How accelerating feed-forward networks in disaggregated inference pipelines power next-generation AI - d-Matrix

br]:hidden text-3xl lg:text-5xl leading-[1.36]"><br>How accelerating feed-forward networks in disaggregated inference pipelines power next-generation AI

Feed-forward networks offer a small oasis of predictability in a fundamentally uncertain field. And they’re perfect candidates for optimized accelerators that manage those fixed, reliable needs.

Published: June 9, 2026<br>By: Matthew Lynley

Disaggregated AI inference pipelines that split the pre-fill and decode process across different hardware—like two different GPUs or a GPU and a custom accelerator—already substantially speed up AI inference and dramatically improve efficiency.

But the decomposition of AI inference doesn’t have to stop there: it can go all the way down to the model itself. The feed-forward network (FFN) , in particular, offers a unique opportunity for AI inference optimization in a disaggregated pipeline.

Rather than managing the entire transformer layer/block on a single piece of hardware, disaggregated pipelines can split it up: run the attention on a memory-rich accelerator like a GPU; and run the FFN on an accelerator that doesn’t have to manage the growing size of a KV cache.

How the feed-forward network works

Once the attention process is complete, the model has provided a kind of “best guess” in the form of an output vector. That guess is based on the limited “space” it has available and is relative to every prior token. The attention layer can’t see the whole picture, which is where the FFN comes in.

The FFN substantially improves the quality of that guess, but it requires a much bigger surface area to work. It first expands the space available—by increasing the dimensionality—to access a larger set of patterns it’s learned during training.

Essentially, attention says “this is the best guess, based on what I have to work with.” The FFN says, yes, here are some reasons why that guess works.

For example, a token may come in, and the FFN can determine from a decorator and the wall of python code coming in behind it that:

This is part of code for managing a web request or route

It’s written in Python

It’s most likely built with Flask.

Flask uses decorators to define endpoints, so treat this as part of an endpoint.

The code suggests it’s a ‘/’ route, so this is relevant to the front page of a website

This works by expanding the dimensional space of what’s coming in from the attention layer. A “hidden state”—basically a vector capturing the results from the attention process—is passed in between the GPU and an optimized accelerator. The accelerator projects it into a larger dimensional space to capture more context.

Once that context is fully captured, it’s “dropped” back into standard dimensionality with a substantially richer texture and is then either fed into the next layer, or in the case of the final layer, the result moves on to selecting the vocabulary to generate the final result.

An FFN on memory-optimized accelerators is also very well positioned for mixture-of-expert models. GPUs using HBM can saturate compute but require significantly higher batch sizes. SRAM-based accelerators excel in lo batch size scenario

Disaggregating the feed-forward network accelerates the AI inference decode step

The majority of the parameters of an AI model comprise the FFN itself. But the amount of memory required for the FFN is fixed—there’s no growing KV cache to manage on the attention side.

Instead, you know exactly what’s on it: the weights for the function to capture fine-grained context on what’s fed back into the attention layer or transformed into a final token. That makes it an excellent candidate to offload to another set of accelerators in a disaggregated pipeline with fixed memory.

As a result, you can predictably scale and manage the FFN throughout the entire AI inference process.

Memory-optimized accelerators, for example, can host the FFN in a pool of SRAM that allows it to process at substantially lower latencies without having to fiddle with the complicated tradeoff of latency and throughput for growing KV caches.

This approach is also particularly well-suited to mixture-of-expert models.

While the total pool of experts is still stored on SRAM, inference only taps a small slice of those experts. The weights are always accessible, but the accelerator only pays compute cost when it needs a specific expert, which ends up reducing energy requirements.

Disaggregated feed-forward networks offer better predictability to enterprises

AI applications are inherently unpredictable: outputs can differ radically, KV caches don’t necessarily grow uniformly, and even an extra space in a prompt can result in a completely different response.

That’s a massive challenge for enterprises, where the success of an AI-powered application isn’t just the results—it’s the ability to scale to meet demand in a reliable,...