The rapid evolution of artificial intelligence (AI) has been marked by the rise of large language models (LLMs) with ever-growing numbers of parameters. From early iterations with millions of parameters to today’s tech giants boasting hundreds of billions or even trillions, the sheer scale of these models is staggering.
Table 1 outlines the number of parameters in the most popular LLMs today.
Table 1 The number of parameters in today’s most popular LLMs reaches into the billions if not trillions. Source: VSORA
To understand why leading-edge LLMs are scaling so rapidly, we must explore the relationship between parameters, performance, and the technological advancements driving this trend.
Role of parameters in language models
In neural networks, parameters represent the weights and biases that the model captures and modifies. They are analogous to synaptic connections in the human brain.
From a computational architecture perspective, parameters act as the model’s memory, storing the complex relationships and subtle nuances within the input data. Intuitively, an increase in the number of parameters in a language model translates to enhanced ability to understand context, generate coherent text, and even perform tasks for which they were not explicitly trained.
Today, the largest models exhibit behaviors such as advanced reasoning, creativity, and the ability to generalize across diverse domains, reinforcing the notion that scaling up is essential for pushing the boundaries of what AI can achieve.
Scaling laws and diminishing returns
Early LLMs demonstrated that increasing the size of models led to predictable improvements in performance, especially when paired with larger datasets and superior computational power. However, these improvements follow a diminishing returns curve. As models grow larger, the incremental benefits become smaller, requiring exponentially more resources to achieve significant gains.
Despite this, the race to build bigger models persists because the returns, while diminishing, remain valuable for high-stakes applications. For instance, in areas like medical diagnostics, scientific research and autonomous systems, even marginal improvements in AI performance can have profound implications.
Drivers of parameter explosion
Modern LLMs are trained on vast and diverse datasets encompassing entire libraries of books, research papers, studies, analyses of a wide range of human endeavors, extensive software code repositories, and many more data sources. The breadth of these datasets necessitates larger models with billions of parameters to fully exploit the richness of the data.
Multimodal capabilities
Leading-edge LLMs are not limited to processing text alone; many are designed to handle multimodal inputs, integrating text, images, and other types of data. Expanding the parameter count allows these models to process and draw connections between various data types, thus enabling them to perform tasks that involve more than one type of input—such as image captioning, generating audio responses, and cross-referencing visual data with textual information.
The trend toward multimodal capabilities requires a significant increase in parameters to manage the added complexity. The added computational storage enables richer representations of different data modalities and deeper cross-modal understanding, making these models more versatile and valuable for practical applications.
Zero-shot/few-shot learning
One standout advancement in LLMs has been their proficiency in zero-shot and few-shot learning. These models can perform new tasks with minimal examples or even without explicit task-specific training. GPT-3 popularized this capability, showing that an appropriately large model could infer task instructions from just a few examples.
Achieving this level of generalization requires a massive number of parameters so that the model can encode a wide variety of linguistic and factual knowledge into its architecture. This capability is particularly useful in real-world applications where training data may not be available for every conceivable task. Expanding parameter counts helps LLMs build the necessary knowledge and contextual flexibility to adapt to various tasks with minimal guidance.
The competitive AI landscape
The competitive nature of AI research and development also fuels parameter explosion. Companies and research institutions strive to outdo each other in developing state-of-the-art models with more impressive capabilities.
The metric of “parameter count” has become a benchmark for gauging the power of an LLM. While sheer size is not the sole determinant of a model’s effectiveness, it has become an important factor in competitive positioning, marketing, and funding within the AI field.
Challenges in computational power and training infrastructure
The dramatic rise in parameter counts for AI models would not have been possible without parallel advancements in computational power and the supporting infrastructure. For decades, AI progress was hindered by the limitations of the central processing unit (CPU), the dominant computing architecture since its inception in the late 1940s. CPUs, while versatile, are inefficient at parallel processing, a critical capability for training modern AI systems.
A turning point occurred about a decade ago with the adoption of graphics processing units (GPUs) for executing deep neural networks. Unlike CPUs, GPUs are designed for efficient parallel computation, enabling rapid acceleration in AI capabilities.
