Edge work comes down to a balance between power draw, compute, and heat. This collection covers edge inference chips, development boards, and complete units, from NVIDIA to domestic vendors. Work out your model memory footprint and latency budget on paper before choosing a board; it saves a lot of trial and error.
Tool list
4 toolsNVIDIA Jetson Hot New
Edge computing platform for physical AI and robotics sharing the Blackwell GPU architecture and JetPack software stack; the T5000 module delivers 2,070 FP4 sparse TFLOPS with 128GB of memory
Hailo-10H Hot New
Among the first edge accelerators with generative AI support, rated at 40 TOPS INT4 with 2.5W typical power; the Raspberry Pi AI HAT+ 2 is built on this chip
Rockchip RK3588 Hot New
Flagship embedded SoC from Rockchip, an 8nm octa-core part with a 6 TOPS in-house NPU, 8K encode and decode, and four independent display outputs
Sophgo BM1688 New
High-throughput edge TPU from Sophgo, rated at 32 TOPS INT8 and up to 64 TOPS INT4, able to run 7B-class models locally within an 18W envelope
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