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GPU & AI Chassis

IWILL G4830-SE12-HKW – GPU & AI Chassis

IWILL G4830-SE12-HKW gpu & ai chassis platform.

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Tell us the quantity and what it is for. Technical sales confirms the configuration, availability, lead time, and pricing.

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  • Configuration validated before quoting
  • Written quote with lead time & warranty terms
  • Single evaluation units available
  • Support up to 10 GPU cards (G4830-SE12)
  • Front 8 pcs hot-swap 3.5" drive bays
  • Support for motherboard up to 12"x13"
  • Standard CRPS power supply under 2000W, 4+1 mode
  • 7 full-height PCI/PCIe vertical expansion slots

Platform overview

Built into your system, not just placed beside it.

The IWILL G4830-SE12-HKW is a multi-GPU server chassis from the IWILL GPU & AI Chassis range. It runs from support standard CRPS power module, 4+1 power supply mode. Typical deployments include big data, cloud computing, AI education. Configuration, lead time, and US availability are confirmed per project; technical sales validates the exact build before issuing a written quotation.

Technical specifications

Dimensions
L830mm*W637.6mm*H176mm
Material
Magang (environmentally friendly, fingerprint resistant 1.2mm SGCC)
Surface treatment
Front panel components are sprayed with sand grain black, other colors
GPU support
Max supports 10 double-width turbine GPUs (length 270mm in length)
Drive bays
12 x 3.5"/2.5" drive bays, plus two front 2.5-inch SATA and two front 2.5-inch NVMe slots
Cooling
8 built-in N096 hot swap fans and 4 rear 6038 ball fans (high air pressure/high air volume)
Motherboard support
Standard motherboards up to 12" x 13" with at least two PCIe 5.0 x16 ports
Expansion slots
7 front PCI/PCIE expansion slots, vertical, and two rear full-height PCI/PCIE expansion slots
Power input
Support standard CRPS power module, 4+1 power supply mode
PCIe support
Two Broadcom PEX9104 expansion chips support 10 PCIE5.0x16 channels and 2 PCIE5.0x8 channels
Front I/O
Front VGA, Optional (mainboard I/O front)

Typical applications

Where it deploys

  • Big data
  • Cloud computing
  • AI education
  • Deep machine education
  • Large models

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