AI Model Efficiency Takes Center Stage as Developers Embrace Mixture of Experts and Compression Techniques

Using 100% of Your AI Brain All the Time Isn’t Efficient

AI development has long chased bigger, smarter models, but that approach comes with a heavy price: massive compute demands. This challenge hits hardest in regions with limited access to top-tier AI chips, such as China. Even outside those constraints, developers are shifting focus toward smarter efficiency strategies to manage resource use better.

One approach gaining serious traction is the mixture of experts (MoE) architecture, paired with newer compression techniques. Nearly three years after ChatGPT's launch sparked the AI boom, the community is finally wrestling with the real cost of running large language models (LLMs).

Neural Net Developers Focus on Efficiency

MoE models like Mistral AI's Mixtral aren't brand new, but the last year has seen a surge in open-weight LLMs from industry giants such as Microsoft, Google, IBM, Meta, DeepSeek, and Alibaba adopting MoE. The reason is clear: MoE is far more efficient than traditional dense architectures.

What Is Mixture of Experts (MoE)?

Originally introduced in the early 1990s, MoE splits a large model into many smaller specialized sub-models called "experts." Each expert can focus on a specific task, like coding or math. At any given time, only a subset of these experts is active, which greatly reduces the compute needed for inference.

For example, DeepSeek's V3 model contains 256 routed experts plus one shared expert, but only nine experts get activated per token. This selective activation reduces workload but can slightly dip the model’s output quality compared to dense models of similar size. Alibaba’s Qwen3-30B-A3B MoE model, for instance, trails its dense counterpart marginally in benchmarks.

However, the efficiency gains outweigh the minor quality differences. Since fewer parameters are active, memory bandwidth demands no longer scale directly with model size. This means you can store model weights in more affordable, slower memory without sacrificing performance.

Breaking Through the Memory Wall

To understand the impact, compare Meta's dense Llama 3.1 405B model with Llama 4 Maverick, which uses MoE. Running an 8-bit quantized Llama 3.1 405B requires over 405 GB of VRAM and around 20 TB/s memory bandwidth to generate 50 tokens per second. In contrast, Llama 4 Maverick uses the same memory but needs less than 1 TB/s of bandwidth because only 17 billion parameters are active per token.

This bandwidth reduction means Llama 4 Maverick can generate text roughly ten times faster on the same hardware. It also opens the door to using cheaper memory types like GDDR6 or GDDR7 instead of expensive, power-hungry high-bandwidth memory (HBM).

For example, Nvidia’s new RTX Pro Servers, announced at Computex, come with eight RTX Pro 6000 GPUs, each featuring 96 GB of GDDR7 memory. Together, they offer 768 GB VRAM and 12.8 TB/s bandwidth — enough to run Llama 4 Maverick at hundreds of tokens per second. This setup is much more affordable than Nvidia’s HGX H100 systems, which cost hundreds of thousands of dollars.

That said, extremely large models like a hypothetical Llama 4 Behemoth—boasting 2 trillion parameters—would still require multiple racks of GPUs with HBM to run effectively.

Are CPUs Gaining Ground in AI Inference?

CPUs might finally be practical for some AI workloads. Intel recently showed a dual-socket Xeon system running Llama 4 Maverick at 240 tokens per second with under 100 ms latency per token. This system could support roughly 24 simultaneous users at 10 tokens per second each.

While not as fast or efficient as GPUs for many workloads, CPUs may be a viable fallback in regions with GPU import restrictions or for specific use cases where latency and batch size trade-offs are acceptable.

Reducing Model Size: Pruning and Quantization

MoE models reduce bandwidth but not the memory needed to store weights. For instance, Llama 4 Maverick still requires over 400 GB of memory in 8-bit precision. That's where pruning and quantization come in.

Pruning removes redundant or less important weights, slimming down models without a big hit to quality. Nvidia has released pruned versions of Meta’s Llama 3 models and has pushed support for lower-precision datatypes like 8-bit and 4-bit floating-point formats.

Similarly, AMD will soon release chips with native FP4 support, helping avoid bottlenecks during inference at scale.

Quantization compresses model weights from 16-bit formats down to 8-bit or even 4-bit. This drastically cuts memory and bandwidth requirements but can impact output quality. Dropping from 16-bit to 8-bit usually causes minimal quality loss, and some models are now trained at 8-bit precision. Going down to 4-bit is trickier and often requires selective precision to maintain acceptable results.

Putting It All Together

Combining MoE with 4-bit quantization can dramatically reduce the cost and compute needed to run large models, especially when memory bandwidth is a bottleneck or when expensive HBM is out of reach due to trade restrictions or cost.

Even using just one of these technologies can cut equipment and operating expenses significantly, provided the model is put to good use. Not everyone is finding immediate value, though — a recent IBM survey of 2,000 CEOs found only a quarter of AI deployments delivered expected returns.

If you’re looking to sharpen your AI skills or explore AI model deployment techniques, check out some practical courses and resources like those offered at Complete AI Training.