Edge AI: Why AMD is the Best Value Play for 2026

Artificial intelligence (AI) is one of the major tech disruptors of this decade, driving fundamental shifts across industries and society at large.

Data show that approximately one in six people worldwide are now using generative AI tools. Moreover, 90% of tech workers are already using AI in their jobs. Despite this adoption, the AI industry is still projected to grow by about 9x by 2033.

With this massive adoption comes soaring compute costs, growing latency challenges, and rising concerns about security, energy usage, and scalability. Companies are now realizing that constantly sending data to remote servers for AI inference—cloud computing or Cloud AI—is costly, slow, and involves privacy risks.

In Cloud AI, companies leverage the vast resources of platforms such as AWS, Azure, and Google Cloud to deliver AI services. This allows users to access AI models on demand over the internet without building their own infrastructure.

The foundation of Cloud AI is hyperscalers—massive AI data centers that provide extreme scalability to handle workloads far beyond traditional on-premises capabilities. With their vast horizontal server arrays, they provide businesses with the resources to efficiently access, build, train, deploy, and maintain AI applications.

This combination of cloud computing and AI offers the benefits of cost efficiency, scalability, and the ability to leverage shared models. But at the same time, it has significant drawbacks, including high recurring costs due to computational resources, storage, data transfer, and specialized expertise required for continuous use.

Other issues faced by Cloud AI include latency, security risks, data privacy, internet dependency, limited control, and vendor lock-in.

With the cloud proving expensive and challenging for consumer apps, laptops, industrial systems, and real-time use cases, companies are pivoting to “Edge AI.” Performing local inference on the device rather than relying on expensive cloud GPUs is now reshaping how AI gets deployed beyond data centers.

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Edge AI Explained: Why Inference Is Moving On-Device

The industry is undergoing a strategic and architectural shift toward Edge AI, moving AI away from centralized, power-hungry data centers toward local inference hardware.

In Edge AI, artificial intelligence is combined with edge computing to remove the reliance on the cloud by enabling devices to process data locally. “Edge” here refers to the device being used—such as a phone, car, camera, TV, sensor, or medical device—so edge computing means the computer designed to process data is near or inside that device.

Besides the edge devices that collect and process data, other key components include AI models trained in the cloud and deployed on the edge, as well as specialized hardware chips that efficiently handle AI tasks locally.

With this pivot to power-efficient devices, the aim is to address critical issues of latency and data privacy by enabling real-time processing on user devices, where data is actually generated.

This means instead of sending data to an off-site data center, computations are performed close to the source, allowing devices to make decisions in milliseconds without requiring an internet connection. The data is essentially put to use just as it is created by the device.

This real-time processing is crucial for robotics, autonomous vehicles, and surveillance applications that require rapid response times.

Edge computing also relieves the heavy strain on data centers by eliminating the need to move data back and forth. In Edge AI, only relevant data is sent to the cloud, reducing bandwidth requirements and associated costs.

Besides cost-effectiveness, moving from gigawatt-scale data centers to devices offers the advantage of energy efficiency, as they can run AI with minimal power. By keeping sensitive data local, companies can further address security concerns, protecting against unauthorized access and data breaches.

Thanks to the benefits of speed, cost, privacy, and energy efficiency, AI inference is increasingly being performed at the edge.

In AI, inference is the model’s actual operation—a process that begins after a model has been trained and stops learning. Inference is when the model begins to work, drawing conclusions from data and turning that knowledge into real-world results.

Local inference refers to running AI models directly on a user’s machine using specialized silicon, such as NPUs (Neural Processing Units) embedded in CPUs or system-on-chip (SoCs), rather than sending every request back to a cloud GPU.

NPUs are AI chips optimized for complex computations in deep learning tasks such as natural language processing, speech processing, object detection, and image recognition. These specialized AI accelerator chips enable fast, on-device inference with minimal energy consumption, enabling real-time applications.

In practice, local inference means that your laptop, PC, embedded system, or even smartphone can run large language models (LLM queries), vision models, or assistant workloads without hitting large, expensive, powerful servers.

This reduces latency, lowers bandwidth costs, improves privacy, and decreases server bills. Because Edge AI systems can operate without constant internet connectivity, they offer improved reliability, making them suitable for remote areas.

As AI workloads scale from experimentation to everyday use, this shift toward local inference is no longer theoretical but a necessity as billions of devices gain AI capabilities and cloud-based inference becomes unsustainable at scale.

Edge AI market research estimates AI processors at the edge can be worth almost $60 billion by the end of this decade, up from $9 billion in 2020, driven largely by local compute in PCs and devices.

