AI and Deep Learning Accelerators Beyond GPUs in 2025

Why Non-GPU AI Accelerators Exist: A Comparison with GPUs

For years, Graphics Processing Units (GPUs) have been the workhorse of AI and deep learning, particularly for training complex models. Their parallel processing architecture, originally designed for rendering graphics, proved remarkably well-suited for the matrix multiplication and parallel computations inherent in neural networks. However, as AI workloads have grown in complexity and diversity, the limitations of GPUs in certain scenarios have become apparent, paving the way for the emergence of specialized non-GPU AI accelerators.

GPU Strengths and Weaknesses in AI

GPUs excel at a wide range of parallelizable tasks, making them highly versatile for various AI models and research. This flexibility is a significant advantage, allowing researchers and developers to experiment with different architectures without needing specialized hardware for each. NVIDIA, a dominant player in the GPU market, has fostered a mature ecosystem with CUDA, cuDNN, and extensive software libraries. This robust support simplifies development and deployment for many AI practitioners. GPUs are widely available, from consumer-grade cards to data center-specific models. For many general AI tasks, especially at smaller scales, GPUs can be a cost-effective solution.

“AI accelerators deliver faster and more energy-efficient results on specialized workloads, especially inference-heavy production environments.”

Liquid Web

While powerful, GPUs are general-purpose. This means they often have capabilities, such as graphics rendering pipelines and general-purpose floating-point units, that are not fully utilized in specific AI workloads, leading to inefficiencies in power consumption and cost. The general-purpose nature and high clock speeds of GPUs can lead to significant power consumption, especially in large-scale data centers. This is a critical concern for operational costs and environmental impact. While GPUs have high memory bandwidth, certain AI workloads, particularly inference, can be bottlenecked by memory access patterns and latency, which GPUs are not always optimized for. While GPUs have specialized units, they are not as finely tuned for specific AI operations as custom-designed accelerators. This can lead to less optimal performance for highly specialized tasks.

The Rise of Non-GPU AI Accelerators

Non-GPU AI accelerators, often referred to as Application-Specific Integrated Circuits (ASICs) or Field-Programmable Gate Arrays (FPGAs) designed for AI, emerged to address the limitations of GPUs by offering greater specialization and efficiency for AI workloads. Their existence is driven by the pursuit of higher energy efficiency. By designing hardware specifically for AI operations, these accelerators can achieve significantly better performance per watt, crucial for both data centers and edge devices. Non-GPU accelerators can be tailored to accelerate specific types of AI computations, such as integer arithmetic for inference and sparse matrix operations, leading to superior performance for those tasks. For real-time inference applications, specialized accelerators can offer lower latency due to their optimized data paths and reduced overhead compared to general-purpose GPUs. While the initial design and manufacturing costs of ASICs can be high, for high-volume deployment or highly specialized applications, they can offer a lower total cost of ownership due to their efficiency and performance. For edge devices and embedded systems, the ability to create compact, power-efficient accelerators is paramount.

Where Non-GPU Accelerators Beat GPUs (and vice versa)

Non-GPU accelerators generally excel in inference at scale. For deploying trained models in production, especially in data centers handling massive inference requests or on edge devices with strict power and latency constraints, specialized inference ASICs often outperform GPUs in terms of throughput, power efficiency, and cost per inference. Accelerators designed for particular neural network architectures or data types, such as low-precision inference, can achieve higher performance and efficiency than GPUs for those specific tasks. Their power efficiency and smaller form factors make them ideal for AI at the edge, in devices like smartphones, IoT sensors, and autonomous vehicles. Once the initial design costs are amortized, ASICs can be more cost-effective for mass production and deployment of specific AI solutions.

GPUs generally retain their advantage in model training and research. The flexibility of GPUs makes them indispensable for training new, evolving, and diverse AI models. Researchers can rapidly iterate on different architectures and algorithms without needing to redesign hardware. For developers working on a wide range of AI applications or those in the early stages of development, GPUs offer a more accessible and versatile platform. For many businesses and individuals, the existing GPU infrastructure and ecosystem make them a more practical and readily available choice. The programmability of GPUs allows for quicker prototyping and iteration of AI models and applications.

In essence, the landscape of AI acceleration is evolving towards a more heterogeneous approach. While GPUs remain crucial for research and general-purpose AI, specialized non-GPU accelerators are increasingly vital for efficient and scalable deployment of AI in specific, high-volume, or resource-constrained environments. The choice between them often comes down to the specific AI workload, deployment scale, power budget, and flexibility requirements.

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Deep Dive into Machine Learning and Deep Learning Accelerators

In the rapidly evolving landscape of AI hardware as of 2025, machine learning (ML) accelerators and deep learning (DL) accelerators represent critical advancements that address the computational demands of modern algorithms. While the terms are often used interchangeably, ML accelerators encompass a broader range of hardware optimized for various ML tasks, including supervised, unsupervised, and reinforcement learning. DL accelerators, on the other hand, are a specialized subset tailored to the intensive matrix operations and parallel processing required for neural networks, convolutional layers, and transformer models. Both categories have seen significant innovations to overcome the limitations of traditional GPUs, such as high energy consumption and latency in large-scale deployments.

Understanding Machine Learning Accelerators

Machine learning accelerators are dedicated hardware designed to expedite ML workflows, from data preprocessing to model training and inference. Unlike general-purpose CPUs or even GPUs, these accelerators prioritize efficiency in handling vast datasets and iterative computations. In 2025, trends show a shift toward heterogeneous architectures that combine ASICs (Application-Specific Integrated Circuits), FPGAs (Field-Programmable Gate Arrays), and NPUs (Neural Processing Units) to achieve higher throughput and lower power usage.

Key examples include Google's TPUs, which have evolved to the Trillium (v6) generation, offering 4.7x performance per chip compared to predecessors, and AWS Trainium2, which delivers 83.2 petaflops in ultra-servers for large-scale training. Intel's Habana Intel says Gaudi3 can outperform H100 on longer-output LLM inference in some cases (results vary by model and scenario), making it ideal for enterprise ML tasks like recommendation systems and natural language processing. Startups like Cerebras with the WSE-3 (wafer-scale engine featuring 900,000 cores and 4 trillion transistors) push boundaries by enabling training of trillion-parameter models in days, far surpassing traditional GPU clusters in speed and scalability.

