30 AI Hardware Companies & Startups [2026]

Executive Summary: Top 30 AI Hardware Companies

The top AI hardware startups and companies are:

1. TSMC: The world’s leading semiconductor foundry that manufactures cutting-edge AI chips for global tech giants. TSMC shares have gained 30% till October this year.
2. NVIDIA: Develops specialized GPUs and AI hardware accelerators that power large-scale AI models and systems.
3. AMD: Designs high-performance processors and GPUs optimized for AI workloads and data center applications. OpenAI is set to deploy 6 gigawatts (GW) of AMD GPUs.
4. Qualcomm: Creates AI-centric system-on-chips (SoCs) for edge devices like smartphones and IoT hardware.
5. Google (Alphabet): Builds custom AI hardware like tensor processing units (TPUs) for accelerating machine learning tasks.
6. Groq: Develops ultra-fast AI inference hardware and offers unique architectures for latency-critical machine learning applications. It raised USD 500 million recently.
7. IBM: Produces specialized AI chips and accelerators with a focus on enterprise and cloud AI solutions.
8. Intel: Manufactures advanced CPUs, GPUs, and dedicated AI chips for servers, edge, and personal computing markets. Its Gaudi 3 AI accelerator chip enables large-scale AI training and inference.
9. Amazon: Designs custom AI chips (like Inferentia and Trainium) for use in its AWS cloud infrastructure to accelerate machine learning. Amazon plans to invest at least USD 20 billion in Pennsylvania to expand its data center infrastructure.
10. Microsoft: Incorporates custom AI processors in its Azure cloud platform and supports edge AI hardware development.
11. Rebellions: Builds AI-specific chips focused on high-efficiency inference for data centers and edge devices.
12. Tenstorrent: Creates hardware accelerators aimed at speeding up neural network processing and scalability for AI. It announced a partnership with LG Electronics.
13. Vaire: Specializes in high-performance, low-power AI hardware for edge applications in various industries. The startup raised a total funding of USD 4.5 million.
14. Axelera AI: Develops efficient edge AI chips that deliver powerful machine vision and inference capabilities. The startup raised USD 68 million in Series B funding.
15. NextSilicon: Offers AI accelerators to boost computational throughput in HPC and machine learning tasks. It raised USD 200 million in Series C funding.
16. Semron: Engineers ultra-compact AI hardware for real-time processing in edge and mobile environments. The startup raised EUR 7.3 million in seed round funding.
17. Mobilint: Focuses on AI processors optimized for mobility and low-power embedded systems. It raised KRW 20 billion in Series B funding.
18. Arago: Integrates AI hardware into its automation platforms for complex enterprise algorithm execution. The company raised EUR 22.1 million in seed funding.
19. NeuReality: Provides purpose-built AI inference hardware, centered around its NR1 AI-CPU chip.
20. Edgehax: Builds rugged, modular single-board edge AI hardware platforms that integrate compute, storage, and network capabilities.
21. NeuroNova: Makes neuromorphic hardware emulating neural architectures for efficient AI computation.
22. Neuromorphica: Develops neuromorphic chips and ultra‑low‑power smart sensors targeting autonomous vehicles, industrial robotics, secure communications, and healthcare.
23. Neucom: Offers a fully neuromorphic general‑purpose platform with event‑based processing and a user‑friendly SDK to convert algorithms into ultra‑low‑power edge deployments.
24. Spin-Ion: Creates materials and hardware that enhance the performance of next-generation AI chips.
25. Listen AI: Develops on-device AI hardware for audio and voice processing applications.
26. EVAS Intelligence: Builds AI accelerators for embedded vision and sensory systems in autonomous platforms. The startup raised USD 31.7 million in seed funding.
27. Dodola: Offers an AI plant‑care device with a camera that detects early plant diseases and pests to provide personalized watering plans via a mobile app.
28. Phoenix AI: Provides a fanless industrial edge AI gateway for intelligent video analytics. It supports up to four camera streams with simultaneous deep learning detection.
29. Onescope: Builds a pocket‑sized medical device that leverages AI for real‑time respiratory analysis and diagnostic support.
30. AI Blox: Makes a modular edge AI hardware platform with configurable GPU options and ruggedized variants for heavy‑duty vehicles and multi‑camera vision.

