Goodtico G1
Goodtico

G1

CPU i9 10885H
GPU Mobile 1650 Ti
Max RAM 64GB
Drives 2
Seller Amazon

The Goodtico G1 is a mini PC powered by the Intel i9 10885H (10th gen). It features 8 performance cores and 16 threads and a soldered CPU. Graphics are handled by the discrete NVIDIA Mobile 1650 Ti. Supports up to 64GB of RAM across 2 slots at 2666 MT/s. Storage capacity of up to 2 drives including 1 NVMe, 1× 2.5" SATA. Networking features 1 Ethernet port (up to 1G). Dimensions: 222 × 195 × 38mm (1.65L).

CPU & Performance

CPUi9 10885H
Generation10th
P-Cores8
Threads16
Single-core54
Multi-core22
Socket0

GPU

GPUMobile 1650 Ti
Discrete GPUYes
Integrated GPUYes
eGPU supportNo

RAM

Max RAM64GB
RAM slots2
Max speed2666 MT/s
ECC supportNo

Storage

Max drives2
NVMe slots1
Gen3 x41
2.5" SATA1
SD cardNo

Ports & Connectivity

Max displays2

Network

Max Ethernet1G
1G ports1
vProN

Physical

Dimensions222 × 195 × 38 mm
Volume1.65L
Fan size80mm

Power

Additional

Recommended for gaming, GPU-accelerated creative work, 3D or graphics-heavy tasks, and larger multitasking workloads. Consider Goodtico G1 if you want a small-form-factor mini PC built around its listed Intel i9 10885H processor and NVIDIA Mobile 1650 Ti discrete GPU plus integrated graphics. It is a stronger fit when the listed CPU, GPU, connectivity, and expansion features match your workload requirements.

Goodtico G1 features Intel i9 10885H with NVIDIA Mobile 1650 Ti discrete GPU plus integrated graphics, offering 1× 1G Ethernet. CPU details include 10th CPU generation, 8 performance cores, and 16 threads. Memory support includes up to 64GB RAM across 2 slots at 2666 MT/s. Storage support includes up to 2 storage drives including 1 NVMe SSD slot, 1× PCIe Gen3 x4 NVMe, and 1× 2.5-inch SATA bay. Display support includes support for up to 2 displays. Networking includes 1× 1G Ethernet. Physical design includes a 222 × 195 × 38 mm chassis (1.65L). Cooling information lists 80 mm main fan and 80 mm secondary fan.

The overall score is not populated for Goodtico G1, so this model should be evaluated by the listed hardware specifications rather than by a ranked dataset position.

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