100 GB for multi-modal model vision components GPU: RTX 4080 / RTX 4090...
por velcomex | Jul 21, 2026 | GPTQ
🧾 Hash-sum — 8f81bf40c1f6c89e1e571bde3eaad4fa • 🗓 Updated on: 2026-07-19VerifyCPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB or higher for smooth 32k context lengths Disk Space: required: fast PCIe 4.0 drive for instant boots Graphics: CUDA...
por velcomex | Jul 21, 2026 | GPTQ
🔐 Hash sum: eeb9db1353d35985d838ea8e39e3e206 | 📅 Last update: 2026-07-14VerifyCPU: 8-core / 16-thread recommended for orchestration RAM: enough space for background apps and OS overhead Disk: high-speed SSD 120 GB to cache model layers GPU: high memory bandwidth GPU...
