Rethinking the CPU: Unlocking Hidden Performance for Client-Side AI Inference

When most people think of AI acceleration for client devices, they think GPUs. Some may nod to NPUs or specialized ASICs. But the CPU, the most ubiquitous compute unit in every device, rarely enters the conversation. That is a mistake. In our experience building AI inference engines optimized for real-world constraints, we have demonstrated that on client devices, particularly those with unified memory architectures, the primary bottleneck is not compute, but memory bandwidth. And when that is true, the humble CPU is often just as capable as any specialized processor, if you know how to use it.

The Bandwidth Bottleneck Nobody Talks About

It is easy to get swept up in the peak FLOPs of a GPU or the promise of dedicated neural engines. But for most transformer-based models, especially LLMs and multimodal architectures, the majority of inference time is not spent crunching math. It’s spent waiting on memory.

Take a typical 7B parameter model like LLaMA or Mistral. Even with int8 quantization, most of the matrix multiplies can require reading many megabytes of weights from memory. Multiply that by the number of layers, and tokens being generated, and it is easy to saturate the memory bandwidth on most client devices. In our testing, even small 3B param models are enough to fully saturate the available bandwidth on devices with unified memory.

Whether you are on a GPU, an NPU, or a CPU, once memory becomes the bottleneck, the raw FLOP advantage of specialized accelerators becomes less relevant. The key question shifts from "how fast can I multiply matrices?" to "how efficiently can I move data?"

CPUs often provide greater visibility and control over how memory is accessed, enabling software-level optimizations that are difficult or impossible on closed, vendor-locked acceleration pipelines.

Why CPUs Deserve a Second Look

Modern CPUs have quietly evolved into powerful AI inference platforms,if you treat them right. Today’s CPUs offer:

• Shared memory and coherent caches, enabling flexible memory sharing across threads and cores
• Wide SIMD units (AVX2, AVX-512, NEON, SVE) that can match the parallelism of small-scale NPUs
• Cache-aware scheduling strategies for maximizing locality across CPU cores
• Full low-level control, allowing the deployment of unconventional quantization schemes, custom scheduling strategies, and sparse or fused memory layouts Most importantly, CPUs let you build inference engines that adapt to your model, rather than fitting your model into a rigid accelerator kernel.

Beyond the FLOPs: Real-World Implications

In practice, we have found that when the working set size of a model approaches or exceeds cache size, the theoretical advantage of NPUs and integrated GPUs shrinks significantly. Why? Because they hit the same memory bottleneck,often without any way to address it.

On the CPU, by contrast, we can:

• Use software prefetching and cache-aware layout tricks to reduce bandwidth stalls
• Schedule threads with awareness of cache hierarchies and memory contention
• Implement non-standard quantization formats like per-block or asymmetric quantization that do not fit into vendor runtimes
• Overlap memory I/O and compute using custom multi-threaded pipelines

These are not abstract optimizations, they yield measurable speedups. On certain ARM-based SoCs we have tested, our optimized CPU inference engine (running 4-bit quantized models using custom memory-aligned layouts) can match the throughput of the integrated NPU, while also consuming less system memory and giving us full control over scheduling and fallback behavior.

We have also benchmarked SIMD-optimized inference paths on x86 laptops achieving throughput within 10–15% of integrated GPUs when running larger models in memory-bound scenarios (small or single batch, large prompt context), a scenario common in real-world applications like offline assistants, document summarization, and smart editors or code assistants.

On devices with unified memory architectures, the overhead of accessing large amounts of memory from an accelerator often negates theoretical performance gains in compute it could offer. In these cases, the CPU is not just a fallback, it is the smarter choice.

The Strategic Opportunity

There is a strategic blind spot in today’s AI stack. As everyone races toward more silicon, more cores, and more abstraction, we are overlooking the flexibility that CPUs offer. If we embrace CPUs not as a fallback but as a first-class target, we unlock:

• Greater developer agility, unbound by vendor APIs
• Platform independence across devices that already ship with powerful CPUs
• Performance-per-watt and memory efficiency that rivals or exceeds black-box NPUs in real-world usage

This shift in mindset,toward CPU-aware, bandwidth-optimized inference,opens the door to a new wave of AI applications that are lightweight, transparent, and performant on general-purpose hardware.

Looking Forward

This is just the beginning. In future posts, we will continue to examine the evolving role of CPUs in AI inference, and share our insights as we push the boundaries of what's possible on general-purpose hardware.

We believe the next generation of intelligent applications won’t just be about what model you run,but how and where you run it. And sometimes, the best place to run it is on the compute unit you already have.