MSI XpertStation WS300 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 5.0 x4 NVMe + 2× M.2 2280 PCIe 6.0 x4 NVMe (64GB/s)
Form Factor
Deskside tower, NVIDIA DGX Station architecture
Dimensions
24.78 x 58.27 x 52.79 cm
Networking & Expansion
High-Speed Networking
2× 400G QSFP112 ports via NVIDIA ConnectX-8 SuperNIC
PCIe Expansion
PCIe 5.0, 1× double-width x16 slot + additional x8 slots
Power & Thermal
Power Supply
1600 W ATX power supply, Titanium efficiency
Cooling
Closed-loop liquid cooling, sustained performance under heavy AI workloads
Management & Software
BMC
ASPEED AST2600
Remote Management
IPMI 2.0 and Redfish API, data-center-style monitoring and control
MSI introduced the XpertStation WS300 on March 16, 2026, positioning it within the NVIDIA GB300 Grace Blackwell Superchip ecosystem as a system that supports running trillion-parameter models locally.
Hardware Specifications
The WS300 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. The system provides 2× M.2 2280 PCIe 6.0 x4 NVMe storage (64GB/s) and a dual-port ConnectX-8 400G network interface.
Local AI Server for Business Use
The WS300 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 WS300 to operate as ongoing AI infrastructure for the business, supporting steady document workloads.
Cost Comparison: On-Premises vs. Cloud AI APIs
For SMBs processing high volumes of documents on a daily basis, the difference between a fixed on-premises system and usage-based cloud AI billing compounds significantly over time.
| Cost Category | Cloud AI APIs | WS300 (On-Premises) |
|---|---|---|
| Initial hardware investment | None | $85K (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 | 9553214 INR |
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 XpertStation WS300 provides a single, fixed-cost system capable of supporting continuous AI document processing, combined with the architecture to run large AI models locally. For SMBs with sustained AI workloads, it offers a lower cost alternative to recurring cloud AI expenditure.
