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SOM System-On-Modules - SOM Google Edge TPU ML Compute Accelerator, Integrate The Edge TPU into Legacy and New Systems Using a Standard M.2-2280-B-M-S3 (B/M Key)
Google Coral

Google Coral SOM System-On-Modules

Bottom line: The Google Coral SOM System-On-Modules is a specs-and-price decision rather than a crowd-consensus one in the ai accelerators category. Check the specification table below against one or two close alternatives — on price and spec fit, not on star ratings.

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By , Founder & Editor-in-Chief · Listing data updated

Mike Perry — Hardware researcher since 2014, focused on GPU performance across gaming, rendering, and local-LLM inference. Synthesises community benchmarks, manufacturer specs, and public forum data into actionable buying guides.

Pros & Cons

Pros

  • ✓ Connector M.2-2280-B-M-S3 (B/M Key)
  • ✓ Backed by Google Coral's warranty and support channels

Cons

  • ✗ Confirm socket / form-factor / power-rating compatibility against your build before ordering
  • ✗ Price, stock, shipping and returns are set by the retailer and may have changed since this page was last updated

Manufacturer description

Performs high-speed ML inferencing The on-board Edge TPU coprocessor is capable of performing 4 trillion operations (tera-operations) per second (TOPS), using 0.5 watts for each TOPS (2 TOPS per watt). For example, it can execute state-of-the-art mobile vision models such as MobileNet v2 at 400 FPS, in a power efficient manner. See more performance benchmarks. Works with Debian Linux Integrates with any Debian-based Linux system with a compatible card module slot. Supports TensorFlow Lite No need to build models from the ground up. TensorFlow Lite models can be compiled to run on the Edge…

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SpecPicks Verdict

SpecPicks scored Google Coral SOM System-On-Modules using a rating × review-volume × price-fit model applied to published specs and reviews across the ai accelerators category. Google Coral's ai accelerators lineup sits within the broader category mid-tier; price and specs are the deciding factors over brand loyalty here. Judge it on the specification table and the price rather than on sentiment: check the figures that decide your use case, then put it side-by-side with two or three close alternatives in the Compare tool before clicking through.

Common buyer scenarios for ai accelerators of this kind: matching it to an existing build, replacing a failing part, or upgrading from a previous-generation equivalent. Check the spec table below against your current setup — particularly socket / form-factor / power-rating fields — and confirm compatibility on the retailer's listing before purchase. Prices and stock are set by the retailer and may have moved since this page was last updated.

Key Features

  • Connector M.2-2280-B-M-S3 (B/M Key)
  • Google Edge TPU coprocessor
  • 22.00 x 80.00 x 2.35 mm
  • Supports TensorFlow Lite
  • Works with Debian Linux

Full Specifications

ASINB08BR3C21M
BrandGoogle Coral
Item Weight0.317 ounces
ManufacturerGoogle Coral
Product Dimensions2 x 2 x 1 inches
Date First AvailableJanuary 2, 2020
Full listing titleSOM System-On-Modules - SOM Google Edge TPU ML Compute Accelerator, Integrate The Edge TPU into Legacy and New Systems Using a Standard M.2-2280-B-M-S3 (B/M Key)

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SOM System-On-Modules - SOM Google Edge TPU ML Compute Accelerator, Integrate The Edge TPU into Legacy and New Systems Using a Standard M.2-2280-B-M-S3 (B/M Key) is listed on Amazon. Price, availability, shipping and returns are set by Amazon and its sellers — check the listing for current terms. SpecPicks earns a small commission on qualifying purchases — thank you for supporting independent review work.

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SOM System-On-Modules - SOM Google Edge TPU ML Compute Accelerator, Integrate The Edge TPU into Legacy and New Systems Using a Standard M.2-2280-B-M-S3 (B/M Key)
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