The AI hardware market is worth about USD 34.05 billion in 2025 and grows above 22.43% CAGR. Demand for generative AI, edge processing, and energy-efficient accelerators drives this expansion.

How We Selected these 30 AI Chip Startups & Companies

The data in this report comes from StartUs Insights’ Discovery Platform, covering over 9 million startups, scaleups, and tech companies globally. We applied filters such as location, founding year, technology readiness, and employee count to select 30 standout AI hardware companies.

We further evaluated each company based on estimated revenue, total money raised, and proprietary innovation metrics that reflect their real-world influence within the global tech ecosystem. The final list combines established leaders shaping the industry’s direction and emerging challengers making headlines with breakthrough innovations.

Explore the 30 Growing AI Hardware Companies & Startups to Watch in 2026

1. TSMC – World’s Leading Chip Manufacturer

• Founding Year: 1987
• Location: Taiwan

Taiwanese company TSMC develops advanced semiconductor manufacturing technologies that power global computing infrastructure. It fabricates high-performance wafers through precision lithography, material science, and process optimization that enable chips with superior performance and energy efficiency.

The company integrates logic, memory, and packaging innovations to produce scalable hardware platforms suited for intensive workloads, particularly in AI. Its research focuses on near and in-memory computing, embedded non-volatile memory, and 3D integration to enhance computational density and reduce latency.

TSMC reported a net income of NTD 452.30 billion in the 3rd quarter of 2025, which accounts for a 30.3% year-over-year (YoY) increase. These results are driven by the surging AI chip demand and partnerships with Nvidia and Apple.

TSMC shares have gained 30% till October, in 2025, based on optimism over AI, dismissing rising tariff concerns in the US.

2. NVIDIA – GPUs for AI Processing

• Founding Year: 1993
• Location: USA

US-based technology company NVIDIA creates GPU and accelerated computing solutions that power AI processing in PC, data center, and corporate environments. Its solution speeds up large-scale workloads related to model training, data analysis, and inference from cloud to edge environments. Nvidia achieves this through deep learning optimization and parallel processing architectures.

The same architecture that drives industrial AI is used to improve consumer apps using RTX GPUs. This allows AI PCs to provide sophisticated real-time experiences. Nvidia allows businesses to install intelligent systems more accurately and efficiently. Consequently, this enhances performance across computer environments and operationalizes AI more quickly.

The company announced a USD 500 billion investment for AI infrastructure in the US. This marked a shift in reliance on chip production in Taiwan.

The NVIDIA Blackwell GPU architecture is a purpose-built AI superchip that packs fifth-generation tensor cores and a new numerical format, 4-bit floating-point (NVFP4). It offers high computing performance with high accuracy.

The architecture also integrates NVIDIA NVLink‑72, the company’s new high-bandwidth interconnect. It enables ultra-fast GPU-to-GPU communication and scaling across multi-GPU configurations for demanding AI workloads.

3. AMD – CPUs, GPUs, and Accelerators for AI Processing

• Founding Year: 1969
• Location: USA

AMD is a US-based technology company that creates advanced CPUs, GPUs, and accelerators to support AI processing. For effective data handling and model training, it utilizes high-performance and adaptable computing architectures that balance workload distribution between CPU and GPU systems.

AMD eliminates the requirement for code rewrites by allowing organizations to scale AI applications while maintaining compatibility with current x86 software through its unified platform.

Its solutions offer the best performance per watt in the industry, lowering data center footprint, power consumption, and operating costs.

