This page tracks all hardware currently planned for the TorresVault 2.0 NAS/GPU Build. Parts are grouped by category, with checkboxes for tracking purchases.

• [ ] CPU: AMD Ryzen 7 5700X
  • 8 cores / 16 threads
  • Excellent efficiency for a 24/7 NAS + GPU box
  • Item added: December 11, 2025

• [ ] CPU Cooler: Thermalright Peerless Assassin 120 SE
  • Dual 120mm fans, high-performance tower cooler
  • Model: PA120 SE
  • Item added: December 11, 2025

• [ ] Motherboard: MSI B550-A PRO V1
  • AM4 socket
  • PCIe 4.0
  • M.2 NVMe support
  • Item added: December 11, 2025

• [ ] GIGABYTE RTX 5060 Ti WINDFORCE MAX OC — 16GB GDDR7
  • PCIe 5.0
  • Ideal for LLM inference / Jarvis workloads
  • Model: GV-N506TWF2MAX OC-16GD
  • Item added: December 11, 2025

• [ ] TeamGroup MP44 1TB NVMe (PCIe 4.0)
  • 7000/6000MB/s
  • For OS + AI model storage
  • Item added: December 11, 2025

*(You can add your 6TB SAS enterprise disks separately under your NAS drive inventory page.)*

• [ ] CORSAIR RM650e (2025 model) — 650W Fully Modular
  • 80+ Gold
  • Modern standby support
  • Item added: December 11, 2025

• [ ] Phanteks XT View (Black)
  • Mid-tower
  • Tempered glass
  • 3× M25-120 DRGB fans
  • Item added: December 11, 2025

The list contains multiple RAM kits because stock varies. Choose one of the 32GB kits below depending on availability and price.

• [ ] Silicon Power 32GB (2×16GB) DDR4-3200 CL16
  • Item added: December 11, 2025
  • Good performance/price

• [ ] Crucial Ballistix 16GB (2×8GB) DDR4-3600 CL16
  • Item added: December 11, 2025
  • Use only if building a lower-RAM starter config

• [ ] Corsair Vengeance LPX 16GB (2×8GB) DDR4-3200 CL16
  • Item added: December 11, 2025
  • Backup option when other kits are OOS

• This list represents the planned build for TorresVault 2.0 NAS + GPU compute node.
• All parts were added to Amazon on December 11, 2025.
• RAM choices remain flexible depending on stock and pricing.
• Goal: Maximum performance, cooling, and stability for:
  • Proxmox workloads
  • Local LLM inference
  • Jellyfin transcoding
  • Heavy automation and CI workloads