Edge Computing AI Accelerator Cards Market Research: the market reached US$ 26,805 million in 2024

The Edge Computing AI Accelerator Cards market is entering a high-velocity growth phase. According to QYResearch Report Edge Computing AI Accelerator Cards - Global Market Share and Ranking, Overall Sales and Demand Forecast 2025-2031, the market reached US$ 26,805 million in 2024 and is projected to hit US$ 99,014 million by 2031 at a 21.9% CAGR (2025-2031). Momentum is reinforced by a series of 2024-2025 product launches that are pushing generative, vision, and robotics workloads to the edge while slashing latency and energy cost per inference. Nvidia introduced Jetson AGX Thor bringing Blackwell-class compute to robots, Hailo launched the Hailo-10H M.2 GenAI accelerator at single-digit watts, AMD rolled out Versal AI Edge Gen 2 doubling down on deterministic, safety-critical edge performance, Intel expanded its Data Center GPU Flex family for edge inference, and Huawei advanced its Atlas 300I Pro with improved Ascend architecture.

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Latest Data

· Global Market Size 2024: US$ 26,805 million

· Forecast 2031: US$ 99,014 million

· CAGR 2025-2031: 21.9%

· Regions: North America, Europe, Asia Pacific, South America, Middle East & Africa

· Forecast Units: USD million (value)

· Coverage: Revenue & volume forecast, company share, competitive benchmarking, growth factors and restraints, regulation, and risk analysis

Classification

· Cloud Deployment

· Device Deployment

Applications

· Smart Grid

· Smart Manufacturing

· Smart Rail Transit

· Smart Finance

· Other

Leading Companies

NVIDIA
AMD
Intel
Huawei
Qualcomm
IBM
Hailo
Denglin Technology
Haiguang Information Technology
Achronix Semiconductor
Graphcore
Suyuan
Kunlun Core
Cambricon
DeepX
Advantech

Five Product Snapshots

NVIDIA - Jetson AGX Thor

· Blackwell-based edge module for robotics and physical AI

· Up to 7.5× AI compute compared with Jetson Orin

· Up to 3.5× higher energy efficiency

· Supports up to 128 GB LPDDR5X memory

· Developer kit listed at US$ 3,499; production modules US$ 2,999

AMD - Versal AI Edge Series Gen 2

· Up to 3× TOPS per Watt vs. previous generation

· Up to 10× more scalar compute

· Designed for deterministic real-time control and functional safety

· Targeting industrial automation, medical imaging, aerospace, and defense

Intel - Data Center GPU Flex 170

· 32 Xe cores with 512 XMX engines

· 16 GB GDDR6, 576 GB/s bandwidth, 150 W power envelope

· PCIe Gen4 x16 interface

· Optimized for video analytics and media-rich pipelines at the edge

Huawei - Atlas 300I Pro Inference Card

· Ascend 310 series processor with 8 AI cores and 8 CPU cores

· 24 GB LPDDR4X memory with ~204.8 GB/s bandwidth

· Half-height, half-length PCIe card for industrial deployment

· Capable of approximately 140 INT8 TOPS

Hailo - Hailo-10H

· Around 40 TOPS (INT4) or 20 TOPS (INT8) performance at 2.5 W

· M.2 form factor (2242/2280) with PCIe Gen3 x4

· Equipped with 4-8 GB LPDDR4/4X memory

· Focused on running GenAI workloads locally on PCs and embedded systems

Verified Downstream Users

Amazon Robotics
Caterpillar
Figure AI
Meta Platforms
Siemens
John Deere
Apptronik
Foxconn
H3C Technologies
ZTE
Lenovo
Dell Technologies
Hewlett Packard Enterprise
Supermicro
Cisco Systems

Market Trend

Physical AI goes mainstream in robotics

Robotics vendors are moving LLM and VLM reasoning from the cloud to on-device compute for lower latency, privacy, and resilience. NVIDIA's Jetson AGX Thor brings a 7.5× performance uplift over Orin with 3.5× better efficiency, enabling robots to handle multi-modal perception and interaction directly at the edge. Early adopters include Amazon Robotics, Caterpillar, Meta, and Figure AI, with agriculture, logistics, and manufacturing poised to expand adoption.

Low-power GenAI with quantized inference

Edge accelerators are adopting INT4 and INT8 quantization to allow transformer models and diffusion networks to run on small devices. Hailo-10H exemplifies this with 40 TOPS INT4 performance at just 2.5 W. This trend makes on-device copilots and real-time inspections feasible without relying on high-power GPUs or cloud servers.

Adaptive SoCs for safety-critical applications

Industrial, medical, and rail environments require deterministic performance and functional safety. AMD's Versal AI Edge Gen 2 delivers up to 3× TOPS per Watt with stronger real-time control and safety features. These adaptive SoCs and FPGA solutions are gaining traction in regulated industries that demand consistent latency and certified safety standards.

Memory-rich edge accelerators for GenAI

Accelerator cards are increasingly equipped with larger onboard memory to support bigger models and reduce round-trips to the cloud. Qualcomm's Cloud AI 100 Ultra with 128 GB on-card memory achieves lower latency on generative workloads like Stable Diffusion, and scales throughput by multi-device scheduling, proving that high-memory PCIe cards are now vital for edge GenAI.

Industrial adoption through platform partnerships

Industrial ecosystems are expanding by integrating accelerator cards with complete software platforms. Siemens has strengthened its partnership with NVIDIA, embedding GPU-powered AI into industrial PCs for predictive maintenance and inspection. Such partnerships are accelerating adoption in smart manufacturing and smart grid sectors.

Regional ecosystems and localized supply chains

In China, accelerator providers like Cambricon and Baidu Kunlunxin have secured large orders from H3C and ZTE for edge deployments, driven by national priorities for domestic AI hardware. Local supply chains are fostering CUDA-compatible alternatives and new PCIe-based edge cards tailored for surveillance, transport, and utilities.

Open-source enablement

Firmware and drivers for AI accelerators are being upstreamed into major Linux distributions, cutting deployment time and easing fleet management. Qualcomm's AIC100 firmware inclusion in linux-firmware in 2024 exemplifies how open-source contributions accelerate adoption across enterprise edge systems.

Diversified form factors and power envelopes

The product spectrum is widening from 2.5 W M.2 modules to 150 W PCIe cards, allowing customers to right-size AI acceleration for their application. PCIe Gen4 and Gen5 adoption is expanding, with device-level accelerators serving mobile robots and cameras, while server-class cards address retail, utilities, and transportation back-ends.

Strategic Takeaways

· Robotics and industrial AI will be the strongest growth engines through 2025, powered by Blackwell-class modules and low-power GenAI at the edge.

· Quantization and memory-centric designs will define leadership, with INT4/INT8 compute and high onboard memory emerging as critical metrics.

· Adaptive SoCs and FPGA-based accelerators are set to dominate regulated industries requiring functional safety and deterministic performance.

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