The US Department of Energy formed a USD 1 billion partnership with AMD to construct supercomputers with three times the AI capacity of current supercomputers.

Additionally, OpenAI is planning the deployment of 6 gigawatts (GW) of AMD GPUs based on a multi-year, multi-generation agreement. The initial 1 GW deployment of the AMD Instinct MI450 series GPU is expected to be in the second half of 2026.

4. Qualcomm – On-device AI Deployments

• Founding Year: 1999
• Location: USA

US-based technology company Qualcomm offers on-device AI technologies that integrate advanced computing capabilities directly into consumer PCs and edge devices. Its Snapdragon X Series processors featuring the new NPUs deliver up to 80 trillion operations per second (TOPS). This enables complex models to run locally without relying on the cloud.

The company’s architecture minimizes latency, enhances energy efficiency, and safeguards data privacy. This ensures that sensitive information remains on the device. Qualcomm’s designs optimize AI hardware for real-time performance and improve responsiveness across varied workloads.

Recently, Qualcomm’s stock value increased by 11% following the announcement of its new AI accelerator chips to compete with Nvidia and AMD.

The Qualcomm AI200 and AI250 accelerator chips enable rack-scale performance and higher memory capacity for data center generative AI inference at a lower total cost of ownership. Both chips also feature a software stack that is compatible with leading AI frameworks. This allows enterprises and developers to deploy secure, scalable generative AI across data centers.

5. Google (Alphabet) – Tensor Processing Units

• Founding Year: 2015
• Location: USA

US tech giant Google creates cloud tensor processing units, which include a line of specially made AI accelerators. These accelerators maximize the efficiency and cost of tasks related to training, fine-tuning, and inference in machine learning.

The system uses specialized hardware to effectively carry out matrix multiplications and other compute-intensive AI operations within Google’s worldwide data center architecture. It integrates natively with Google Kubernetes Engine for coordinating large-scale models and scales smoothly across distributed systems. The system also supports popular frameworks like PyTorch, JAX, and TensorFlow.

Further, the sixth-generation Trillium TPUs (v6e), released late last year, demonstrated a 4.7-times increase in performance and double the high-bandwidth memory compared to previous gen chips.

Alphabet recently announced a USD 75 billion investment in AI infrastructure. It focuses heavily on chips, servers, and networking equipment to power services like Google Search and its Gemini AI model.

Google also announced a USD 15 billion AI data center hub in Visakhapatnam, India, to expand its global AI hardware footprint.

6. Groq – Language Processing Unit

• Founding Year: 2016
• Location: USA

US-based company Groq offers a language processing unit (LPU) that enables fast and deterministic AI inference. It operates through a compiler-driven, software-defined architecture that eliminates traditional software complexity while executing token-based workloads with consistent, predictable performance.

The chip integrates hundreds of megabytes of on-chip SRAM as the primary weight storage, which reduces data latency and drives compute units at full speed. This supports scalable tensor parallelism across interconnected devices.

Its direct chip-to-chip connectivity aligns hundreds of LPUs to function as one coordinated core. This allows precise data synchronization and computation without relying on caches or switches.

Groq is raising up to USD 500 million in a new funding round that brings its valuation to around USD 6 billion. The company also committed USD 1.5 billion to Saudi Arabia’s AI ventures by supplying the necessary chips.

7. IBM – AI Hardware Developer

• Founding Year: 1911
• Location: USA

Global tech giant IBM provides advanced AI hardware by integrating analog and digital AI cores. The company’s heterogeneous computing architectures and comprehensive testbeds accelerate machine learning capacity.

Its technology functions through the in-memory storage and processing offered by analog AI cores, which speeds up computations. The digital AI cores apply reduced precision to established semiconductor technology to lower power requirements while maintaining rapid processing.

IBM’s high-bandwidth CPUs, specialized AI accelerators, and fast interconnect solutions allow high-speed data exchange between components.