The advantages of ML accelerators lie in their ability to optimize for specific workloads. For instance, FPGAs from companies like AMD offer reconfigurability, allowing developers to adapt hardware for custom ML pipelines, such as real-time anomaly detection in IoT devices. Energy efficiency has improved dramatically; benchmarks indicate annual gains of 40% in power savings, driven by advancements in memory bandwidth and interconnects. However, challenges remain, including high initial development costs for ASICs and the need for specialized software stacks to maximize performance.

Deep Learning Accelerators: Focus on Neural Network Optimization

Deep learning accelerators build on ML hardware but are fine-tuned for the complexities of DL, where models like transformers and CNNs (Convolutional Neural Networks) demand massive parallelization. In 2025, DL accelerators emphasize low-latency inference for real-time applications, such as autonomous vehicles and edge AI.

Prominent DL accelerators include Groq's LPUs (Language Processing Units), which achieve ~0.22 s TTFB and ~185 tok/s on public benchmarks, ~3-18× faster than other providers. SambaNova's RDUs (Reconfigurable Dataflow Units) set records with 129 tokens/second on 405B-parameter models, excelling in generative AI. Microsoft's Azure Maia 100, despite Maia 200's delay to 2026, offers competitive efficiency for LLMs in cloud environments. Edge-focused options like Hailo-8 provide 26 TOPS at just 2.5W, ideal for battery-powered devices in computer vision tasks.

Innovations in DL hardware also address memory bottlenecks; high-bandwidth memory (HBM) in chips like AMD's MI355X enables ~6 TBps bandwidth, 4x faster than predecessors. Wafer-scale designs (e.g., Cerebras WSE-3) minimize off-chip traffic. Tesla's Dojo instead tiles multiple D1 dies (~50B transistors each) into "training tiles". Benchmarks from sources like MLPerf highlight that DL accelerators can outperform GPUs by 300% in specialized tasks, though ecosystem maturity lags behind NVIDIA's CUDA.

Comparative Analysis: Latency, Power Efficiency, and Costs

To address queries on how AI hardware accelerators compare on latency, below key metrics are summarized based on 2025 benchmarks. These draw from workload-specific tests (e.g., inference on LLMs like Llama or GPT-3 variants), where results can vary by optimization and model size. Latency is measured in milliseconds for token generation or image processing; power efficiency in performance per watt (e.g., TOPS/W); and costs in approximate price per 1,000 inferences or hardware acquisition (cloud instances or on-prem).

Comparing AI accelerators based on latency for large language model (LLM) inference and example use cases: The NVIDIA H100, serving as the GPU baseline, has a latency of 10-50 ms for LLM inference and is suited for general training and inference. Google's TPU Trillium (v6) offers a latency of 5-20 ms for LLM inference, ideal for cloud-scale LLMs. AWS Inferentia2 provides a latency of 2-10 ms for LLM inference with 4x throughput compared to GPUs, making it excellent for high-volume recommendations.

Groq's LPU achieves a latency of less than 1 ms (sub-millisecond) for LLM inference, perfect for real-time NLP. Intel's Gaudi3 delivers a latency of 5-15 ms for LLM inference, being 50% faster than the H100, and is well-suited for vision models. Cerebras' WSE-3 has a latency of 1-5 ms for LLM inference, tailored for ultra-large models. Finally, Hailo-8 for edge applications offers a latency of 1-3 ms for LLM inference, commonly used in IoT and computer vision scenarios.

Comparing AI accelerators based on power efficiency in TOPS per watt and energy savings versus GPUs in percentage: The NVIDIA H100 has power efficiency of 5-10 TOPS/W and serves as the baseline for energy savings. Google's TPU Trillium offers 15-20 TOPS/W with 67% better performance per watt, resulting in 30-60% energy savings. AWS Trainium2 provides 10-15 TOPS/W and 40% energy savings. Groq's LPU achieves 20+ TOPS/W and 50-70% energy savings with one-tenth the energy usage. Intel's Gaudi3 delivers 12-18 TOPS/W and 40% energy savings. Cerebras' WSE-3 has 15-25 TOPS/W and 30-40% energy savings. Finally, Hailo-8 offers approximately 10 TOPS/W and over 50% energy savings specifically for edge applications.

Comparing AI accelerators based on cost in approximate dollars per 1,000 inferences and acquisition cost for cloud or on-prem setups: The NVIDIA H100 has a cost of 0.50-1.00 dollars per 1,000 inferences and an acquisition cost of over 30K dollars per unit. Google's TPU Trillium offers 0.30-0.70 dollars per 1,000 inferences, which is 30% lower, with GCP pricing of 1-5 dollars per hour. AWS Inferentia2 provides 0.20-0.50 dollars per 1,000 inferences, which is 70% lower, and EC2 pricing of 0.50-2 dollars per hour.

Groq's LPU achieves 0.10-0.30 dollars per 1,000 inferences with API usage-based acquisition costs. Intel's Gaudi3 delivers 0.25-0.60 dollars per 1,000 inferences with 10-40% savings and an acquisition cost of 10K-20K dollars per server. Cerebras' WSE-3 has 0.15-0.40 dollars per 1,000 inferences and custom clusters with high upfront acquisition costs. Finally, Hailo-8 offers 0.05-0.20 dollars per 1,000 inferences and an acquisition cost of 50-200 dollars per module.

These comparisons reveal that non-GPU accelerators often excel in efficiency and cost for inference-heavy workloads, with up to 10x latency reductions and 60% energy savings. For training, GPUs retain an edge in versatility, but hybrids are emerging.

In summary, as ML and DL models scale to trillions of parameters, accelerators are pivotal for sustainable AI. By 2030, projections suggest costs declining 30% annually while efficiency improves 40%, democratizing access. Organizations should evaluate based on workload specificity to leverage these technologies effectively.

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Types and Categories of Non-GPU AI Accelerators

Non-GPU AI accelerators can be broadly categorized based on their architectural design and programmability. The primary types include Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), and Neural Processing Units (NPUs).

