Beyond the Norm: How Non-Von Chips Outperform NVIDIA, Google, and Apple in AI

​by the Non-Von team
AI chip technology has become a competitive battleground as the demand grows for faster, more efficient, and scalable solutions. While companies like NVIDIA, Google, and Apple have led the charge, their legacy-laden technologies often fall short in critical areas. Non-Von is reshaping the field with a different kind of chip that meets today’s challenges head-on, setting a new benchmark in performance and sustainability.


The Industry ChallengeAI is pushing the limits of current hardware. Chips must handle complex algorithms, reduce power consumption, and operate efficiently in real-time environments like autonomous systems and defense applications. Current leaders are facing critical shortcomings. For example:

• NVIDIA: While NVIDIA’s AI chips are powerful and excel in large-scale cloud computing, their general-purpose GPUs lack the specialized optimization required for real-time, low-latency edge AI tasks​ (AIDevelopers, AIAccelerators).
  
  
  
• Google: Google’s TPUs are optimized for machine learning in the cloud and perform well in training environments. However, they are not designed for edge AI, as they rely heavily on centralized cloud processing, which introduces latency and limits their performance for real-time decision-making at the edge​ (AIDevelopers, Google Cloud Blog).
  
  
  
• Apple: Apple’s chips, like the A15 Bionic, are designed for consumer applications such as photo processing and mobile AI. While efficient for consumer-level tasks, they are not built for high-performance, industrial-scale AI applications, particularly those requiring real-time processing and continuous computation​ (Apple Machine Learning Research, AnandTech).
  
  
  

Non-Von is addressing these challenges with a chip that offers unmatched efficiency for real-time edge AI, handling demanding tasks while drastically reducing power consumption. Non-Von’s chip can make data center inferencing orders of magnitude more efficient while also enabling edge applications where energy efficiency and performance are paramount.



Head-to-Head: How Non-Von Outshines Its CompetitorsEnergy Efficiency: Doing More with LessWhen selecting the right AI chip, energy efficiency is a key consideration. Edge AI tasks often require a delicate balance between performance and power consumption, especially when working with autonomous systems or real-time decision-making. Here's how Non-Von stacks up against NVIDIA, Google, and Apple:
1. NVIDIA Jetson Orin Nano

• Power Consumption: The Jetson Orin Nano is a compact AI chip that delivers up to 40 TOPS (Tera Operations Per Second) of performance while consuming 7W to 15W.​ (CRN)
  
  The Jetson Orin Nano is designed for edge AI applications, offering a balance of performance and efficiency for real-time tasks like autonomous vehicles or robotics. While it’s efficient in comparison to more powerful NVIDIA chips, it still requires much more power than Non-Von for similar workloads.
  

2. Google Coral Dev Board (Edge TPU)

• Power Consumption: The Google Coral Dev Board, powered by the Edge TPU, operates in the range of 2-4W, optimized for AI inference at the edge​. (Extrapolate)
  

While efficient for tasks like image recognition and object detection in IoT devices, the Coral Dev Board consumes significantly more power than Non-Von’s chip, which requires just .25W for comparable workloads. This makes Non-Von’s chip 400-800% more energy-efficient, a critical advantage for demanding applications in real-time edge AI and power-sensitive environments.
3. Apple A15 Bionic Chip

• Power Consumption: The Apple A15 Bionic chip is designed for mobile devices, offering high energy efficiency in tasks like photo processing and real-time video analysis. Exact power consumption for AI tasks isn’t publicly disclosed.​ (RedSwitches, extrapolate.com)
  

Apple's chips are optimized for consumer-level AI applications, such as photo processing and mobile tasks, but they are not designed to handle the demands of industrial-grade AI or edge AI workloads. Although they offer energy efficiency, they lack the power and computational capacity needed for high-performance, real-time edge AI tasks that require continuous and intensive processing.
4. Non-Von

• Power Consumption: Non-Von’s chip is designed for maximum energy efficiency, consuming less than 1W at peak loads.
  

Non-Von’s chip and model compilerexcel in edge AI applications requiring both high performance and low power consumption. It is 400-800% (or greater) more efficient than leading competitors, making it ideal for energy-sensitive, real-time tasks like autonomous drones, wearable devices, and sensor-based systems.