The company’s AI Hardware Center produces specialized hardware like the Telum II processor and Spyre Accelerator. These hardware solutions integrate onboard AI acceleration cores and are being featured in new IBM systems such as the IBM z17 and Power11.

8. Intel – AI Processors

• Founding Year: 2003
• Location: USA

US-based company Intel builds advanced AI processors that optimize computing performance for complex machine learning workloads. Its technology integrates specialized architectures like Intel Xeon scalable processors and AI accelerators that enable efficient parallel processing and rapid inference.

The processors utilize hardware-optimized deep learning frameworks that enhance model training speed and improve power efficiency while maintaining secure data management. Intel’s extensive ecosystem of software tools and partner integrations streamlines AI deployment across cloud, edge, and enterprise environments. This ensures scalability and compliance.

The company’s revenue reached USD 13.7 billion in the third quarter of 2025, which is up 3% YoY due to the accelerated global demand for AI.

Further, Intel’s Gaudi 3 AI accelerator chip enables large-scale AI training and inference. It features 64 tensor processor cores and advanced memory or networking for generative AI and deep learning deployments. The chip claims up to 20% higher throughput and 2 times the performance against Nvidia offerings in inference for models like LLaMA 2.

9. Amazon – Chips for Cloud AI

• Founding Year: 1994
• Location: USA

US tech company Amazon develops AWS Inferentia, a family of AI inference chips that power deep learning and generative AI workloads in Amazon EC2. The chips accelerate AI model execution by integrating custom-built cores optimized for neural network inference and connecting them through high-bandwidth, low-latency interconnects.

The first-generation Inferentia chip supports EC2 Inf1 instances that deliver up to 2.3 times higher throughput and up to 70% lower cost per inference than comparable instances. The second-generation Inferentia2 chip achieves up to 4 times higher throughput and up to 10 times lower latency. This enables efficient deployment of large language and diffusion models.

Benchmark data shows Trainium chips can deliver two to five times faster processing and 3-8 times cheaper operation than Nvidia’s V100 GPU for specific workloads. Trainium also sustains approximately 54% lower cost per token compared to Nvidia’s A100 cluster.

Additionally, Amazon plans to invest at least USD 20 billion in Pennsylvania to expand its data center infrastructure for AI and cloud computing.

10. Microsoft – Cloud Infrastructure Hardware

• Founding Year: 1975
• Location: USA

Tech giant Microsoft delivers advanced cloud infrastructure hardware through its Azure Maia 100 AI accelerator. It supports large-scale and complex AI workloads in Azure data centers. The technology integrates a co-designed hardware and software stack optimized from silicon to the system level. This allows efficient execution of frontier AI models.

Maia 100 is built on a 5-nanometer process using TSMC’s advanced packaging technology. It features rack-level power management and a custom Ethernet-based network delivering 4.8 terabits of aggregate bandwidth per accelerator. Maia 100 also incorporates liquid cooling and thermal sidekicks that enhance efficiency and sustainability while maintaining data center density.

This year, Microsoft plans to spend USD 80 billion on building AI data centers, which is a significant increase from USD 53 billion in 2023.

11. Rebellions – Chiplets with High-Bandwidth Interconnects

• Founding Year: 2020
• Location: South Korea

South Korean startup Rebellions builds advanced chiplets with high-bandwidth interconnects for efficient generative AI processing. Its technology integrates multiple specialized compute units through low-latency interconnects.

The company’s chiplets enable faster data transfer and reduce communication bottlenecks in large-scale AI models. They feature a modular design that allows resource scalability and optimized power consumption across diverse workloads.

The startup aligns hardware architecture with AI compiler optimization to achieve faster model training and enhanced real-time inferencing efficiency. Its solution provides AI developers and data centers with a cost-effective and scalable foundation for new generative AI deployment.

Rebellions raised USD 250 million from investors, including Arm Holdings, at a total valuation of USD 1.4 billion.