Already, this year, the trend of local inference has moved from research demos to real products, as shown by CES 2026, where dozens of AI PCs and edge form factors were demonstrated with on-device inference capabilities.

For instance, Ambarella launched its CV7 vision SoC with advanced on-device edge AI processing for various real-time perception applications. Qualcomm doubled down on vertical integration for “intelligent computing everywhere” with its Snapdragon X Elite Gen 2 PCs. Broadcom is also focusing on integrating “Neural Engines” into processors to enable local AI, specifically targeting smart-home applications.

When it comes to giants like Apple (AAPL -3.46%) and NVIDIA (NVDA -4.38%), the former is utilizing a hybrid model of on-device AI and “Private Cloud Compute”, while the latter is making a shift toward “physical AI” and on-device processing.

Physical AI, which extends AI beyond the digital world into robotics, drones, and industrial machinery, is one of the emerging trends in the Edge AI sector and is expected to be a major growth driver.

Why AMD Is Positioned to Win the Edge AI Hardware Cycle

In the world of Edge AI stocks, one of the most prominent names to watch is Advanced Micro Devices (AMD +0.04%), which develops semiconductors, processors, and GPUs for data centers, AI, gaming, and embedded applications.

Earlier this month, at CES 2026, AMD Chair and CEO Lisa Su shared the company’s goal of delivering AI for everyone as she highlighted an edge-oriented AI strategy across PCs, embedded devices, and developers, reinforcing the company’s focus on local inference hardware beyond hyperscale cloud environments.

As part of this approach, the company introduced a new line of AI processors. This includes the Ryzen AI 400 Series processor for AI PCs, with built-in NPUs that deliver about 60 TOPS of AI compute for local inference. This latest version of AMD’s AI-powered PC chips features 12 CPU Cores and 24 threads and will enable faster (1.3x) multitasking than its competitors. They are also 1.7x times faster at content creation.

PCs, including the Ryzen AI 400 Series processor, will become available in the current quarter.

At a press briefing, Rahul Tikoo, senior vice president and GM of AMD’s client business, noted that they have already expanded to over 250 AI PC platforms, representing 2x growth over the last year. He said:

“In the years ahead, AI is going to be a multi-layered fabric that gets woven into every level of computing at the personal layer. Our AI PCs and devices will transform how we work, how we play, how we create, and how we connect with each other.”

AMD also introduced Ryzen AI Max+ chips at the world’s biggest consumer electronics show. It is aimed at premium notebooks and mini-PCs for advanced local inference, content creation, and gaming.

For developers, AMD announced the Ryzen AI Halo platform for on-device model development, all set to bring powerful AI development capabilities to a compact desktop PC in the next quarter.

Its new portfolio of embedded x86 processors is meanwhile designed to power AI-driven applications at the edge. The new P100 and X100 Series processors deliver high-performance AI compute for smart healthcare, automotive digital cockpits, and humanoid robotics.

“No matter who you are and how you use technology on a daily basis, AI is reshaping everyday computing. You have thousands of interactions with your PC every day. AI is able to understand, learn context, bring automation, provide deep reasoning, and personal customization to every individual.”

– Rahul Tikoo, Senior VP & GM of Client Business

With these moves, the American chipmaker is targeting on-device AI workloads and contributing to the industry’s push toward local inference and distributed intelligence across billions of endpoints.

Besides enabling AI computation at the edge, the company has shown off its advanced AI processors, which are used in the data center server racks. An enterprise version of the MI400 series chip (the MI440X) has been designed for on-premises use but is not specifically designed for AI clusters.

To meet the future computing needs of companies like OpenAI, AMD has also previewed the MI500 platform, which the company says is designed to enable orders-of-magnitude performance gains at the system and rack level compared to earlier generations, rather than a simple one-to-one chip upgrade. The chips will be launched next year.

In addition to an impressive product portfolio, AMD boasts an excellent roster of clients, including OpenAI, Blue Origin, Liquid AI, Luma AI, World Labs, Illumina, Absci, AstraZeneca, and Generative Bionics, which have been leveraging the company’s technology to turn the promise of AI into real-world impact. According to Su:

“As AI adoption accelerates, we are entering the era of yotta-scale computing, driven by unprecedented growth in both training and inference. AMD is building the compute foundation for this next phase of AI through end-to-end technology leadership, open platforms, and deep co-innovation with partners across the ecosystem.”

In an interview, she noted that, given the “incredible” demand for AI, which is “going through the roof,” it will require massive, unavoidable investment in computing power and cutting-edge hardware to stay competitive in the AI market.

