When people think of large language models, the first thing that usually comes to mind is something huge, resource-hungry, and expensive to maintain. And with good reason: the more powerful a model is, the more computational resources it consumes. But in recent years, an approach has been gaining popularity in the industry that allows us to get a lot from models without spending quite as much. It's called Mixture of Experts, or MoE for short.
A Large Model That Works Like a Small One
Simply put, the idea behind MoE is this: inside the model, there isn't one «universal solver», but rather a multitude of specialized blocks – so-called experts. When the model receives a request, it doesn't engage all of them at once. Instead, a special mechanism selects only those experts needed for that specific request.
Imagine a large company with dozens of employees in different specializations. When a client comes in with a tax question, they are sent to an accountant – not the entire office. This is exactly how MoE works: the model can be huge «on paper», but at any given moment, only a small part of it is active.
This fundamentally changes the relationship between a model's size and its operational cost. A model with hundreds of billions of parameters can process requests using only a fraction of those parameters – and do so faster and more cheaply than a «dense» model of comparable size, where the entire network is engaged for every request.
Why This Matters for Business
From a corporate standpoint, the picture gets even more interesting. Companies that deploy language models in-house – whether on their own infrastructure or in the cloud – pay for computational power with real money. And every extra gigabyte of memory, every percentage point of GPU load, is an expense item.
MoE models allow for higher effective capacity at a lower cost per request. In other words, for the same money, you can either process more requests or use a more powerful model without paying proportionally for its size.
Additionally, MoE scales well. When the load increases, the architecture allows for a more flexible distribution of work among experts. This is crucial for organizations where the number of requests to the model can change dramatically depending on the time of day or season.
It's Not That Simple: The Challenges of MoE
However, it would be unfair to only mention the upsides. MoE models create their own set of infrastructure challenges.
The first is memory footprint. Even though only a part of the model is active at any given time, all the experts must be stored in memory. This means the total «weight» of an MoE model can be very large, and deploying it requires substantial hardware resources.
The second is load balancing. If requests are highly homogeneous, the same set of experts will be overloaded while the rest sit idle. This is known as the routing imbalance problem, and it needs to be addressed during system setup.
The third is data transfer latency. When a model is deployed across multiple servers (which is typical for large MoE models), different experts may need to exchange data between nodes. This adds latency to each response if communication between the system's components is not optimized.
How This is Handled in Practice
This is where specialized tools for running and maintaining MoE models in corporate environments come into play. Red Hat, in particular, is developing an approach where several components work in tandem, each tackling a specific part of the challenge.
One of the key elements is vLLM, a system for running language models efficiently. It knows how to work with MoE architectures and solves some of the memory problems by smartly managing what data to keep «on hand» and what to load on demand.
On top of this, there's the llm-d project – a distributed inference system designed specifically with MoE characteristics in mind. In short, it separates the two stages of the model's operation – pre-processing the prompt and generating the response – and allows them to run on different hardware. This enables more fine-grained resource management and helps reduce the latencies mentioned above.
Another component is KServe, a platform for managing models in production. It takes on the orchestration: it ensures that the necessary model parts are available, scales the load, and manages model versions. For a business, this is crucial, because it's one thing to run a model in a test, and a completely different challenge to maintain its stable performance under a real workload.
Economics That Change the Equation
Putting it all together, the picture looks like this: MoE models enable companies to use more powerful AI with a comparable or even smaller infrastructure budget. It's not magic – it's the result of an architectural choice that avoids unnecessary computations where they are not needed.
For enterprises considering scaling their AI systems, this changes the very conversation about feasibility. Previously, the argument «we need a smarter model» almost automatically meant, «we need more servers.» With MoE, this dependency ceases to be linear.
This is especially true as more companies want to keep their models in-house instead of sending data to external services. In-house deployment is a matter not only of security but also of cost control. And in this regard, MoE can be more than just a technical detail; it can be a truly significant economic lever.
Open Questions
MoE is not a silver bullet, and the industry recognizes this. Issues like routing, load balancing, and memory management in multi-node deployments are all active areas of research and development. Tools like llm-d are only just starting out, and only time will tell how well they handle real-world corporate workloads across different configurations.
Another interesting question is how well the expert selection mechanism performs with atypical or «fringe» requests – those that do not fit neatly into any single specialization. This is a matter not just of performance but also of response quality, which directly impacts how users will perceive MoE models in real-world scenarios.
Nevertheless, the direction is clear: the large models of the future will likely not just be larger, but also smarter in how they distribute their work. And MoE is one of the most compelling steps in this direction that we have right now.