12. Tenstorrent – AI Processing Chips

• Founding Year: 2016
• Location: Canada

Canadian startup Tenstorrent develops advanced AI processing chips that offer high-performance computing for AI workloads. Its main product, the Blackhole PCIe board, operates through an architecture built on 16 large RISC-V cores paired with up to 32 GB of GDDR6 memory per chip. This allows for scalability across multiple systems.

The Wormhole n150 and n300 PCIe boards integrate Tensix Cores that combine compute units, network-on-chip designs, local cache, and compact RISC-V cores to optimize data flow and memory efficiency.

The startup supports a broad range of data precision formats and offers improved compute performance relative to traditional GPUs. It streamlines AI training and inference with reduced infrastructure costs.

The startup secured Series D funding of USD 693 million, led by Samsung Securities and AFW Partners at a pre-money valuation of USD 2 billion.

Tenstorrent announced a partnership with LG Electronics to enhance the development of AI chips tailored to LG’s products and services.

13. Vaire – Low Energy Computing Chips

• Founding Year: 2021
• Location: UK

Vaire is a UK-based startup that provides near-zero energy computing chips to reduce power loss in data processing. The startup’s technology operates on the principle of reversible computing, where logic operations recover and recycle energy rather than dissipate it as heat.

The startup converts conventional chip inefficiency into sustainable performance. For this, it utilizes energy-conserving architectures that support high-performance computing for AI workloads. Vaire’s reversible computing test chip recycles 50% of its energy.

Vaire’s approach addresses the rising energy demands of modern computation while maintaining processing speed. This offers an efficient hardware foundation for future data infrastructure. The startup builds scalable, low-energy computing systems that decouple computing growth from energy consumption, enabling a more sustainable digital future.

The startup raised a total funding of USD 4.5 million as it concluded its seed funding round recently.

14. Axelera AI – Accelerator Chips for CNN applications

• Founding Year: 2021
• Location: Netherlands

Dutch startup Axelera AI provides accelerator chips that enhance convolutional neural network (CNN) applications on edge devices. It integrates proprietary in-memory computing technology with a RISC-V-based dataflow architecture to process visual data directly within memory. This reduces data movement and minimizes latency.

The startup’s platform combines custom hardware with an optimized software stack to deliver high performance while maintaining energy efficiency and scalability. It supports multiple AI inference workloads simultaneously to improve throughput and reduce power consumption in computer vision tasks.

Axelera AI thus enables businesses to deploy advanced AI solutions at the edge. This enables accessible, cost-efficient intelligence for sectors like smart cities, retail analytics, and industrial automation.

The startup raised USD 68 million in Series B funding to accelerate the development of its chips.

15. NextSilicon – Intelligent Software-defined Hardware Acceleration

• Founding Year: 2017
• Location: Israel

Israeli startup NextSilicon makes intelligent software-defined hardware acceleration technology for high-performance computing. Its Maverick-2 Intelligent Compute Accelerator integrates adaptive software layers with custom hardware to dynamically optimize data paths and computation workloads in real time.

The startup’s unified architecture eliminates bottlenecks between compute and memory. The architecture achieves efficient task distribution across heterogeneous resources while maintaining full programmability.

The accelerator supports workloads in HPC, AI, and vector database processing. It enables high throughput with reduced power consumption and latency. The startup thus provides researchers and organizations with a compute foundation that accelerates discovery in fields like cancer research and astrophysics.

NextSilicon successfully raised USD 200 million in Series C funding, reaching a valuation of USD 800 million.

NextSilicon also partners with Sandia National Laboratories for the Vanguard program. This supports the evaluation and deployment of advanced computing technologies for the US National Nuclear Security Administration‘s computing programs.

Semron is a German startup that develops advanced AI hardware, CapRAM. It is a memcapacitive in-memory computing technology that integrates hundreds of layers on a single chip to run generative AI models directly on devices like smartphones, wearables, and AR/VR headsets.