Sustainability: Reducing Environmental ImpactNVIDIA:NVIDIA GPUs, especially those used in data centers, are known for their high power consumption, which can contribute to significant operational costs and increased natural resource demands, including water for cooling. Data centers, which house NVIDIA GPUs, already account for about 1-2% of global electricity consumption, and this consumption is expected to grow. Cooling systems, essential for maintaining optimal GPU performance, can consume up to 40% of a data center's total energy​ (Home of Energy News, NVIDIA Developer)
Google:Google's reliance on cloud infrastructure and TPUs also brings significant environmental costs. While the company has made strides toward sustainability, the energy consumption and water usage required to power and cool large-scale data centers remain high​. Google is working towards more sustainable practices, but its infrastructure still demands substantial energy for processing and cooling operations, impacting both cost and the environment. (Google Cloud Blog)
Apple:Apple focuses on reducing the environmental impact of its consumer devices, but its chips are not optimized for large-scale industrial AI applications or edge computing, where energy optimization is crucial. While Apple has implemented sustainability programs, its chips are not designed to meet the energy needs of high-performance AI tasks, like those used in autonomous systems or other real-time, edge AI applications​ (Tech Times, Sustainable Electronics Initiative)
Non-Von:Non-Von’s chips offer a more sustainable solution by being energy-efficient, consuming significantly less power than the industry leaders. By drastically reducing power usage and the need for cooling, Non-Von’s chips help minimize the carbon footprint for industries like defense, robotics, and autonomous systems. These advantages make Non-Von a more environmentally-friendly alternative in the field of AI hardware.

Flexibility: Ready for Today and TomorrowNVIDIA:NVIDIA’s GPUs are general-purpose chips designed for a wide range of applications, including AI and machine learning tasks. However, they often require custom integration for specialized use cases like defense or autonomous systems, where specific performance needs must be met (such as low latency or real-time processing). While highly versatile, NVIDIA’s chips are primarily designed for data-heavy cloud applications rather than optimized for low-power, real-time edge use. As a result, while they perform well across many domains, they do require significant custom integration for niche applications​ (NVIDIA Newsroom, NVIDIA)
Google:Google’s TPUs (Tensor Processing Units) are highly optimized for Google’s cloud infrastructure, excelling in tasks like machine learning and AI model training. However, TPUs are not as flexible as general-purpose GPUs and are not designed for use outside of Google’s infrastructure. This limits their effectiveness for a wide variety of real-world applications, especially when it comes to edge AI or situations that require low-latency, on-device processing. Their lack of flexibility makes them less adaptable compared to other chips that support a broader range of use cases​ (AI Stacked)
Apple:Apple’s chips, such as the A15 Bionic or the M1, are highly efficient in consumer applications but are specifically designed for Apple’s ecosystem. They are powerful for tasks like photo processing, mobile AI, and video streaming, but they are not optimized for industrial-scale AI tasks that require high-performance edge processing or specialized applications like autonomous vehicles. Their design is tailored for consumer devices and lacks the scalability and flexibility needed for demanding industrial applications​ (Profolus, AnandTech)
Non-Von:Non-Von’s chips are specifically built for parallel processing and AI model optimization, making them ideal for real-time, inference-specific applications like autonomous vehicles, wearable military tech, and sensor-based systems. They are optimized for sparse and unstructured models but flexible enough to provide efficiency gains for a wide variety of other types of currently popular models. This flexibility allows them to handle a wide variety of AI workloads, offering long-term scalability for future AI innovations. Unlike the general-purpose NVIDIA GPUs or cloud-based Google TPUs, Non-Von is focused on delivering high performance while maintaining energy efficiency and versatility, which makes it an ideal solution for demanding, energy-sensitive environments, both on the edge and in the data-center.

Real-World Use CasesNon-Von’s edge in energy efficiency and adaptability isn’t theoretical—its been tested in multipel scenarios and has applications across a range of use cases.  

• Defense: Enabling rapid signal processing in autonomous drones and wearable devices for military applications.
  
• Data Centers: Reducing energy and cooling costs by up to 40% compared to NVIDIA-powered systems.
  
• Autonomous Systems: Enabling expanded autonomy and previously infeasible intelligence capabilities in robots, land vehicles, and aerial drones.
  
  
  

What Sets Non-Von Apart?While NVIDIA, Google, and Apple have built their success on specific strengths, Non-Von’s design offers:

• Faster Processing: Achieving data center-level throughput in portable, low-power devices.
  