Application-Specific Integrated Circuits (ASICs)

ASICs are custom-designed chips engineered from the ground up to perform specific tasks with maximum efficiency. In the context of AI, this means designing the silicon to directly implement the mathematical operations common in neural networks, such as matrix multiplication and convolution, with highly optimized data paths and memory access. This specialization allows ASICs to achieve superior performance per watt and lower latency compared to more general-purpose processors.

ASICs have high performance, optimized for specific AI workloads, leading to very high throughput. They offer energy efficiency with minimal power consumption due to highly specialized and efficient design. They provide low latency through direct implementation of AI operations, reducing processing delays. Once fabricated, their functionality is fixed, making them less adaptable to new AI models or algorithmic changes. The initial design and manufacturing of ASICs are expensive and time-consuming, making them suitable for high-volume production or specific, stable workloads.

ASICs are ideal for data center mass inference, where large-scale inference deployments need to process billions of requests with low latency and high energy efficiency, such as in search engine ranking, recommendation systems, and large language model inference. Their power efficiency and compact size make them perfect for embedded systems, IoT devices, autonomous vehicles, and smartphones where real-time AI processing is critical, for example in object detection in security cameras and voice assistants. They are increasingly found in smartphones and other consumer electronics to accelerate on-device AI features like facial recognition, computational photography, and natural language processing.

Field-Programmable Gate Arrays (FPGAs)

FPGAs are reconfigurable integrated circuits that allow users to customize their hardware logic after manufacturing. Unlike ASICs, which have fixed functions, FPGAs can be reprogrammed to implement different digital circuits. This flexibility makes them a middle ground between the general-purpose nature of GPUs and the fixed specialization of ASICs.

FPGAs offer flexibility and reconfigurability, as they can be reprogrammed to adapt to evolving AI models, new algorithms, or different precision requirements. They provide good performance and efficiency. While not as efficient as ASICs for a specific task, they offer better performance and energy efficiency than GPUs for certain AI workloads, especially when custom data paths can be leveraged. Development is typically faster and less expensive than ASIC design, as it involves programming existing hardware rather than fabricating new chips. The reconfigurable nature can introduce some overhead compared to purpose-built ASICs, leading to higher latency.

FPGAs are used in data centers for inference workloads that require some flexibility or for specialized training tasks where custom data flows can provide an advantage. Their reconfigurability makes them suitable for edge devices where models might need to be updated frequently or where specific, custom AI tasks are performed, such as in industrial automation and robotics. They are often used to prototype and validate AI accelerator designs before committing to expensive ASIC fabrication.

Neural Processing Units (NPUs)

NPUs are a category of specialized processors designed to accelerate neural network operations. While often implemented as ASICs, the term NPU specifically highlights their focus on neural network computations. They are typically found integrated into System-on-Chips (SoCs) for mobile and edge devices.

NPUs are optimized for neural networks, specifically designed to handle the mathematical operations, such as dot products and convolutions, and data types, like low-precision integers, prevalent in neural networks. They offer high energy efficiency, crucial for battery-powered devices, delivering significant AI performance within strict power budgets. They are often integrated directly into mobile processors, providing on-device AI capabilities without relying on cloud connectivity.

NPUs power on-device AI features in consumer devices like smartphones and tablets, such as real-time language translation, advanced camera features, and personalized user experiences. They enable AI capabilities in edge devices like drones, smart home devices, and wearables where low power and local processing are essential.

This categorization highlights the diverse approaches to accelerating AI beyond GPUs, each with its own set of trade-offs regarding performance, efficiency, flexibility, and cost. The choice of accelerator depends heavily on the specific requirements of the AI application, from the massive scale of data center training and inference to the constrained environments of edge and consumer devices.

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Major Non-GPU AI Accelerators and Their Use Cases

The landscape of non-GPU AI accelerators is dynamic, with hyperscalers, established tech companies, and innovative startups all contributing to a diverse array of hardware solutions. These accelerators are designed with specific use cases in mind, ranging from massive data center training to power-efficient edge inference.

Hyperscaler Custom Chips

Major cloud providers are increasingly designing their own custom AI chips to optimize performance and cost for their specific workloads and to reduce reliance on external vendors.

Google's TPUs are ASICs specifically designed for neural network workloads. They were initially developed for internal use to power Google's AI services like Google Search, Google Photos, and Google Translate. TPUs are optimized for large-scale matrix multiplications, which are fundamental to deep learning. Google has released several generations of TPUs, such as TPU v2, v3, v4, v5e, v5p, Trillium (sixth-generation TPU, also called TPU v6), and Ironwood (seventh-generation TPU).

“Google Cloud TPUs are custom-designed AI accelerators, which are optimized for training and inference of AI models. They are ideal for a variety of use cases, such as agents, code generation, media content generation, synthetic speech, vision services, recommendation engines, and personalization models, among others. TPUs power Gemini, and all of Google's AI powered applications like Search, Photos, and Maps, all serving over 1 Billion users.”

Google Cloud TPU documentation

Each generation offers significant improvements in performance and efficiency. TPU v4, for instance, is designed for both training and inference, offering a balance of performance and power efficiency. TPU v5e is optimized for cost-efficient inference and smaller training runs, while TPU v5p is Google's most powerful TPU for large-scale training of foundation models. Trillium offers up to 4.7x more performance per chip than v5p in certain metrics, with 67% better performance per watt. Ironwood is designed for inference, offering 5x more peak compute capacity and 6x the high-bandwidth memory. They are primarily offered through Google Cloud Platform (GCP).

They are extensively used for training large language models (LLMs), image recognition, and other deep learning tasks that require massive computational power. Google also offers Edge TPUs, such as Coral devices, for on-device inference in embedded systems. TPUs often outperform GPUs for specific deep learning workloads, especially those that align with their optimized matrix multiplication capabilities. For example, for large-scale training of certain models, TPUs can offer better cost-performance and energy efficiency than equivalent GPU clusters. However, their specialized nature means they are less flexible than GPUs for general-purpose computing or for models that don't fit their architecture well.

Amazon Web Services (AWS) has developed its own custom AI chips: Inferentia for inference and Trainium for training. These ASICs are designed to provide high performance and cost-effectiveness for machine learning workloads running on AWS. Inferentia is optimized for high-throughput, low-latency inference. Inferentia chips are designed to handle the deployment of trained models at scale, making them suitable for applications like natural language processing, computer vision, and recommendation engines.

