Was I wrong about Etched? - zach's tech blog

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Was I wrong about Etched?<br>Or did they just fake it until they made it?

zach<br>Jul 15, 2026

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I’ve been a public critic of Etched for a while. I’ve put out a couple articles rebuking the idea that a chip focused purely on transformers would significantly outperform a modern GPU. That idea was ostensibly Etched’s core premise. Put simply, modern datacenter GPUs are already ruthlessly optimized for running transformers, so “focusing on transformers” entails going head-to-head with Nvidia. There have also been a number of negative rumors about Etched going around the chip world, from senior talent leaving to their first tapeout failing due to horrible thermal issues. So recently, when they announced their first chips were going into production, I was surprised. Their inference servers lean on a couple technologies: highly under-volted logic, and pooled rack-level memory modules. So, was I wrong about Etched the whole time?<br>Well, maybe not. All the doubters were skeptical of Etched’s original plan, which was just a dedicated transformer ASIC. If their under-volted logic and rack-level memory solutions were invented after those initial announcements, that doesn’t mean the skeptics were wrong. It just means that Etched managed to fundraise enough on their initial bad ideas to hire the silicon engineers who developed new, better ideas -- a common route for startups. Separately, though, it remains to be seen whether their new technology proposals actually result in better chips.<br>So today, we’ll be going through both. I’ll briefly discuss the history of Etched and all the rumors surrounding it, and then move on to the substance of their current technology, to actually answer the question: was Etched good before, and is Etched actually good now?<br>Thanks for reading zach's tech blog! Subscribe for free to receive new posts and support my work.

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The Rumor Mill

Etched was founded in 2022, ostensibly on the idea that a chip purely focused on LLMs would significantly outperform GPUs. They drew an analogy to dedicating Bitcoin ASICs, which genuinely offer massive performance improvements over GPUs by focusing purely on running SHA-256. There are two big problems with this comparison, though. Firstly, SHA-256 is a highly specialized algorithm, with a bunch of bit-shifts and XORs and modular additions that conventional GPUs aren’t optimized for. LLM inference, on the other hand, is mostly just big matrix multiplies, which GPUs and TPUs are already pretty great at. Also, more importantly, Nvidia is actively focusing on optimizing their datacenter GPUs for running LLMs! So “focusing on LLMs” means competing directly with Nvidia, without any differentiating technology or market counterpositioning. In my book, that’s a bad idea.<br>After Etched was founded, they hired a team to execute on that first transformer ASIC. From the rumors I’ve heard, this did not go super well -- though the following is all unconfirmed, so take it with a grain of salt. I’ve heard about senior talent leaving due to conflict with company leadership, but there were technical issues too. Rumor has it that they were originally planning on leveraging a combination of AlphaChip-like AI techniques and external contractors for physical design of that first Sohu transformer chip. That didn’t go well, and their Sohu transformer chip apparently had major thermal issues, which forced them to reevaluate their entire system design. Etched’s leadership even publicly admits the issues they’ve had with Indian physical design contractors. Clive Chan called it a “rocky start”, which is an understatement.<br>So what do you do when your chips are melting, your senior talent is leaving, but you have a ton of capital raised from high-frequency-trading firms and venture funds1 who don’t know enough about chips to question the too-good-to-be true “focus on transformers” story? Well, you pivot. And when your chief architect has experience working on Bitcoin miners, which are often run at aggressively low voltages to reduce power consumption, it makes sense to try to do the same for AI chips.<br>Thus, Etched’s Low Voltage Inference technology was born. It means that their next chip won’t melt like the first one did. But is it actually good?<br>Thanks for reading zach's tech blog! This post is public so feel free to share it.

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Is low voltage inference actually good?

The formula for the power consumption of a standard digital switching circuit is:<br>P ∝ CV2f<br>where C is the load capacitance, V is the voltage, and f is the frequency you run the circuit at. At the same time, the maximum frequency of a circuit is inversely proportional to the supply voltage -- lower voltage circuits are slower.<br>That means if they lower the supply voltage by a factor of N, the circuits run slower by a factor of N, but they reduce power by a factor of N2. So LVI improves the datacenter economics (tokens per...