The startup’s proprietary architecture utilizes variable capacitance for storing AI model weights. It utilizes electric fields instead of electrical currents to perform calculations. This minimizes electron movement, reduces energy usage, and mitigates overheating.

Semron stacks silicon dies in a 3D configuration to achieve high density and efficiency. This supports AI models up to 500 times larger and delivers up to 50 times greater energy efficiency compared to memristive solutions. It also cuts deployment costs by as much as 95%.

The company makes high-performance, low-cost generative AI accessible for mobile and edge devices. This enables true on-device intelligence that improves user experience by eliminating reliance on cloud resources and optimizing energy and cost profiles for manufacturers.

The startup raised EUR 7.3 million in seed round funding led by Join Capital and supported by SquareOne, OTB Ventures, and Onsight Ventures.

17. Mobilint – High Performance Edge AI Chip

• Founding Year: 2019
• Location: South Korea

South Korean startup Mobilint offers high-performance edge AI chips that accelerate on-device and on-premise AI applications. The startup integrates dedicated neural processing units with co-optimized algorithms, software, and hardware technologies.

The startup’s chips execute complex AI inference tasks through a custom architecture. It balances high compute throughput, up to 80 TOPS, with extremely low power draws, as little as 3-25W. This is well-suited for vision-based models, robotics, and embedded industrial systems.

Mobilint’s chips, like Aries and Regulus, offer scalable efficiency, compact form factors, reduced reliance on cloud backends, and cost advantages over traditional GPU solutions.

The startup raised KRW 20 billion in Series B funding from investors like Kyobo Securities, Union Investment Partners, Daesung Private Equity, and Game Changer Investment.

18. Arago – Optical Computing Chip

• Founding Year: 2024
• Location: France

French deep tech startup Arago builds an AI accelerator powered by optical computing. The startup’s photonic chip maximizes energy efficiency and throughput for AI workloads.

It integrates a PyTorch-based software stack with its proprietary multi-physics processor. This allows light to perform matrix multiplications and essential AI operations at ultra-low power and high computational density.

The chip achieves 10 to 30 times reductions in energy consumption and executes high-volume AI tasks and pointwise operations with deterministic memory transfer. This results in multi-PetaOps performance and scalable throughput across tensor sizes in the hundreds and thousands.

The startup thus provides a practical optical computing solution that reduces operational costs and increases performance for modern data-centric industries.

Arago raised EUR 22.1 million in seed funding co-led by Earlybird, Protagonist, and Visionaries Tomorrow.

19. NeuReality – AI Inference Appliance Solution

• Founding Year: 2019
• Location: Caesarea, Israel

Israeli startup NeuReality offers NR1, an AI inference appliance solution that allows enterprises to accelerate AI inference in on-premises data centers and cloud environments. It offers high-performance neural network processing and system integration.

NR1 integrates pre-loaded generative and agentic AI models and features an architecture that bypasses legacy CPU/NIC bottlenecks. It utilizes up to 16 GPUs and 10 NR1 Inference Modules in a single 4U chassis.

The plug-and-play deployment and scalable performance enable cloud service providers and enterprise customers to optimize AI infrastructure. This reduces operational complexity and realizes savings in energy consumption and total cost of ownership.

NeuReality raised USD 20 million in new funds from the European Innovation Council (EIC) Fund, Varana Capital, Cleveland Avenue, XT Hi-Tech, and OurCrowd.

20. Edgehax – Edge AI Devices

• Founding Year: 2025
• Location: India

Indian startup Edgehax develops AI hardware and an IoT prototyping platform that accelerates the rapid development of edge-enabled products. The platform integrates compute, network, and storage capabilities into a single-board computer.

This way, the startup streamlines the process to build and deploy intelligent devices directly at the edge for brands, OEMs, startups, and innovators.

The solution unifies complex hardware interfaces and pre-built software stacks. This allows efficient data gathering, processing, and secure transmission between connected physical devices and digital applications without extensive setup.

