Global and U.S. Edge AI Acceleration Modules Market Report, Published by QY Research.

QY Research has released a comprehensive new market report on Edge AI Acceleration Modules, compact hardware modules that offload and accelerate AI inference workloads at the edge of the network-close to sensors, machines, and devices. By integrating dedicated AI compute engines (GPU, NPU, TPU, FPGA, or ASIC) with high-speed memory and industrial interfaces, these modules deliver low-latency, energy-efficient inference without relying on centralized cloud resources. As vision AI, robotics, and real-time analytics proliferate across factories, vehicles, and infrastructure, this report provides an in-depth assessment of global market size, silicon architectures, software ecosystems, and competitive dynamics.

Core Market Data

Global market size: USD 4.5 billion
CAGR (2024-2030): 21.8%
Average price: USD 240 per unit
Annual production: 18.9 million units
Gross margin: 40%
Production capacity: 22.5 million units

List of Main Players

NVIDIA (NASDAQ: NVDA, USA)
Intel (NASDAQ: INTC, USA)
Qualcomm (NASDAQ: QCOM, USA)
Samsung (KRX: 005930, South Korea)
Apple (NASDAQ: AAPL, USA)
MediaTek (TWSE: 2454, Taiwan)
AMD (NASDAQ: AMD, USA)
IBM (NYSE: IBM, USA)
Micron Technology (NASDAQ: MU, USA)
Alphabet (NASDAQ: GOOGL, USA)
Advantech (TWSE: 2395, Taiwan)
ADLINK (TWSE: 6166, Taiwan)
SECO (BIT: IOT, Italy)
Kontron (FWB: KTN, Germany)
Murata (TSE: 6981, Japan)
NXP Semiconductors (NASDAQ: NXPI, Netherlands)
Huawei (Private, China)
Hailo (Private, Israel)
Kinara (Private, USA)
EdgeCortix (Private, Japan)
Axelera AI (Private, Netherlands)
Mythic AI (Private, USA)
Untether AI (Private, Canada)
Celestial AI (Private, USA)
Kneron (Private, Taiwan)
GreenWaves Technologies (Private, France)
Toradex (Private, Switzerland)
Aaeon (Private, Taiwan)
Premio (Private, USA)
Virtium (Private, USA)

1. By Accelerator Architecture

GPU-based edge AI acceleration modules
NPU/TPU-based inference accelerators
FPGA-based AI acceleration modules
Custom ASIC edge AI accelerators

2. By Form Factor

PCIe and M.2 AI accelerator modules
System-on-Module (SoM) AI accelerators
Mini PCIe and COM Express AI modules
Standalone edge AI cards

3. By Power Envelope

Ultra-low power (Below 5 W)
Low power (5-15 W)
Performance class (15-75 W)

4. By Application

Machine vision and quality inspection
Autonomous mobile robots and AMRs
Smart cameras and video analytics
Industrial edge gateways and controllers
Transportation, smart cities, and retail analytics

Case Study for Bidding

Time: June 2024
Supplier: NVIDIA
Buyer: Foxconn
Product: GPU-based edge AI acceleration modules for vision-based quality inspection lines
Quantity: 85,000 modules
Contract Value: USD 195 million
Delivery Timeline: Q2 2026

Case Study for End Using

End User: Bosch Application: Real-time visual inspection and predictive analytics in smart manufacturing plants.

Installation Location / Usage Stage

Installed inside industrial edge servers and vision controllers.
Connected to high-resolution cameras and PLCs.
Operated continuously in factory environments with real-time constraints.

Purpose

Accelerates deep-learning inference at the production line.
Enables defect detection and classification in milliseconds.
Reduces data transfer to cloud systems.

Effect

Improved defect detection accuracy by 25-40%.
Reduced inspection latency to sub-10 ms.
Lowered bandwidth and cloud operating costs.

Alternative AI Computing Approaches

CPU-only edge computing offers flexibility but insufficient AI performance.
Centralized cloud AI provides scale but introduces latency and connectivity risk.
Smart sensors with embedded AI reduce hardware count but limit model complexity.
On-premise GPU servers deliver power but at higher cost and energy use.

Therefore, Edge AI Acceleration Modules are a critical enabler of real-time, scalable AI at the edge, delivering high inference performance, energy efficiency, and deployment flexibility-supporting the next wave of intelligent machines, vision systems, and autonomous operations across industrial and commercial environments.

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