Hard-Wired LLMs: What Taalas’ Custom AI Chips Really Mean

AI Hardware Feb 26, 2026

Every few months there’s a new headline about some breakthrough in AI hardware. Most of them boil down to one of three things:

we put more compute on the chip,
we moved memory closer to the compute,
we found a better way to run the same models as everyone else.

Recently I stumbled over a different flavour of story:

“This new AI chipmaker hard-wires AI models into silicon to make them faster and cheaper.”

The company is called Taalas, and their pitch is basically: give us your model, and we’ll turn it into custom silicon – a "hard-wired" version that runs an order of magnitude faster, cheaper and with lower power than a software implementation on GPUs.

That raised exactly the kind of questions I like:

What does “hard-wired LLM” actually mean in practice?
When does that make sense, and when is it a terrible idea?
What could this mean for people building real systems, not just benchmarks?

This post is my attempt to sort through that, in plain language.

What Taalas is actually claiming

I’m not going to repeat their marketing copy line by line, but the core idea looks like this:

they built a platform that can take an existing AI model (e.g. a language model) and “compile” it into a custom ASIC,
from "previously unseen model" to working silicon supposedly in around two months,
the result is what they call a "Hardcore Model": a chip that runs that specific model much faster and more efficiently than a general‑purpose solution.

The context here is important:

LLM workloads are very latency‑sensitive, especially in agentic setups where a model calls tools, APIs and other models.
Token‑per‑second (TPS) figures are a huge differentiator – nobody wants to wait for an agent to finish a chain of thought.
GPUs are flexible but power‑hungry and expensive at scale.

Other players are pushing in similar directions: bringing SRAM closer to compute, building massive wafer‑scale engines, or optimising compiler stacks for transformers.

Taalas takes a more radical route: instead of general‑purpose chips + smarter software, they move the model itself into custom silicon.

What “hard-wiring a model” really means

When you train a neural network, you end up with a set of weights and an architecture. Normally you deploy that to a GPU, NPU or CPU and let software (frameworks, kernels, runtime) do the work of applying those weights to inputs.

"Hard-wiring" a model, in the way Taalas describes it, essentially means:

taking the final, fixed model,
and mapping its structure directly into circuits and memory on a chip.

Instead of a generic matrix‑multiply engine that can run any model, you end up with:

a data path tailored to exactly your layers and dimensions,
weights baked into on‑chip storage in a way the hardware can stream through very efficiently,
control logic that doesn’t need to handle arbitrary graph structures – just your graph.

From an engineering point of view, that’s not absurd. We’ve done this before:

fixed‑function video encoders/decoders,
crypto accelerators,
DSP blocks for very specific workloads.

The new part is applying that idea to large language models.

Why you’d even consider doing this

If you have the scale and the right use case, model‑specific silicon can give you three big wins:

1. Lower latency

No generic scheduler, no "one size fits many" kernels. Just one model, mapped tightly onto the hardware.

That means:

fewer indirection layers,
less overhead per token,
and more of the chip’s energy going directly into useful compute.

In agentic environments where an assistant calls multiple models and tools in a chain, shaving tens of milliseconds off each call adds up quickly.

2. Better energy efficiency

If your chip only has to do one thing well, you can:

optimise data movement patterns aggressively,
tune memory and compute exactly to your model’s needs,
drop features and flexibility you don’t need.

That can translate into a much better “tokens per watt” number compared to a big, flexible GPU.

3. Predictable cost per token

If you know how many chips you need to serve a given load, and each chip has a stable performance profile, you can calculate your cost per token more like you’d calculate the cost of a database transaction.

For very large, stable workloads, that kind of predictability can be a big deal.

When this could make sense

All of this sounds nice in theory, but when would I actually consider something like this?

A few scenarios where it isn’t crazy:

You have a very stable, high‑volume model workload.
You’re not constantly swapping models in and out. You run the same model (or a small family of models) millions or billions of times per day.
Latency is a hard requirement.
You’re in a domain where even tens of milliseconds matter – trading, real‑time control systems, agent chains with strict SLAs.
Your model changes infrequently and in controlled ways.
You’re not shipping a new version every two days. When you do update, you can plan ahead.

Think about:

a customer service assistant that always uses the same fine‑tuned model,
a translation engine for a specific set of languages,
a ranking/model inference service inside a search or recommendation system.

In those cases, you could imagine moving from “generic hardware + model” to “hardware that is the model”.

Why I’d still be careful

As attractive as the performance story is, there are a few obvious trade‑offs.

1. Flexibility and update cycle

Models are changing fast. Architectures evolve, training techniques evolve, input distributions change.

If your model is baked into silicon, you need to answer:

How often can I update this without losing the benefit?
What if I discover a subtle issue in the model after it’s in production on hardware?
Do I need to keep multiple generations of chips around, each tied to slightly different versions?

Companies like Taalas claim they can go from model to chip in a couple of months. That’s impressive if true – but it’s still not the same as deploying a new checkpoint to GPUs this afternoon.

2. Vendor lock‑in and ecosystem

General‑purpose hardware (GPUs, NPUs) benefits from broad ecosystems:

multiple vendors,
open toolchains,
lots of battle‑tested kernels and frameworks.

With model‑specific ASICs you’re much more dependent on:

a single vendor’s toolchain,
their ability to stay in business and support the chips,
their roadmap for new model architectures.

That might be fine for some high‑value, high‑volume workloads. But it’s a strategic bet, not just a technical one.

3. The “good enough” bar for general‑purpose hardware keeps moving

GPUs, NPUs and compiler stacks are not standing still. Every generation brings:

better support for transformers,
improved quantisation and kernel fusion,
clever scheduling and batching.

The question is not “can a custom chip beat a GPU on one benchmark?” – that’s almost always possible. The question is:

Is the extra performance worth the loss in flexibility and the complexity of a custom hardware pipeline?

How this fits into the bigger picture

I don’t see hard‑wired LLMs replacing general‑purpose AI hardware. I see them as one more point on the spectrum:

Cloud GPUs/TPUs: maximum flexibility, high power, high cost, fast iteration.
On‑device NPUs / edge chips: balanced flexibility and efficiency for a wide range of models.
Model‑specific ASICs: minimal flexibility, maximum efficiency for a fixed workload.

If you’re building systems in 2026, it’s worth knowing that this option exists – especially if:

you run a small number of models at very high scale,
latency and energy use materially affect your business,
and you’re willing to trade flexibility for performance.

For most projects, we’ll stay in the world of GPUs, NPUs and maybe some edge accelerators. But if the idea of "LLM on a chip" keeps showing up in headlines, it’s good to understand what’s actually behind it – and when it might make sense to hard‑wire intelligence into silicon on purpose.

For more background on Taalas’ positioning and claims, you can check their coverage and materials here:

Coverage of Taalas’ "hard-wired" model chips
Taalas website (if you want to read their own description and specs)