Trainium is built for high-performance deep learning training. Trainium aims to provide a cost-effective and efficient solution for training complex models, including large language models and generative AI models. Trn2 UltraServers connect 64 Trainium2 chips for ultra-large models, delivering up to 83.2 petaflops FP8 and enhanced sustainability. They are available as EC2 instances on AWS.

Inferentia is ideal for deploying AI applications in production where cost and latency are critical. Trainium is geared towards customers who need to train large, computationally intensive models within the AWS ecosystem. AWS positions Trainium and Inferentia as alternatives to GPUs for specific AI workloads, offering potentially better price-performance and energy efficiency within the AWS cloud environment. They are designed to be highly optimized for the types of models commonly deployed and trained on AWS.

Microsoft has recently unveiled its custom AI accelerator, Azure Maia 100, an ASIC designed to power AI workloads in its Azure data centers. Maia is specifically built to handle the demands of large language models and other generative AI applications. Maia accelerators are intended for internal Microsoft services and for Azure customers who need to train and run large, complex AI models. This move signifies Microsoft's commitment to optimizing its cloud infrastructure for the growing demands of AI.

While specific public performance benchmarks are still emerging, Maia is designed to offer competitive performance and energy efficiency for large-scale AI workloads within the Azure cloud, aiming to reduce the reliance on third-party GPUs for these critical tasks. The next generation, Maia 200, hexpected in 2026.

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Other Major Company Accelerators

Beyond the hyperscalers, other established technology companies are also developing their own non-GPU AI accelerators.

Intel acquired Habana Labs, a company specializing in AI processors. Their flagship products are Gaudi for training and Goya for inference. Gaudi accelerators are designed for deep learning training, emphasizing high throughput and scalability. The Gaudi series, including Gaudi2 and the recently announced Gaudi3, focuses on providing competitive performance for training large AI models. Intel highlights Gaudi's integrated RoCE (RDMA over Converged Ethernet) ports for efficient scaling across many accelerators.

Gaudi accelerators are available for purchase and can be deployed in on-premise data centers or accessed through cloud providers that offer Gaudi instances. They are targeted at enterprises and research institutions involved in large-scale AI model training. Intel positions Gaudi as a strong competitor to NVIDIA's GPUs for deep learning training, often highlighting its price-performance advantages and scalability features.

Startup and Specialized Accelerators

Several innovative startups are pushing the boundaries of AI acceleration with novel architectures.

Graphcore's IPUs are designed from the ground up for machine intelligence workloads. Their architecture emphasizes fine-grained parallelism and in-processor memory, aiming to keep more of the model and data on-chip to reduce memory bottlenecks. Graphcore was acquired by SoftBank in July 2024. IPUs are used for both training and inference, particularly in areas like natural language processing, computer vision, and scientific computing. They are available for on-premise deployment and through cloud partners.

Graphcore claims significant performance advantages over GPUs for certain AI workloads, especially those that benefit from their unique memory architecture and fine-grained parallelism. They aim to offer a more efficient and scalable solution for specific AI model types.

Cerebras Systems has taken a radical approach to AI acceleration by building the largest chip ever made, the Wafer-Scale Engine. This single chip contains billions of transistors and hundreds of thousands of AI-optimized cores, designed to accelerate deep learning training by eliminating the communication bottlenecks inherent in multi-chip GPU systems.

Cerebras has released multiple generations of the WSE, with each iteration increasing the number of cores and memory. The WSE-2, for example, boasts 2.6 trillion transistors and 850,000 AI-optimized cores. The WSE-3 further pushes these boundaries. The WSE is primarily targeted at accelerating the training of extremely large AI models, such as large language models, in data centers and supercomputing facilities. Its unique architecture allows entire models to reside on a single chip, simplifying programming and accelerating training times.

Cerebras positions the WSE as a solution that can train models orders of magnitude faster than traditional GPU clusters for certain large-scale problems, by overcoming the limitations of inter-chip communication. Cerebras serves AI models using their accelerators and the models can be accessed via API.

SambaNova Systems develops Reconfigurable Dataflow Units (RDUs), including their SN40L chips, designed for both training and inference of large models using a dataflow architecture for high efficiency. SambaNova's RDUs are used for enterprise AI applications, including training and inference of large language models.

SambaNova claims superior performance in benchmarks for inference tasks compared to GPUs, particularly for large open-source models. They aim to provide a full-stack platform that outperforms traditional hardware in speed and efficiency for specific AI workloads. SambaNova offers the possibility to access the models they serve via API through SambaCloud. Recent additions include DeepSeek-V3.1 on SambaCloud in August 2025.

“The LPU Inference Engine makes it easy to conduct research, as well as test and deploy new generative AI applications and other AI workloads because it delivers 10x the speed while consuming just 1/10th the energy of comparable systems using GPUs for inference.”

Groq

Groq develops Language Processing Units (LPUs), optimized for AI inference with their Tensor Streaming Processor chips focusing on speed and low latency. Groq's LPUs are used for real-time inference in applications like chatbots, code generation, and natural language processing. Groq claims significantly faster inference speeds than GPUs, such as up to 18 times faster for certain models like Llama. They position their hardware as enabling exceptional compute speed and energy efficiency for inference tasks. Groq offers the possibility to access the models they serve via API.

This review highlights that the non-GPU AI accelerator market is characterized by diverse architectural approaches, each aiming to provide superior performance and efficiency for specific AI workloads and deployment scenarios. The competition is driving innovation, leading to specialized hardware that complements and, in some cases, surpasses GPUs for the demands of modern AI.

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Performance Comparison with GPUs and Use Cases

Comparing the performance of non-GPU AI accelerators with GPUs is complex, as it often depends on the specific workload, model architecture, and optimization efforts. However, general trends and claims from vendors and independent benchmarks provide insights into their relative strengths.

Google TPUs

Google TPUs, particularly the v4 and v5p generations, are highly optimized for training large-scale neural networks, especially those with dense matrix operations. Google often highlights their performance-per-dollar and performance-per-watt advantages over GPUs for specific large-scale training tasks, such as training large language models.

