QNAP

QNAP TS-2888X AI-Ready Intel Xeon NAS

AI-ready 28-bay NAS with Intel Xeon W-2195, 512 GB RAM, and multi-GPU support for demanding machine learning workflows.

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Overview

The QNAP TS-2888X is a network attached storage appliance built specifically for organizations running AI and machine learning workloads. At its core sits an Intel Xeon W-2195 with 18 cores running at 2.30 GHz, paired with 512 GB of DDR4 RAM — enough to handle large training datasets and multiple concurrent model runs. The 28 drive bays split across 8 HDD slots and 20 SSD slots let teams build tiered storage architectures, keeping frequently accessed training data on fast NVMe or SATA SSDs while archiving completed projects on high-capacity hard drives.

What distinguishes the TS-2888X from conventional NAS units is its multi-GPU support. By installing compatible graphics cards directly into the chassis, teams can run deep learning training and inference locally rather than offloading to a separate workstation or cloud instance. QNAP complements the hardware with a pre-configured AI software environment that reduces the overhead of setting up frameworks and dependencies. For studios, research labs, or enterprises looking to centralize AI development and large-scale data storage in a single appliance, the TS-2888X offers a compelling — if investment-heavy — solution.

Specifications

Processor
Intel Xeon W-2195 Octadeca-core (18 Core) 2.30 GHz
Memory
512 GB DDR4 SDRAM
Total Drive Bays
28 (8 x HDD, 20 x SSD)
Storage Controller
Serial ATA/600, NVMe
Brand
QNAP
Model
TS-2888X

Frequently Asked Questions

It has 28 total drive bays — 8 bays for 3.5" HDDs and 20 bays for 2.5" SSDs, allowing a mix of high-capacity bulk storage and fast SSD tiers.
Yes, it supports multiple GPU installations, enabling accelerated deep learning training and inference workloads directly on the NAS.
It comes equipped with an Intel Xeon W-2195 18-core processor running at 2.30 GHz and 512 GB of DDR4 RAM.
Yes, QNAP includes a ready-to-use AI software environment designed to reduce the setup complexity for machine learning model development and deployment.
It supports both Serial ATA/600 and NVMe interfaces, so you can use traditional SATA drives alongside high-speed NVMe SSDs.