Today, LLMs leverage distributed training across thousands of GPUs or specialized hardware such as tensor processing units (TPUs), combined with optimized software frameworks. Innovations in cloud computing, data parallelism, and sophisticated training algorithms have made it feasible to train models containing hundreds of billions of parameters.
Techniques like model parallelism and efficient gradient-based optimization have further advanced the field by distributing training tasks across multiple processors and clusters.
However, while larger parameter counts unlock unprecedented potential, they also bring significant challenges, chief among them being the soaring hardware computing resource demands. These demands inflate the total cost of ownership, encompassing not only sky-high upfront hardware acquisition costs but also steep operational and maintenance expenses.
Training vs. inference
Training: A computational beast
Training involves processing massive amounts of unstructured data to achieve accurate results, regardless of how long the task takes. It’s an extremely computationally intensive process, often reaching performance levels in the ExaFLOPS range.
Achieving these results typically demands months of continuous 24/7 operation on cutting-edge hardware. Today, this is conducted on thousands of GPUs, installed on large boards in vast numbers only available in the largest data centers. These setups come at enormous costs, but they are essential investments as no viable alternative exists at present.
Inference: A different approach
Inference operates under a distinct paradigm. While high performance remains critical, whether conducted in the cloud or at the edge, inference typically handles smaller, more targeted datasets. The primary objectives are achieving fast response times (low latency), minimizing power consumption, and reducing acquisition costs. These attributes make inference a more cost-effective and efficient process compared to training.
In data centers, inference is still executed using the same hardware designed for training—an approach that is far from ideal. At the edge, a variety of solutions exist, some outperforming others, but no single offering has emerged as a definitive answer.
Rethinking inference for the future
Optimizing inference requires a paradigm shift in how we approach three interconnected challenges:
- Reducing hardware requirements
- Accelerating latency
- Enhancing power efficiency
Each factor is critical on its own but achieving them together is the ultimate goal for driving down costs, boosting performance, and ensuring sustainable scalability.
Reducing hardware requirements
Lowering the amount of hardware needed for inference directly translates to reduced acquisition costs and a smaller physical footprint, making AI solutions more accessible and scalable. Achieving this, however, demands innovation in computing architecture.
Traditional GPUs, today’s cornerstone of high-performance computing, are reaching their limits in handling the scaling of AI models. A purpose-built architecture can significantly reduce the hardware overhead by tailoring design to the unique demands of inference workloads, delivering higher efficiency at lower costs.
Accelerating latency
Inference adoption often stalls when query response times (latencies) fail to meet user expectations. High latencies can disrupt user experiences and erode trust in AI-driven systems, especially in real-time applications like autonomous driving, medical diagnostics, or financial trading.
The traditional approach to reducing latency—scaling up hardware and employing parallel processing—inevitably drives up costs, both upfront and operational. The solution lies in a new generation of architectures designed to deliver ultra-low latencies intrinsically, eliminating the need for brute-force scaling.
Enhancing power efficiency
Power efficiency is not just an operational imperative; it is an environmental one. Energy-intensive AI systems contribute to rising costs and a growing carbon footprint, particularly as models scale in size and complexity. To address this, inference architectures must prioritize energy efficiency at every level, from the processor core to the overall system design.
Breaking through the memory wall
At the core of these challenges lies a shared bottleneck: the memory wall. Even with the rapid evolution of processing power, memory bandwidth and latency remain significant constraints, preventing full utilization of available computational resources. This inefficiency is a critical obstacle to achieving the simultaneous reduction in hardware, acceleration of latency, and improvement in power efficiency.
Transformation of AI systems
The rapid expansion of parameters in cutting-edge LLMs reflects the industry’s unyielding drive for superior performance and enhanced capabilities. While this progress has unleashed groundbreaking possibilities, it has also exposed critical limitations in current processing hardware.
Addressing these challenges holistically will open the path forward to wide adoption of inference as a seamless, scalable process that performs equally well in both cloud and edge environments.
In 2025, innovative solutions are expected to redefine the hardware landscape, paving the way for more efficient, scalable, and transformative AI systems.
Lauro Rizzatti is a business advisor to VSORA, a startup offering silicon IP solutions and chips. He is a verification consultant and industry expert on hardware emulation.
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