The world, according to her, would need more than “10 yottaflops” of compute, “10,000 times more compute than we had in 2022,” to keep up with AI’s growth. And in line with that, she shared the company’s blueprint for yotta-scale infrastructure, unveiling the AMD “Helios” rack-scale platform, which will deliver up to 3 AI exaflops of performance in a single rack.

At the same event, AMD’s top competitor, Nvidia, launched its next-gen Vera Rubin platform, comprising six chips and expected to debut later this year.

But while Nvidia continues to focus on hyperscale with top-tier mega GPUs and enterprise stacks, AMD is taking a diversified approach to its products that enable AI functionality at lower total costs. This contrast increasingly defines the AMD vs NVIDIA 2026 debate.

AMD is undercutting NVIDIA on price for “AI PC” chips in order to capture a larger share of the emerging AI PC market by making high-performance AI-capable processors more affordable for OEMs and consumers. As a result, AMD is seen as one of the key undervalued AI stocks in the market.

As of January 20, 2026, AMD, with a $377.4 billion market cap, is trading at $231.87, up 8.25% YTD and 90.87% in the past year. It has an EPS (TTM) of 1.92 and a P/E (TTM) of 120.97.

AMD’s financial position is strong too, with Jean Hu, AMD executive vice president, CFO, and treasurer, noting, “Our continued investments in AI and high-performance computing are driving significant growth and positioning AMD to deliver long-term value creation.”

This is evident in the global semiconductor company’s record revenue of $9.2 billion in the third quarter of 2025. This includes $4.3 billion from the data center segment, up 22% YoY, $4 billion from combined client and gaming revenue, up 73% YoY, and $857 million from the embedded segment, down 8% YoY.

AMD’s revenue still doesn’t include shipments of its Instinct MI308 chips to China, as it did last quarter, though the company expects revenue from them soon. “We have received some licenses for MI308,” noted Su at the time. “We’re still working with our customers on the demand environment and sort of what the overall opportunity is.”

Its operating income for the period was $1.3 billion, and net income was $1.2 billion, while gross margin came in at 52%. Its diluted earnings per share was $0.75.

“We delivered an outstanding quarter, with record revenue and profitability reflecting broad-based demand for our high-performance EPYC and Ryzen processors and Instinct AI accelerators,” said Su. This “marks a clear step up in our growth trajectory as our expanding compute franchise and rapidly scaling data center AI business drive significant revenue and earnings growth,” she added.

At the time, the semiconductor giant noted that customer momentum for its AI platforms is accelerating, as evident from its deepening partnerships with OpenAI, Oracle (ORCL -5.82%), Cisco (CSCO -2.45%), IBM (IBM -4.68%), and Cohere.

The U.S. Department of Energy also formed a $1 billion partnership with AMD to build two next-generation supercomputers that would “supercharge” advances in drug development, nuclear power, and national security technologies. The first one is called Lux and will be powered by MI355X AI chips and networking chips, making it the first U.S. AI factory supercomputer. The more advanced Discovery supercomputer will be based on the MI430 series of AI chips.

For the last quarter, AMD expects revenue of about $9.6 billion and a non-GAAP gross margin of 54.5%.

Last week, AMD’s manufacturing partner TSMC, which is the biggest contract chipmaker, also beat revenue estimates to report a 35% increase in its fourth-quarter profit. The company expects to boost capital spending this year, signaling confidence in the AI buildout.

“We expect our business to be supported by continued strong demand for our leading edge process technologies.”

– Wendell Huang, TSMC CFO

So, while trying to keep pace with Nvidia, AMD is pushing deeper into AI accelerators, edge-focused computing, and cost-efficient platforms, positioning itself as a compelling value-driven alternative in the evolving AI landscape.

Edge AI Is the Next Major Hardware Cycle

The AI landscape is evolving at a rapid pace, being embedded in everything from smartphones to wearables, displays, drones, robots, and autonomous vehicles. As AI models become more efficient and the technology’s usage moves from experimentation to deployment and scaling, the industry is shifting from the cloud to the edge to keep up with the AI boom.

While Nvidia continues to dominate data center training and hyperscale inference with high-end GPUs and ecosystem lock-in, the hardware cycle is now moving from centralized data centers to everyday devices, where value, efficiency, and price matter most. In this new era of device-level AI, AMD stands out for its strategic focus on local inference, embedded NPUs, and AI PC processors, making it a compelling value play in 2026.

Edge AI is still in its early stages, but its potential is vast. By embedding intelligence into every device, it can enable AI to operate everywhere, regardless of internet connectivity. And as everything becomes a computer, the opportunity for Edge AI could prove huge, even bigger than the cloud. But rather than replacing it, the future of AI is likely to be hybrid, with cloud platforms handling training and edge devices delivering real-time inference, marking the next major computing paradigm.

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