Price: $149.99(as of Sep 12, 2024 11:32:48 UTC – Details)
-Rock Pi 4C+ 4GB Specs-RK3399T 64bit hexa core processor
Dual Cortex-A72, frequency 1.5GHz (oc to 2GHz) with quad Cortex-A53, 1.4Ghz (os to 1.6GHz) with separate NEON coprocessor
GPU
Memory LPDDR4
SPI Flash (optional)
32Mb SPI flash can be mounted – NVMe boot or e.g. Network boot via SPI supported*
*will reduce available DSI lanes by 2
Wifi/BT Antenna (for Wifi model)
Wifi/BT with external antenna support (uFL)
Storage eMMC module (optional)
Micro SD card
SD card slot supports up to 128GB uSD card
M.2 SSD
M.2 (M) 2230 up to 2T M.2 NVME SSD via PCIe 2.0 x4 interface
Display HDMI
Micro HDMI 2.0 up to 4k@60 (Type D), H.265/VP9 (HEVC) hardware decode
Micro HDMI 2.0 up to 2k@60 (Type D), H.264 hardware decode
USB 3.0
USB 3.0 HOST x1
USB 3.0 OTG port x1 (upper one)
default – DEVICE, activate – HOST via serial command GPIO header
USB 2.0
2x USB HOST ports
Network
Wireless (wifi version only)
Wireless 802.11 b/g/n/ac
Bluetooth 5.0
-In the Box-
ROCK Pi 4C Plus Single board Computer (4GB RAM) x1
18W QC Power Adapter x1
Type-C Cable x1
USB Type A dual Male Cable x1
-Technical Support-
Getting started:”wiki.radxa.com/Rockpi4/getting_started”
Tech-team:monica#smartfire.cn (#——>@) Skype: monica_9122
IO performance-ROCK Pi development board comes with multiple storage options and providing read and write performance on external storage drives. This improves workflow, copying, backups and overall file usage
Maker Friendly, IoT Connectivity-ROCK Pi features maker friendly expansion options, including a 40-pin GPIO interface that allow for interfacing with a range inputs from buttons, switches, sensors, LEDs, and much more
Multi Storage eMMC module-Optional industrial compatible high performance eMMC module, 16G/32G/64G/128G available, TF card slot supports up to 128G; M.2 connector supports up to 2T M.2 NVME SSD
AI Capabilities- ROCK Pi supports mainstream AI stack with GPU acceleration. Further more, a dedicated hardware NPU accelerator coming up next for ROCK Pi will boosts complex Machine Learning algorithm and reduce the power
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