Inside the Architecture Powering Today’s Most Advanced Frontier AI Models
If you take a close look at nearly any leading frontier artificial intelligence model in 2025, you will find a common architectural choice beneath the surface: mixture-of-experts (MoE). Inspired by how the human brain activates specific neural regions depending on the nature of a task, MoE architectures optimize performance by dynamically routing requests to targeted sub-networks of “experts.” Instead of engaging every parameter of a massive model for every token of output, only the experts most relevant to the prompt are activated.
This selective activation dramatically increases efficiency. It speeds up inference, reduces energy consumption and operational cost, and enables significantly larger models to run without a corresponding explosion in compute demand. As a result, MoE has quickly become the structural foundation behind today’s most capable open-source models.
The momentum is clear: on the independent Artificial Analysis (AA) leaderboard, all top 10 highest-ranking open-source intelligence models now rely on MoE techniques. These include notable models such as DeepSeek-R1 from DeepSeek AI, Kimi K2 Thinking from Moonshot AI, gpt-oss-120B from OpenAI, and Mistral Large 3 from Mistral AI — each demonstrating that MoE is now the preferred approach for building powerful reasoning systems.
Yet despite its advantages, scaling MoE deployments into practical production environments has historically been a significant engineering challenge. Because MoE models distribute experts across numerous GPUs, bottlenecks in memory bandwidth, network latency, and inter-GPU coordination make achieving peak performance extremely difficult. This is why the arrival of NVIDIA’s GB200 NVL72, a rack-scale system built through extreme hardware-software codesign, represents a major breakthrough. It allows MoE models to scale cleanly and efficiently while unlocking dramatically higher performance.
Among the most impressive results so far is the performance gain demonstrated by Kimi K2 Thinking, currently the highest-ranked open-source MoE model. When running on the GB200 NVL72 system, it achieves up to 10× faster performance compared with operation on NVIDIA HGX H200. Similar gains have been observed for other top MoE models, including DeepSeek-R1 and Mistral Large 3, reinforcing that MoE combined with NVIDIA’s full-stack inference platform is becoming the foundation for the next era of frontier AI.
What Exactly Is MoE, and Why Has It Become Dominant for Frontier AI?
Before MoE architectures became mainstream, the industry focused almost exclusively on building denser and larger models. The thinking was simple: more parameters meant higher intelligence. But this approach requires activating all parameters — often hundreds of billions — every time the model generates a token. Although such dense approaches enabled large leaps in capability, they impose massive computing and power requirements that scale poorly.
MoE turns this paradigm on its head. Rather than relying on a uniform set of parameters for every task, MoE models contain multiple specialized expert networks trained to solve different categories of problems. For each piece of text processed, a router determines which experts are the best match. This means only a small subset of the model — sometimes as few as tens of billions of parameters — is used at inference time.
The result is a system that can scale intelligence dramatically without scaling compute requirements in parallel. MoE models therefore offer superior performance per watt, performance per dollar, and token throughput efficiency compared to dense systems of the same size.
Unsurprisingly, this efficiency is driving rapid adoption. More than 60% of open-source model releases in 2025 now use MoE architectures, and since early 2023, MoE-based approaches have helped drive an approximate 70× improvement in model reasoning capability.
As Guillaume Lample, cofounder and chief scientist at Mistral AI, explains:
Our development of open-source mixture-of-experts systems starting with Mixtral 8x7B two years ago laid the groundwork for building models that deliver both high intelligence and sustainability. With Mistral Large 3, we’ve shown that MoE enables dramatic gains in efficiency while reducing energy and compute requirements.
Why Scaling MoE Has Been Hard — and How Extreme Codesign Solves It
MoE models are intrinsically distributed systems. Because no single GPU can contain all expert parameters, the experts must be spread across many GPUs — a practice called expert parallelism. While GPU clusters like NVIDIA H200 have the horsepower to support MoE deployment, key bottlenecks remain:
- Memory pressure: Each GPU must continually load and unload parameters from high-bandwidth memory for the experts involved in each token, creating substantial memory strain.
- Communication latency: All-to-all communication between experts is essential to assemble each final answer, but spreading experts across many GPUs creates delays when communication depends on slower scale-out networking.
To eliminate these barriers, NVIDIA engineered the GB200 NVL72, a fully integrated rack-scale platform consisting of 72 NVIDIA Blackwell GPUs working cohesively as a single giant GPU. Connecting all 72 GPUs through NVLink Switch provides 30 TB of shared fast memory and 130 TB/s of NVLink bandwidth — allowing every GPU to communicate with every other with extremely low latency.
This architecture resolves the fundamental MoE scaling limits:
Reduced memory load
Because experts are distributed across up to 72 GPUs, fewer experts reside on each GPU, easing memory bandwidth and enabling support for larger batches, more concurrent users, and longer input sequences.
Accelerated expert coordination
Communication between experts occurs at NVLink speeds rather than slower external networking. NVLink Switch can even contribute compute toward combining expert outputs, significantly reducing latency.
In addition to these hardware improvements, NVIDIA’s software stack incorporates key optimizations, including:
- NVIDIA Dynamo — orchestrates distributed inference, dividing workloads between prefill and decode stages to improve throughput.
- NVFP4 precision format — maintains model accuracy while delivering additional speed and energy savings.
- Support from TensorRT-LLM, SGLang, and vLLM — enabling developers to deploy large-scale MoE inference efficiently.
The impact is substantial enough that major cloud providers and NVIDIA Cloud Partners — including AWS, CoreWeave, Google Cloud, Microsoft Azure, Lambda, Oracle Cloud Infrastructure and others — are actively rolling out GB200 NVL72 infrastructure worldwide.
As Peter Salanki, CTO and cofounder at CoreWeave, explains:
Our customers are using our platform to productionize mixture-of-experts models for advanced agent workflows. Working closely with NVIDIA enables us to deliver the performance and reliability required to run MoE at scale — something only possible on a cloud built specifically for AI.
Enterprises building next-generation AI systems, such as DeepL, are already leveraging NVL72 rack-scale design to train and deploy larger, more efficient MoE models.
The Bottom Line: Performance Per Watt Transforms AI Economics
The NVIDIA GB200 NVL72 is demonstrating up to a 10× improvement in performance per watt relative to prior platforms. This isn’t just a technical milestone; it reshapes business models. A tenfold boost in performance per watt equates to ten times more token revenue within the same power and cost footprint — a crucial advantage as datacenter energy limits and operating budgets tighten globally.
The combination of MoE architecture and NVIDIA’s full-stack platform marks a turning point. Frontier AI development is shifting from “bigger at any cost” to smarter, more efficient, more scalable intelligence, paving the way for models with significantly more reasoning power, lower latency, and dramatically improved economics.
Source Link:https://blogs.nvidia.com/blog/mixture-of-experts-frontier-models/
