Artificial intelligence (AI) is no longer a futuristic concept — it's here, powering everything from your smartphone's camera to complex financial models. At the core of this transformation are AI chips — specialized hardware designed to accelerate the complex computations required by machine learning and deep learning algorithms. These tiny but mighty processors are the powerhouse behind today's most advanced AI applications.
As AI models grow exponentially larger and more complex, the need for purpose-built hardware has become clear. For developers and organizations building the next wave of AI, standard processors just can't keep up. This blog explores the different types of AI chips, why specialized AI platforms matter more than ever, and what the future holds for AI infrastructure.
For decades, the Graphics Processing Unit (GPU) has been the workhorse of computing. It's a general-purpose processor designed to handle a wide range of tasks sequentially — one after another, very quickly.
When it comes to AI, GPUs are good at model training, but inefficient and expensive for inference. Cooling high-performance inference is impractical for GPUs because of the cost of feeding this power-hungry platform compared to other architectures. In comparison, Reconfigurable Dataflow Units (RDUs) can deliver the same high-level performance for agentic workloads with lower latency, without high power demands, and in a smaller footprint.
Modern AI demands truly extraordinary performance. Leading-edge models like DeepSeek-V-3.1-Terminus and OpenAI’s gpt-oss 120B contain billions of parameters, making traditional processing architectures obsolete for all but the smallest tasks.
Let’s break down the typical flow of AI model inference to understand why. Although an excellent choice for training AI, traditional GPUs are tremendously inefficient for AI inference. Once the prompt is entered and the query is submitted, the model is loaded into active memory and GPUs must load the entire model, regardless of how much is actually needed for a specific prompt.
Large dense models can strain hardware, requiring immense processing power and active memory — most of which is wasted on irrelevant data since the system runs the whole model. This approach leads to soaring energy demands, excessive data movement, and ultimately slower, more expensive inference.
To solve this bottleneck, Mixture of Experts (MoE) models activate only the specific submodules ("experts") relevant to a given task instead of loading and running an entire model. Hardware must rapidly shuttle data to and from different "experts," requiring ultra-efficient, intelligent routing. AI accelerators that focus solely on SRAM simply aren’t designed for this level of modular compute. MoE’s dynamic activation pattern overwhelms their memory bandwidth and internal communication pathways.
Purpose-built AI hardware, like RDU chips, has more memory to handle the unprecedented data movement complexity of MoE models. The SN40L RDU architecture from SambaNova is engineered from the ground up to unlock MoE — dynamically routing data, activating only the necessary experts, and scaling to support the world’s largest, most advanced models with high efficiency. RDUs unify operator fusion to reduce the latency experienced with CPUs and GPUs.
This is the new era of AI hardware innovation: ultra-targeted, outrageously fast, and designed for tomorrow’s most demanding applications.
AI chips aren’t just faster versions of GPUs — they’re purpose-built engines designed to crush the demands of modern machine learning. Here’s how purpose-built AI chips set themselves apart:
Purpose-built AI chips, such as SambaNova’s SN40L, are engineered from the ground up to meet these challenges — delivering both speed and flexibility for the latest AI models.
At a basic level, all computer chips are made of billions of tiny switches called transistors. These transistors perform two primary functions: logic (making decisions) and memory (storing data). The world of chips for AI is diverse, each with its own benefits and is suited for different tasks. Let's compare the most common ones.
Originally designed for rendering graphics in video games, GPUs were the first hardware to demonstrate the power of parallel processing for AI. Thousands of cores make GPUs effective for training deep learning models.
Developed by SambaNova, the SN40L RDU runs the largest, most complex models, including MoE, with ease. A next-generation AI chip engineered to accelerate deep learning models at scale, the SN40L is purpose-built for AI that offers the performance of a custom chip with the flexibility of a programmable one. Its unique dataflow processing and three-tier memory system enable the RDU to achieve blistering-fast inference speeds, incredible power efficiency, and the ability to adapt to new and evolving AI algorithms. Unique to the RDU, model bundling combines multiple models to hot-swap models in milliseconds to reduce compute requirements and costs. As agents are taking off, RDUs offer the inference speeds needed to run numerous models for high-performance agentic AI.
Developed by Google, TPUs are a type of ASIC specifically designed to accelerate TensorFlow workloads. They excel at the matrix multiplication that is central to neural networks.
Specialized processors designed to accelerate deep learning tasks on edge and mobile devices, NPUs are found in smartphones and cameras. They handle AI-specific computations locally, reducing latency and reliance on the cloud.
FPGAs contain an array of programmable logic blocks. Unlike fixed-function ASICs, they can be reprogrammed after manufacturing, offering a high degree of flexibility. This flexibility allows them to be tailored for specific AI applications, offering a balance between performance and adaptability.
A proprietary chip from Groq, LPUs are a new type of end-to-end chip designed specifically for the speed and memory demands of AI language tasks and large language models (LLMs). Unlike GPUs, which are designed for parallel processing, LPUs are built with a sequential processing architecture to handle intensive language applications.
Developed by Cerebras, WSEs are designed to provide massive computing power for AI and scientific simulations by integrating a vast number of cores and memory on a single wafer, eliminating the need for complex communication between multiple smaller chips.
AI chips are the engines driving innovation across countless industries. Their ability to process information at incredible speeds enables applications that were once science fiction.
AI chips are more than just another piece of silicon; they are the fundamental building blocks upon which the entire AI ecosystem is built. For developers and organizations, the choice of AI infrastructure has profound implications for performance, scalability, cost, and the types of applications you can build.
As the AI landscape matures, relying on specialized, purpose-built hardware will no longer be an advantage — it will be a necessity. The future belongs to those who build on a foundation designed for intelligence from the ground up.
The journey to building high-performance, scalable AI systems starts with the right hardware and technology stack. Explore SambaNova’s AI platform and the revolutionary SN40L RDU chip to see how your organization can achieve unparalleled performance.