As a result, the startup empowers organizations to convert ideas into robust edge applications to optimize time-to-market and enable responsive, data-driven solutions for the physical world.

Edgehax raised USD 158 000 in seed funding led by Inflection Point Ventures.

21. NeuroNova – Neuromorphic Chips

• Founding Year: 2024
• Location: Italy

Italian startup Neuronova builds a neuromorphic chip that processes AI workloads using less than 1 µW. It utilizes a fully analog pipeline that extends from sensor input to in-chip computation. The chip combines in-memory computing with analog spiking neuron and synapse circuits.

The chip processes real-time temporal sensor data efficiently at the edge without relying on digital architectures or microcontrollers. This design enables direct sensor-to-chip integration.

NeuroNova eliminates the need for analog-to-digital conversion and offers a fully programmable, API-driven interface. It addresses real-world applications like always-on monitoring in wearables, IoT, and autonomous devices.

The startup raised EUR 1.5 million in pre-seed funding. The funding was led by 360 Capital, Tech4Planet, and CDP VC.

22. Neuromorphica – Edge Computing Chips

• Founding Year: 2021
• Location: Bulgaria

Bulgarian startup Neuromorphica offers edge computing chips engineered for artificial intelligence workloads. The startup utilizes neuromorphic architectures to enable real-time processing in energy-constrained environments.

The startup’s chips operate using spiking neural networks, which activate only in response to relevant events. It mimics the sparse, event-driven signaling of biological neurons and drastically reduces power consumption during idle states.

Neuromorphica’s solutions include integration of ultra-smart sensors with active power usage in the milliwatt range, real-time adaptability, and application versatility.

The company allows organizations to deploy high-performance, always-on intelligence at the edge to improve operational efficiency and enable new classes of applications that depend on low latency and embedded autonomy.

23. Neucom – Neuromorphic Chips

• Founding Year: 2024
• Location: Denmark

Danish startup Neucom provides ADA, a neuromorphic general-purpose computing platform. It offers efficient and adaptable performance for post-quantum cryptography (PQC). The platform processes event-based data before reaching its neuromorphic inference cores. This removes traditional CPU-intensive encoding and decoding stages.

The startup’s hybrid architecture combines the flexibility of microcontrollers with the energy efficiency of ASICs. ADA executes complex cryptographic algorithms while reducing power consumption.

Neucom’s technology accelerates secure and energy-efficient computation in resource-constrained environments. It provides a scalable foundation for next-generation cryptographic and edge intelligence applications.

24. Spin-Ion – Green AI Chip

• Founding Year: 2017
• Location: France

Spin-Ion is a French startup that develops advanced ion beam treatment technology to enhance the performance of magnetic materials and devices for spintronic applications.

The startup’s proprietary ion engineering process modifies material properties at the atomic scale. This enables improved magnetic layer uniformity, reduced noise, and greater efficiency in data processing and retention.

The startup integrates with standard semiconductor manufacturing workflows to facilitate precise control and scalability for industrial production. Spin-Ion’s solutions deliver higher endurance, lower power consumption, and increased reliability.

The startup is associating with major academic centers, notably the University of Paris-Sud and CNRS.

25. Listen AI – AI System-on-Chip

• Founding Year: 2020
• Location: China

Chinese startup Listen AI offers system-on-chip solutions for intelligent terminal systems by integrating proprietary AI algorithms. The startup’s proprietary chip design technology results in high-performance semiconductor chips targeted at applications like smart home appliances, intelligent vehicles, and consumer electronics.

Listen AI embeds algorithm-defined architecture from the inception of the chip to create streamlined, efficient chips that support both online and offline voice interaction, speech synthesis, and processing in real time.

The startup’s solutions feature support for end-to-end scenarios, including AI-powered educational devices and voice-controlled smart terminals. They also provide simplified integration for device manufacturers.

