Gigabyte W775-V10-L01 AI Workstation
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Full Specifications
Compute & Memory
Superchip
NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip
CPU
1× NVIDIA Grace CPU Superchip, 72 Arm Neoverse V2 cores
GPU
1× NVIDIA Blackwell Ultra GPU
Coherent Memory
748 GB unified: 496 GB LPDDR5X + 252 GB HBM3e
AI Performance
20 PetaFLOPS (FP4)
Storage & Physical
Storage (Slots)
2× M.2 2280 PCIe 6.0 x4 NVMe + 2× M.2 2280 PCIe 5.0 x4 NVMe, protective slot covers
Form Factor
Deskside tower, switchable server / workstation operation
Dimensions
21.8 x 51.95 x 72.63 cm
Networking & Expansion
High-Speed Networking
2× 400G QSFP112 ports via NVIDIA ConnectX-8 SuperNIC
PCIe Expansion
PCIe 5.0, additional x16 and x8 slots, supports optional NVIDIA RTX PRO Blackwell GPUs
Power & Thermal
Power Supply
1600 W ATX power supply, Titanium efficiency
Cooling
Closed-loop liquid cooling with integrated leak-detection tray and automatic shutdown
Management & Software
BMC
ASPEED AST2600
Remote Management
IPMI 2.0 and Redfish API, data-center-style monitoring and control
Operating Modes
Switchable between BMC-managed server operation and direct-access workstation operation
Gigabyte introduced the W775-V10-L01 on January 12, 2026, positioning it within the NVIDIA GB300 Grace Blackwell Superchip ecosystem as a deskside AI workstation built to run trillion-parameter models locally, with particular engineering attention to continuous, unattended operation.
Hardware Specifications
The W775-V10-L01 is built around a single NVIDIA Grace CPU Superchip — 72 Arm Neoverse V2 cores and 496 GB LPDDR5X memory — paired with an NVIDIA Blackwell Ultra GPU with 252 GB HBM3e GPU memory, unified through NVLink-C2C into a coherent memory pool of 748 GB. Storage is handled by four M.2 2280 slots — two PCIe 6.0 x4 and two PCIe 5.0 x4 — each fitted with a protective cover, alongside a dual-port NVIDIA ConnectX-8 400G SuperNIC for high-speed networking.
Local AI Server for Business Use
The W775-V10-L01 is designed to function as a shared local AI resource. Multiple employees can submit document processing tasks — contract review, invoice extraction, report drafting, email triage — throughout the working day, with jobs handled centrally on-site instead of routed through external cloud services.
Through NVIDIA NemoClaw, autonomous agents can run continuously on the system, handling recurring tasks such as inbox monitoring or scheduled report generation without requiring a person to initiate each request. This allows the W775-V10-L01 to operate as ongoing AI infrastructure for the business, supporting steady document workloads.
Engineered for Unattended, Continuous Operation
Gigabyte pairs the closed-loop liquid cooling of the W775-V10-L01 with an integrated leak-detection tray positioned beneath the coolant connectors. If moisture is detected, the system firmware initiates an automatic, orderly shutdown before a leak can reach surrounding equipment.
The chassis can also be set up for two distinct roles. Managed through its onboard BMC over IPMI 2.0 and Redfish, it operates as a centrally administered, remotely monitored server. Its PCIe slots accept NVIDIA RTX PRO Blackwell GPUs for graphics-intensive workstation use.
Cost Comparison: On-Premises vs. Cloud AI APIs
For SMBs processing high volumes of documents on a daily basis, the difference between a on-premises system and cloud AI billing compounds significantly over time.
| Cost Category | Cloud AI APIs | W775-V10-L01 (On-Premises) |
|---|---|---|
| Initial hardware investment | None | €100K (one-time) |
| Year 1 operating cost | ~€100k – ~€200k | ~€4k (power & maintenance) |
| Year 2 operating cost | ~€100k – ~€200k | ~€4k (power & maintenance) |
| Year 3 operating cost | ~€100k – ~€200k | ~€4k (power & maintenance) |
| Cumulative 3-year cost | ~€300k – ~€600k | ~€115K |
Because processing takes place entirely on-site, documents and business data are not transmitted to third-party servers. This is a relevant consideration for organizations in legal, financial, or other data-sensitive sectors.
Summary
The W775-V10-L01 provides a single, fixed-cost system capable of supporting continuous AI document processing, combined with the architecture to run large AI models locally and engineering safeguards for unattended operation. For SMBs with sustained AI workloads, it offers a lower cost alternative to recurring cloud AI expenditure.
