Gigabyte Announced GB-BNiP-N200
Gigabyte

Announced GB-BNiP-N200

CPU Celeron N200
GPU Iris Xe 32EU
Max RAM 16GB
Drives 2

The Gigabyte Announced GB-BNiP-N200 is a mini PC powered by the Intel Celeron N200 (12th gen). It features a soldered CPU. Graphics are handled by the integrated Intel Iris Xe 32EU. Supports up to 16GB of RAM across 1 slot at 3200 MT/s. Storage capacity of up to 2 drives. Networking features 1 Ethernet port (up to 2.5G). Dimensions: 164 × 117 × 28mm (0.54L). Uses a Brick PSU (65W), 6W TDP.

CPU & Performance

CPUCeleron N200
Generation12th
Threads4
Single-core40
Multi-core9
Socket0

GPU

GPUIris Xe 32EU
Discrete GPUNo
Integrated GPUYes
eGPU supportNo

RAM

Max RAM16GB
RAM slots1
Max speed3200 MT/s
ECC supportNo

Storage

Max drives2
SD cardNo

Ports & Connectivity

Max displays3

Network

Max Ethernet1G
2.5G ports1
vProN

Physical

Dimensions164 × 117 × 28 mm
Volume0.54L
Warranty1 yr

Power

TDP6W
PSU typeBrick
PSU65W

Additional

Recommended for office work, web browsing, media playback, light home or business use, and very small desk setups. Consider Gigabyte Announced GB-BNiP-N200 if you want a ultra-compact mini PC built around its listed Intel Celeron N200 processor and Intel Iris Xe 32EU integrated GPU. It is less suitable for heavy gaming or GPU-accelerated workloads because no discrete or high-speed external GPU path is listed.

Gigabyte Announced GB-BNiP-N200 features Intel Celeron N200 with Intel Iris Xe 32EU integrated GPU, offering 1× 2.5G Ethernet. CPU details include 12th CPU generation and 4 threads. Memory support includes up to 16GB RAM across 1 slot at 3200 MT/s. Storage support includes up to 2 storage drives and 2× M.2 SATA slots. Display support includes support for up to 3 displays. Networking includes 1× 2.5G Ethernet. Physical design includes a 164 × 117 × 28 mm chassis (0.54L). Power information lists Brick power supply rated at 65W and 6W TDP. Lifecycle information lists 1-year warranty.

The overall score is not populated for Gigabyte Announced GB-BNiP-N200, so this model should be evaluated by the listed hardware specifications rather than by a ranked dataset position.

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