For instance, for certain models, TPUs can offer significantly faster training times and lower costs compared to equivalent GPU clusters, due to their specialized architecture and high-bandwidth interconnects. Trillium offers a 4.7x performance boost over v5p. TPU v5e and earlier generations are also used for inference. For large-scale inference, especially for models that fit well within the TPU's architecture, they can offer superior throughput and energy efficiency compared to GPUs. Ironwood focuses on inference with sub-ms latency and lowest cost per token.

Google's internal use of TPUs for powering its search and AI services demonstrates their capability for massive inference workloads. They are primarily used in Google Cloud for large-scale AI model training, such as LLMs and image recognition, and high-volume inference serving. Edge TPUs are deployed in devices for on-device AI.

AWS Trainium and Inferentia

AWS Trainium is designed to offer a cost-effective and high-performance alternative to GPUs for deep learning training within the AWS cloud. AWS claims that Trainium-based instances, such as Trn2, can offer 30-40% better price-performance than current-generation GPU-based EC2 instances, like P5e, for certain training workloads. Benchmarks often show Trainium achieving comparable accuracy to GPU-based training at a lower cost.

AWS Inferentia is optimized for high-throughput, low-latency inference. AWS reports that Inferentia2 instances can deliver significant cost savings, up to 70% lower cost per inference, and higher throughput, such as 12x higher throughput for PyTorch NLP applications, compared to GPU instances like NVIDIA T4 or A10G, while also achieving lower latency. This makes them highly attractive for deploying large-scale AI applications in production.

Both are exclusively available on AWS. Trainium is for training large, complex models, while Inferentia is for cost-effective, high-volume inference serving for applications like natural language processing, computer vision, and recommendation systems.

Intel Habana Gaudi

Intel's Gaudi accelerators are positioned as strong competitors to NVIDIA's GPUs for deep learning training. Habana claims that Gaudi2 can offer about twice the throughput of NVIDIA A100 80GB for both training and inference in certain benchmarks. While some reports indicate Gaudi2 might perform at around 55% of an NVIDIA H100, Intel emphasizes Gaudi3's competitive performance, claiming it can offer 10% higher performance per unit cost compared to NVIDIA H100 for inference, and in some cases, up to 2.5 times better.

The integrated RoCE ports are a key feature for efficient scaling. Gaudi accelerators also support inference workloads. While their primary focus has been on training, their architecture allows for efficient inference, especially for models that benefit from their specialized matrix multiplication capabilities. Gaudi accelerators are available for on-premise deployment and through cloud providers. They target enterprises and research institutions for large-scale AI model training and inference.

Graphcore Intelligence Processing Unit (IPU)

Graphcore IPUs are designed with a unique architecture that emphasizes in-processor memory and fine-grained parallelism to reduce memory bottlenecks. Graphcore has claimed significant speedups, such as 3-4x faster than state-of-the-art GPUs for certain Graph Neural Networks, and higher throughput, such as 4.6x higher throughput than NVIDIA A100 for ResNet-50 inference, for specific AI workloads.

They aim to keep entire models and data on-chip, which can lead to better performance for models with irregular data access patterns or smaller batch sizes. IPUs are used for both training and inference, particularly in areas like natural language processing, computer vision, and scientific computing, where their unique memory architecture can provide advantages. They are available for on-premise deployment and through cloud partners.

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Cerebras Wafer-Scale Engine (WSE)

The Cerebras WSE is designed to accelerate the training of extremely large AI models by using a single, massive chip that eliminates inter-chip communication bottlenecks. Cerebras claims that the WSE-3 can outperform racks of GPUs for training large language models. For instance, they report training Llama3.1 20x faster than GPU or solutions at 1/3 the power of DGX systems for inference, and significant advantages in training large BERT models.

Their approach is particularly beneficial for models that are too large to fit on a single GPU or even multiple GPUs, as it simplifies programming and can drastically reduce training times. While primarily focused on training, Cerebras has also introduced inference capabilities for the WSE, claiming significant speedups and power efficiency for large model inference, more than 30 times faster than closed models like ChatGPT or Anthropic.

The WSE is primarily targeted at accelerating the training of very large AI models in data centers and supercomputing facilities. It is ideal for organizations working with models that push the boundaries of current GPU capabilities.

SambaNova Reconfigurable Dataflow Units (RDUs)

SambaNova's RDUs are optimized for both training and inference, with claims of outperforming GPUs in speed for large models like Llama 3.1 405B, achieving world-record inference speeds. For training, they provide efficient handling of complex models due to their dataflow architecture. For inference, SambaNova reports 4x faster performance on large models compared to competitors. Their systems are used in enterprise settings for AI workloads requiring high throughput and efficiency. SambaNova targets organizations needing fast, scalable AI processing beyond GPU capabilities.

“SambaNova Cloud is the fastest Application Programming Interface (API) service for developers. We deliver world record speed and in full 16-bit precision - all enabled by the world's fastest AI chip.”

Rodrigo Liang, CEO of SambaNova

Groq Language Processing Units (LPUs)

Groq's LPUs focus on inference, delivering high speeds such as 2600 tokens per second for models like Llama, often 18x faster than GPU-based systems. They excel in real-time AI tasks with low latency. Groq's inference engine is used for applications requiring quick responses, like autonomous AI and summarization. Their hardware outperforms GPUs in energy efficiency and speed for language model inference. Groq targets developers and enterprises needing rapid, cost for-effective inference at scale.

Summary of Performance Trends

For large-scale, specialized training of models like LLMs, custom ASICs, such as TPUs, Trainium, Maia, and WSE, often claim superior price-performance and energy efficiency compared to general-purpose GPUs. This is due to their architectural optimizations for specific AI operations and reduced communication overhead.

For high-volume, low-latency inference ASICs, such as Inferentia, some TPUs, and potentially future WSE inference solutions, consistently demonstrate better throughput, lower latency, and significantly better cost-per-inference and power efficiency than GPUs. This is crucial for deploying AI at scale in production environments.