• Sustainable Innovation: Designed with energy reduction in mind, Non-Von chips are future-proofed for AI’s growing demands.
  
• User Friendly:  Non-Von’s ecosystem makes it much easier than competitors to integrate existing and new models through their custom SDK and advanced compiler.
  
• Expanded Models:  Sparse and unstructured models could create dramatic efficiencies, but today’s hardware does not enable those efficiencies to be realized.  Non-Von changes this by optimizing for sparse and unstructured models, while remaining flexible enough to also run most of today’s dense, structured, models.  
  
• Hardware/Software integration: At its core Non-Von is a software company that realized today’s hardware was not optimized for the efficiencies their engineers could gain in software.  It now sits at the intersection of hardware and software, co-developing both to gain the greatest performance improvements.
  

The Future of AI Starts HereNon-Von’s chip doesn’t just introduce competition—it brings a revolution. By addressing the shortcomings of legacy-ridden technologies from companies like NVIDIA, Google, and Apple, Non-Von is proving that AI hardware can be powerful, efficient, and sustainable. Whether for defense, data centers, or edge AI, Non-Von is ready to lead the next generation of AI innovation.


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Sources:
AI Stacked. "AI Chips vs GPUs: Understanding the Key Differences." AI Stacked, www.aistacked.com/ai-chips-vs-gpus-understanding-the-key-differences/.

AnandTech. "The Apple A15 SoC Performance Review: Faster & More Efficient." AnandTech, 4 Oct. 2021, www.anandtech.com/show/16983/the-apple-a15-soc-performance-review-faster-more-efficient.
Apple. "Neural Engine Transformers." Apple Machine Learning Research, machinelearning.apple.com/research/neural-engine-transformers.
CRN. "5 Cool Chip Solutions for Edge AI." CRN, www.crn.com/news/internet-of-things/5-cool-chip-solutions-for-edge-ai.

Energy Digital. "How AI Can Make Data Centres Sustainable." Energy Digital, energydigital.com/articles/nvidia-how-ai-can-make-data-centres-sustainable.


Extrapolate. "Top 10 Edge Computing Devices 2024." Extrapolate, www.extrapolate.com/blog/top-10-edge-computing-devices-2024.
Google. "What’s the Difference Between CPUs, GPUs, and TPUs?" Google Cloud Blog, 30 Oct. 2024, https://blog.google/technology/ai/difference-cpu-gpu-tpu-trillium/
Google. "Environmental Sustainability in Google's Data Centers." Google Cloud Blog, 2023, https://blog.google/outreach-initiatives/sustainability/our-commitment-to-climate-conscious-data-center-cooling/
Energy Digital. "How AI Can Make Data Centres Sustainable." Energy Digital, energydigital.com/articles/nvidia-how-ai-can-make-data-centres-sustainable.
NVIDIA Newsroom. "Nvidia Receives DARPA Contract Worth Up to $20 Million for High-Performance Embedded Processor Research." NVIDIA Newsroom,
www.nvidianews.nvidia.com/news/nvidia-receives-darpa-contract-worth-up-to-20-million-for-high-performance-embedded-processor-research-6622726.


NVIDIA. "Autonomous Machines | Embedded Systems." NVIDIA, www.nvidia.com/en-gb/autonomous-machines/embedded-systems/.


NVIDIA Blog. "AI Energy Efficiency." NVIDIA Blog, blogs.nvidia.com/blog/ai-energy-efficiency/

NVIDIA Developer. "Building an Edge Strategy: Cost Factors." NVIDIA Developer, developer.nvidia.com/blog/building-an-edge-strategy-cost-factors/.

RedSwitches. "AI Processors of 2024." RedSwitches, www.redswitches.com/blog/ai-processors-of-2024/.

Sustainable Electronics Initiative. "Apple Becomes First Member of Sustainable Semiconductor Technology Research Program." Sustainable Electronics Initiative, https://sustainable-electronics.istc.illinois.edu/2021/10/29/apple-joins-sust-semiconductor-prog/

Tech Times. "Apple Joins a New Program to Evaluate Environmental Impact of Chip Design and Manufacture." Tech Times, 28 Oct. 2021, https://www.techtimes.com/articles/267253/20211028/apple-joins-a-new-program-to-evaluate-environmental-impact-of-chip-design-and-manufacture-cutting-back-on-ecological-footprint.htm