26. EVAS Intelligence – Computing Chip Company

• Founding Year: 2022
• Location: China

Chinese startup EVAS Intelligence provides AI computing chips purpose-built for automobiles, leveraging its proprietary DSA architecture. The startup’s computing chip, EVAMIND, is based on the RISC-V instruction set to maximize parallel AI processing.

The startup integrates dedicated hardware and software stacks to power intelligent vehicles and robotics. This enables real-time high-throughput data handling and advanced driving assistance without compromising safety or efficiency.

EVAS Intelligence’s technology features a modular chip design that supports adaptive sensing, decision-making, and control functions. The underlying architecture achieves high computational density to optimize cost-performance balance for automotive OEMs.

The company provides manufacturers with scalable, high-performance AI solutions that accelerate the deployment of autonomous and assisted driving systems, driving forward the evolution of intelligent mobility.

The startup raised USD 31.7 million in seed funding led by private equity firms CTC Capital and Sunic Capital.

27. Dodola – AI Plant Care Device

• Founding Year: 2022
• Location: Serbia

Serbian startup Dodola delivers an AI-powered device that automates plant care by recognizing individual indoor and outdoor houseplants. The device sets and executes precise watering plans based on real-time sensor data.

The device leverages a high-resolution camera and integrated sensors to monitor humidity, pH, temperature, and soil moisture, adjusting its approach to ensure ongoing optimal conditions for plant health.

The startup continuously scans for plant diseases and pests to provide immediate treatment recommendations through a connected mobile app. It also allows users to monitor and manage their plants remotely.

Dodola also utilizes recycled materials and a solar-powered battery system. The startup recycles at least 200 kilograms of plastic per 1000 devices and saves over 40 000 liters of water and 200 000 kWh of electricity annually.

28. Phoenix AI – Industrial Edge AI

• Founding Year: 2017
• Location: Belgium

Belgian startup Phoenix AI builds on-device intelligent video analytics systems that integrate proprietary AI models directly into camera hardware for industrial and urban use cases.

The startup embeds neural network inferencing into its hardware for cameras to process video and generate actionable insights in real time at the edge. This removes reliance on cloud infrastructure.

As part of the NVIDIA Metropolis Program, Phoenix AI’s solutions feature rapid detection, automated event response, and support for multiple object types across varied conditions. It ensures continuous data processing with minimal latency.

The startup deploys modular, scalable AI applications compatible with existing systems. It enables industries to enhance operational efficiency and public safety, delivering tangible improvements in automation and decision-making workflows.

29. Onescope – AI-powered Medical Devices

• Founding Year: 2018
• Location: Switzerland

Onescope is a Swiss startup that offers Pneumoscope, an AI-powered pocket-sized medical device for comprehensive respiratory assessment. It captures lung and vital sign data and transmits it securely to a cloud-based AI engine. The device rapidly analyzes signals to assist healthcare professionals with actionable diagnostic insights.

Pneumoscope features automated anomaly detection, a user-friendly interface, and connectivity for real-time reporting. This enables frontline health workers and remote clinicians to ensure continuous patient oversight.

Onescope received a CHF 1.2 million grant from Innosuisse, aimed at developing a product and AI algorithms for automated respiratory disease classification. Additionally, it received a CHF 50 000 grant from the Fongit Innovation Fund.

30. AI Blox – Modular AI Computers

• Founding Year: 2021
• Location: Belgium

Belgian startup AI-Blox provides modular AI computers that deliver real-time, efficient processing for edge AI applications. The startup’s platform enables the deployment of AI directly at the data source.

It integrates hardware and software to ensure quick and localized data analysis without reliance on remote servers. The startup’s solution prioritizes local data processing. It improves network throughput, reduces latency, and enhances sustainability by minimizing cloud dependence.

AI Blox empowers organizations to implement intelligence where it is most needed, supporting flexible, scalable, and sustainable AI deployment at the edge. The startup announced that it closed its first funding round with the backing of Smartfin.

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