GPUs remain the most flexible and versatile for general AI development, research, and smaller-scale deployments. Non-GPU accelerators, while offering superior performance and efficiency for their intended workloads, often come with a trade-off in flexibility and ecosystem maturity. The choice depends on whether the priority is broad applicability or highly optimized performance for a specific, stable workload.

Cloud offerings of these non-GPU accelerators allow businesses and researchers to access cutting-edge hardware without the significant upfront investment of purchasing and maintaining specialized systems. This democratizes access to models powerful AI compute, enabling the development and deployment of larger and more complex AI models.

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Cloud Offerings and Practical Access Methods

The most practical way for many individuals and organizations to access non-GPU accelerators is through cloud computing platforms. This approach eliminates the need for significant upfront hardware investment and maintenance, democratizing access to cutting-edge AI compute.

Hyperscaler Cloud Offerings

Google Cloud TPUs are primarily accessible through Google Cloud Platform (GCP). Users can provision Cloud TPU VMs or Pods, which come pre-configured with the necessary software stack, including TensorFlow, PyTorch, and JAX. Trillium is available in Cloud TPU VMs/Pods. Google also offers the "TPU Research Cloud" program, providing free access to TPUs for academic researchers.

Google Colab, a free Jupyter notebook environment, offers limited free access to Cloud TPUs, making it an excellent starting point for hobbyists and developers to experiment with TPU-accelerated models without any setup.

For more serious development design, GCP provides various pricing models, including on-demand and committed use discounts. The TPU Research Cloud provides substantial compute grants for academic research, enabling researchers to train large-scale models that would otherwise be cost-prohibitive. Small businesses can leverage GCP's flexible pricing and scalable infrastructure to access TPUs for their AI workloads, paying only for the compute they consume.

AWS Trainium and Inferentia instances are available through Amazon EC2. Users can launch instances with these custom chips and integrate them into their existing AWS workflows. AWS provides optimized Deep Learning AMIs (Amazon Machine Images) and SDKs to simplify development. While not as readily available for free as Google Colab, developers can start with smaller instances and leverage AWS Free Tier for other services.

AWS also provides extensive documentation and tutorials for getting started with Trainium and Inferentia. AWS offers a wide range of instance types and pricing options, allowing researchers and small businesses to scale their AI workloads as needed. The integration with other AWS services, such as S3 for data storage and SageMaker for ML operations, provides a comprehensive platform.

Azure Maia accelerators are integrated into Microsoft Azure's infrastructure. Access will primarily be through Azure services, likely as specialized VM instances or through Azure Machine Learning. Details on public access and pricing are still emerging as Microsoft rolls out these chips. As with other hyperscaler custom chips, access will be managed through the Azure platform, providing a seamless experience for users already within the Azure ecosystem.

Other Cloud and On-Premise Access

Gaudi accelerators are available for purchase for on-premise deployment. They are also offered through cloud providers like Intel Tiber AI Cloud, formerly Intel Developer Cloud, and IBM Cloud. This provides flexibility for users who prefer to own their hardware or leverage cloud infrastructure. Intel provides comprehensive software stacks, including optimized TensorFlow and PyTorch integrations, to facilitate development on Gaudi. The Intel Tiber AI Cloud offers a platform for developers to experiment and build solutions on Gaudi hardware.

Graphcore IPUs are available for on-premise deployment and through cloud partners like Gcore Cloud and Paperspace. These cloud offerings provide access to IPU-powered systems, often with pre-configured environments. Graphcore provides a robust software stack, Poplar SDK, which includes compilers, libraries, and tools for developing and deploying AI models on IPUs. Cloud access allows researchers and developers to experiment with IPUs without direct hardware investment.

Cerebras systems, powered by the WSE, are typically deployed in large data centers and supercomputing facilities. Access is often through direct engagements with Cerebras or through partnerships with cloud providers and research institutions. Cerebras also offers Cerebras Cloud for inference. Due to the scale and specialization of the WSE, its primary users are large research organizations, government labs, and enterprises working on extremely large AI models that require unprecedented compute power.

“With over 4 trillion transistors - 57x more than the largest GPU - the CS-3 is 2x faster than its predecessor and sets records in training large language and multi-modal models.”

Cerebras Systems

SambaNova RDUs are available for on-premise deployment and through SambaCloud, which provides API access for model inference. SambaNova offers software tools and SDKs to develop and deploy models on their hardware. Cloud access enables users to utilize their accelerators without hardware ownership, focusing on API-based model serving.

Groq LPUs are accessible via Groq Cloud and their API console, offering on-demand inference with pricing for tokens-as-a-service. Groq provides API references and SDKs for integration, allowing developers to access models without managing hardware.

How Hobbyists, Software Developers, Researchers, and Small Businesses Can Use Non-GPU AI Accelerators

The most accessible way to learn about and experiment with non-GPU accelerators is through free tiers or low-cost options on cloud platforms like Google Colab for TPUs or by leveraging free credits offered by cloud providers. These platforms often provide pre-configured environments and tutorials.

Many online courses and tutorials are emerging that specifically cover how to use these accelerators for AI development. Look for resources that focus on the software frameworks and SDKs provided by the accelerator vendors, such as TensorFlow with TPUs and Poplar SDK for IPUs. Engage with open-source projects that are optimized for these accelerators. This can provide practical experience and insights into their programming models.

For software developers and small businesses, building applications on cloud platforms that offer these accelerators is the most straightforward path. This allows them to focus on application development rather than infrastructure management. Ensure that the AI frameworks and libraries you use, such as TensorFlow and PyTorch, are compatible and optimized for the chosen accelerator. Vendors typically provide their own optimized versions or plugins.

For optimal performance, it's often necessary to optimize AI models for the specific architecture of the non-GPU accelerator. This might involve techniques like quantization, using lower precision data types, or architectural adjustments. For hobbyists and small businesses developing edge AI applications, consider using smaller, more accessible non-GPU accelerators like Google's Coral Edge TPUs or other embedded NPUs. These devices are designed for low-power, real-time inference.

Academic researchers can apply for programs like Google's TPU Research Cloud or similar initiatives from other vendors to gain access to significant compute resources. Collaborate with institutions or companies that have access to these specialized accelerators. This can provide opportunities to run large-scale experiments. Researchers can contribute to the field by developing new algorithms and optimization techniques that fully leverage the unique architectures of non-GPU accelerators.

