Over the past decade or so, the evolution of edge AI inference has mirrored advancements in hardware, software, and AI model optimization.
The origins of AI inference at the edge can be traced back to the early days of embedded computing, when limited processing power made AI development impractical. Before AI models became widespread, edge devices designed for specific applications, such as industrial controllers and automotive electronic control units, relied on deterministic programming rather than data-driven learning approaches.
The advent of machine-learning models, including decision trees and support vector machines, introduced more sophisticated capabilities for tasks such as pattern recognition. While training was largely performed on centralized servers, however, inference remained constrained because of edge devices’ computational limitations.
Large language models (LLMs) have demonstrated remarkable success in data centers, where abundant computing resources and memory capacity enable a vast range of AI applications. Nonetheless, deploying these models closer to the data source, at the edge, remains a challenge because of the inherent constraints of edge devices, including inadequate processing power, limited memory capacity, energy efficiency and cost requirements, and specialized architectural limitations.
Sputnik moment for inference at the edge
The recent unveiling of an AI model by Chinese startup DeepSeek has sent ripples of surprise and even a touch of apprehension through Silicon Valley and the broader AI community. The DeepSeek model appears to be a serious contender to existing U.S. models, showcasing an innovative approach that directly tackles
the hardware limitations hindering LLM deployment for AI inference at the edge. This breakthrough may hold the potential to reshape the AI landscape, opening new possibilities in edge computing.
The challenge of deploying LLMs at the edge stems primarily from the immense computational demands they place on hardware, which in turn leads to high power consumption, excessive latency, and prohibitive costs, all of which are incompatible with the constraints of edge environments. The fabric of an LLM involves a massive number of floating-point matrix operations, a type of calculation that requires substantial memory to store the huge number of parameters and activations, typically exceeding the capacity of edge hardware. Adding to these challenges are the inherent architectural limitations of edge devices, generally optimized for low power consumption rather than the high degree of parallelism necessary for deep-learning tasks.
DeepSeek’s approach offers a potential solution to these problems by enabling a more effective and efficient execution of LLMs at the edge. Its strategy centers on two key innovations. The first is the implementation of the mixture-of-experts (MoE) architecture, a technique that lets the model activate different parts of its network selectively, based on the input it receives, potentially reducing the overall computational workload. The second key innovation is multihead latent attention (MLA). While the details of MLA require further clarification, it appears to be a mechanism designed to improve the efficiency of the attention mechanism, a crucial component of LLMs that allows the model to focus on relevant parts of the input.
These two advancements working in concert offer a promising pathway to overcoming the hardware bottlenecks that have so far restricted LLM deployment at the edge.
MoE to alleviate computational requirements
The latest DeepSeek R1 model features 670 billion parameters. Rather than deploy the full parameter set simultaneously, it breaks it down into smaller models of 37 billion parameters, each targeting a specific task—namely, expert models. The MoE approach allows the model to distribute its computational load, making it more efficient and suitable for resource-constrained environments. By engaging only a fraction of the model’s parameters during inference, the method greatly reduces the processing demand and lowers the active memory footprint, making large-scale reasoning more feasible on edge hardware, allowing for dynamic adjustment depending on task complexity and available resources.
MLA to increase computational efficiency
Another crucial innovation by DeepSeek is the introduction of MLA, an innovative attention mechanism designed to accelerate inference in LLMs that dramatically scales back cache memory requirements by up to 93%.
A major bottleneck in LLM inference is the massive memory required to store the key-value (KV) caches. These caches hold information about the input sequence and are crucial for the attention mechanism, which allows the model to focus on relevant parts of the input when generating text. Traditional multihead attention (MHA) stores key and value vectors for each word in the input sequence and for each attention head, leading to substantial memory consumption, especially with long sequences and numerous attention heads. MLA alleviates the memory bottleneck through a clever compression and decompression strategy.
Instead of storing the full key and value vectors, MLA compresses the high-dimensional tensor input information into a much smaller, low-dimensional latent vector. Think of this latent vector as a condensed representation of the input tensor, capturing the most important information needed for attention. The compression step drastically reduces the memory footprint required for the KV cache. When the model needs to calculate attention for a particular word or token, the latent vector is decompressed back into the high-dimensional space, reconstructing the necessary key and value vectors. The decompression process allows the model to access the relevant information for attention calculation, even though only the compressed latent vector is stored.
The advantage of MLA is its efficiency. By storing only the compressed latent vector, the memory requirements are significantly reduced. This reduction in memory usage translates to faster retrieval of information from the KV cache, leading to improved inference speed. Furthermore, DeepSeek V2’s results suggest that MLA not only reduces memory usage and speeds inference but can even enhance the model’s performance, achieving higher accuracy than traditional MHA.
Essentially, MLA offers a way to process information more effectively. It compresses the essential data needed for attention, allowing the model to work with a smaller, more manageable representation of the input. This compression and decompression process allows LLMs to operate more efficiently, leading to faster and more memory-friendly inference, which is crucial for deploying these large models in real-world applications. It makes it feasible to run powerful LLMs on devices with limited memory resources, opening new possibilities for mobile and edge deployment.
While details about MLA remain limited, initial analyses suggest it shares similarities with speculative AI, a technique used to accelerate AI inference. However, experts believe MLA goes beyond speculative optimization and introduces novel ways of managing memory and attention mechanisms within LLMs deployed on edge devices. By minimizing cache needs, MLA significantly alleviates the dependency on high-end hardware, enabling inference on devices with lower memory capacity. Additionally, this optimization reduces latency, allowing for real-time AI interactions, and improves power efficiency, a critical factor for mobile and embedded applications.
Sunrise for LLM inference at the edge
DeepSeek’s breakthroughs in MoE and MLA signal a fundamental shift in how large LLMs can be deployed at the edge. By optimizing both computation and memory requirements, these innovations open new possibilities for edge AI applications. Enhanced real-time AI assistants on mobile and wearable devices, autonomous driving at Levels 4 and 5, smarter industrial automation with lower-latency inference, and improved AI-driven IoT devices with more powerful on-device intelligence are among the most promising use cases.
Changing the game
The challenge of bringing LLMs to the edge has revolved around balancing processing power, computational efficiency, energy consumption, and cost. DeepSeek’s strategic implementation of MoE and MLA marks a significant departure from traditional methods, paving the way for LLM inference to move beyond the confines of data centers.
This approach could revolutionize edge AI by unlocking a wide array of applications, from real-time language translation on mobile devices to sophisticated predictive maintenance on industrial equipment.
To gauge the importance of this innovation, it’s worth noting that while DeepSeek was the first to bring it to the public, most of the major AI companies today are exploring and implementing similar methodologies, recognizing its game-changing potential.