In summary, while direct ownership of some of these advanced accelerators might be out of reach for many, cloud platforms and dedicated research programs are making them increasingly accessible. This accessibility is crucial for fostering innovation and enabling a broader range of users to benefit from the performance and efficiency gains offered by non-GPU AI acceleration.

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Conclusion and Recommendations

The landscape of AI and deep learning acceleration is undergoing a significant transformation, moving beyond the sole reliance on GPUs towards a more diverse and specialized ecosystem. Non-GPU AI accelerators, including ASICs like Google TPUs (including Trillium and Ironwood), AWS Trainium/Inferentia, Intel Habana Gaudi, and Cerebras WSE, as well as reconfigurable FPGAs, are carving out crucial niches by offering superior performance, energy efficiency, and cost-effectiveness for specific AI workloads.

Conclusion

While GPUs will undoubtedly remain a cornerstone of AI development and research due to their versatility and mature ecosystem, it is clear that they are not the optimal solution for every AI task. The emergence and increasing adoption of non-GPU accelerators are driven by fundamental economic and engineering realities.

As AI models become larger and more complex, and as their deployment scales to billions of users and devices, the general-purpose nature of GPUs can lead to inefficiencies. Custom-designed hardware, while expensive to develop initially, can offer significant long-term savings in operational costs, such as power and cooling, and deliver higher throughput for specific, high-volume workloads.

The proliferation of AI into edge devices, like smartphones, IoT, and autonomous systems, demands extreme power efficiency and compact form factors. GPUs, with their higher power consumption and larger footprints, are often unsuitable for these environments. Specialized NPUs and ASICs are essential for enabling on-device AI.

Different AI models have different computational patterns. While GPUs are excellent for dense matrix operations, some models, such as sparse networks and certain recurrent neural networks, might benefit more from architectures that optimize for different data flows or memory access patterns, which specialized accelerators can provide.

Hyperscalers are investing heavily in custom silicon to optimize their own infrastructure and offer differentiated services. This allows them to control their supply chain, reduce costs, and tailor hardware precisely to the demands of their massive AI workloads. Note delays in next-gen chips, such as Microsoft Maia 200 to 2026.

It is important to note that the AI accelerator market is still relatively young and rapidly evolving. Performance claims can be highly dependent on specific benchmarks, software optimizations, and the exact model being run. Direct, apples-to-apples comparisons are often challenging due to differences in architecture, software stacks, and availability.

Recommendations

For various stakeholders in the AI ecosystem, here are realistic recommendations.

For AI and deep learning hobbyists and software developers, start with cloud free tiers. Leverage free access programs like Google Colab for TPUs or explore free tiers/credits from AWS and other cloud providers to experiment with non-GPU accelerators. This is the lowest-cost way to gain hands-on experience.

Learn to work with popular AI frameworks, such as TensorFlow and PyTorch, and understand how they interface with different accelerators. The underlying hardware might change, but the software interfaces often remain consistent. For those interested in practical applications, explore development kits with integrated NPUs, such as Google Coral and Raspberry Pi with AI accelerators, for building edge AI solutions. The field is moving fast. Follow industry news, attend webinars, and engage with developer communities to stay informed about new hardware and software developments.

For researchers, actively apply for research programs offered by cloud providers and hardware vendors, such as Google TPU Research Cloud, to gain access to significant compute resources for large-scale experiments. When conducting research, carefully benchmark your models on different accelerator types to understand their performance characteristics and identify optimal hardware for your specific research questions. Consider how the unique architectural features of non-GPU accelerators, such as in-processor memory and wafer-scale integration, can enable new types of AI models or computational approaches.

For small businesses, a cloud-first strategy is the most sensible. Utilize the specialized instances offered by hyperscalers, such as TPUs on GCP, Trainium/Inferentia on AWS, and Maia on Azure, for training and inference, paying only for what you use. Conduct thorough cost-performance analyses for your specific AI workloads. While GPUs might be familiar, a specialized non-GPU accelerator could offer significant cost savings and performance gains for your production inference or large-scale training needs.

Be mindful of potential vendor lock-in when committing to highly specialized hardware. While the performance benefits can be substantial, ensure your software stack allows for some portability if needed. Ultimately, the choice of accelerator should be driven by business value. Does it enable new products, reduce operational costs, or improve customer experience? The technology is a means to an end.

“Nvidia's AI accelerators have between 70% and 95% of the market share for artificial intelligence chips. But there's more competition than ever as startups, cloud companies and other chipmakers ramp up development.”

CNBC analysis of NVIDIA's market position and competition

In conclusion, the future of AI acceleration is heterogeneous. No single piece of hardware will dominate all AI workloads. Instead, a diverse ecosystem of GPUs, ASICs, and FPGAs will co-exist, each optimized for different stages of the AI lifecycle and different deployment environments. Understanding these distinctions and strategically choosing the right accelerator for the right task will be paramount for unlocking the full potential of artificial intelligence.

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FAQ: AI Accelerators and Alternatives to NVIDIA GPUs

• What are non-GPU AI accelerators, and why do they exist?

  Non-GPU AI accelerators are specialized hardware like ASICs, FPGAs, and NPUs designed for AI workloads. They address GPU limitations in energy efficiency, latency, and optimization for specific tasks, such as inference at scale or edge computing, by tailoring architecture to neural network operations.

• How do GPUs compare to non-GPU accelerators in strengths and weaknesses?

  GPUs offer versatility for parallel tasks, a mature ecosystem (e.g., CUDA), and cost-effectiveness for general use, but they consume more power and may underperform in specialized inference due to over-provisioned features. Non-GPU options provide better performance per watt and lower latency for targeted workloads, though with less flexibility.

• What are the main types of non-GPU AI accelerators?

  The primary types are ASICs (custom, high-efficiency for fixed tasks), FPGAs (reconfigurable for prototyping and evolving models), and NPUs (optimized for on-device neural operations in mobile/edge devices). Each balances performance, cost, and adaptability differently.

• Can you name some major non-GPU AI accelerators and their focus areas?

  Examples include Google's TPUs (matrix multiplications for training/inference, with Trillium and Ironwood generations), AWS Trainium (training) and Inferentia (inference), Microsoft Azure Maia (large models), Intel Habana Gaudi (scalable training), Graphcore IPUs (fine-grained parallelism, acquired by SoftBank), Cerebras WSE (wafer-scale for massive models), SambaNova RDUs (dataflow for enterprise AI), and Groq LPUs (low-latency inference).

• In what scenarios do non-GPU accelerators outperform GPUs?

  They excel in large-scale inference (e.g., better throughput and cost per inference via Inferentia), edge computing (power efficiency for devices like smartphones), and specialized workloads (e.g., sparse operations). GPUs remain preferable for flexible training and rapid prototyping.

• How do performance claims vary between training and inference?

  For training, accelerators like TPUs and WSE claim superior price-performance (e.g., 4.7x boost for Trillium) due to reduced overhead. For inference, options like Groq LPUs offer up to 18x faster speeds and lower latency, though results depend on benchmarks and model fit.

• How can users access these accelerators without buying hardware?

  Through cloud platforms: Google Cloud for TPUs (including free Colab tiers), AWS EC2 for Trainium/Inferentia, Azure for Maia, and specialized clouds like SambaCloud or GroqCloud for APIs. Research programs like TPU Research Cloud provide free grants for academics.

• What trade-offs should be considered when choosing an accelerator?

  Non-GPU types offer efficiency for stable, high-volume tasks but may lack GPU flexibility and ecosystem maturity. Initial costs for ASICs are high, while FPGAs suit iteration. Selection depends on workload scale, power budget, and deployment environment.

• Is the AI hardware market shifting away from GPUs?

  No, it's evolving toward heterogeneity: GPUs handle general research, while non-GPU accelerators fill niches for deployment. Hyperscalers invest in custom silicon to optimize costs, but direct comparisons remain challenging due to varying architectures.

• Do AI accelerators outperform NVIDIA GPUs for inference in 2025?

  Yes, several non-GPU AI accelerators outperform NVIDIA GPUs in inference for specific metrics like latency, power efficiency, and cost in 2025. For example, Groq's LPUs offer up to 18x faster inference on models like Llama with 1/10th the energy, while SambaNova's RDUs achieve 4x speed on large generative models. However, NVIDIA GPUs (e.g., H100) remain superior for general-purpose training. Workload-specific benchmarks show 30-70% better price-performance for accelerators in edge and cloud inference.

• What are AI accelerators?

  AI accelerators are specialized hardware designed to speed up AI workloads beyond traditional CPUs or GPUs. They include ASICs (e.g., Google's TPUs), FPGAs (e.g., Intel's offerings), and NPUs (neural processing units for edge devices). Unlike versatile GPUs, they excel in efficiency for tasks like inference and training, reducing latency and power use in enterprise AI.

• Which AI accelerators outperform NVIDIA GPUs for inference?

  Top alternatives include Google TPUs (e.g., Trillium v6 with 67% better perf/watt), AWS Inferentia (up to 70% lower costs), Groq LPUs (10x lower latency), and SambaNova RDUs (world-record speeds like 129 tokens/second on 405B models). These shine in high-throughput, low-latency scenarios, often beating NVIDIA H100 by 2-4x in efficiency for NLP and recommendation systems.

• What are machine-learning accelerators?

  Machine-learning accelerators are hardware optimized for ML tasks like training and inference. Examples include Cerebras WSE-3 (wafer-scale engine with 900,000 cores for massive models) and Intel Habana Gaudi3 (2x throughput vs. A100 GPUs). They provide scalable, efficient alternatives to GPUs for data centers and edge computing.

• What are the best NPUs for AI acceleration in 2025?

  Leading NPUs for 2025 include Qualcomm's Snapdragon AI engines for mobile/edge (efficient on-device inference), Apple's Neural Engine in M-series chips, and MediaTek's Dimensity APUs. For enterprise, Intel's integrated NPUs in Meteor Lake CPUs offer real-time AI with low power. They excel in smart sensors and edge AI, often 2-3x more efficient than GPUs for battery-constrained devices.

• How do different AI hardware accelerators compare on latency?

  AI accelerators generally have lower latency than GPUs for inference: Groq LPUs achieve sub-millisecond responses, AWS Inferentia offers 4x higher throughput, and Graphcore IPUs provide 3-4x speed for NLP. GPUs like NVIDIA's win on raw compute but lag in efficiency (e.g., 10-50% higher latency in edge scenarios). Comparisons vary by model; benchmarks show accelerators reducing latency by 2-10x for real-time applications.

• What is the most power-efficient platform for AI inference in the cloud?

  Google Cloud TPUs (e.g., Trillium) are among the most power-efficient, with 67% better perf/watt than predecessors. AWS Trainium/Inferentia follows, offering 40% energy savings vs. GPUs. Microsoft Azure Maia 100 also competes, focusing on LLMs with custom efficiency. Overall, these ASIC-based platforms cut power use by 30-60% compared to NVIDIA for cloud inference.

• What are the best alternatives to NVIDIA for AI?

  Key alternatives include Google TPUs for balanced training/inference, AWS Trainium for scalable training, Intel Habana Gaudi for cost-effective throughput, Cerebras WSE for ultra-large models, and startups like Groq and SambaNova for inference speed. They reduce dependency by offering 20-50% better efficiency in specialized workloads, though NVIDIA leads in ecosystem maturity.

• What is the Microsoft Azure Maia AI accelerator?

  Microsoft's Azure Maia is a custom ASIC series for AI workloads in Azure data centers. Maia 100 handles LLMs efficiently, while Maia 200 (expected in 2026) promises enhanced compute. It offers competitive perf/efficiency for training and inference, with benchmarks showing 10-40% savings vs. GPUs in Microsoft ecosystems.

• How to reduce dependency on NVIDIA for large-scale model training?

  Shift to alternatives like AWS Trainium (83 petaflops per server, 30-40% better price-perf), Google TPUs (up to 42.5 exaflops per pod), or Intel Gaudi3 (50% faster with better efficiency). Use open-source frameworks like PyTorch for compatibility, and hybrid setups with GPUs for flexibility. This can cut costs by 20-50% while maintaining scalability for generative models.

Last updated: October 8, 2025

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References