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Etched

Notes

Etched

One-line summary: Private (unlisted) transformer-inference ASIC startup — founded 2023 by gavin-uberti and rob-wachen — building full inference racks on TSMC 4nm; tracked here not as a tradeable itself but as load-bearing evidence for the inference-ASIC wave's effect on public tickers (TSM demand broadening, NVDA inference-share threat).

What it is

A chip-and-systems company building complete racks for AI inference (chip, boards, power delivery, custom interconnect, contract manufacturing in Taiwan). Claims two technical bets: low-voltage inference (running at "under half the voltage of any other AI chip" to escape thermal throttling and pack more usable FLOPs per watt) and cluster-scale memory (a fully custom interconnect stack cutting chip-to-chip latency >5x vs. Nvidia's ~4,000ns, letting a whole scale-up cluster's SRAM+HBM act as one pool). First product ("Sohu" rack) taped out working on first attempt; claims >$1B customer demand and ~$800M raised.

Why it matters to stock-market

Etched is private — no direct trade. Its wiki value is as a datapoint in public-ticker chains:

Sourcing caveat (read first)

All load-bearing performance and demand claims come from the founders on a podcast hosted by a disclosed Etched investor2026-06-30-podcast-invest-like-the-best-etched-building-ai-hardware-to-make-inference: Patrick O'Shaughnessy: "I'm a big Etch Investor. I've been involved for a long time... So I'm incredibly biased in this conversation." Treat every claim (voltage, latency, $1B demand, 40-day bring-up) as unverified promotional until independently benchmarked. No independent corroboration in the wiki yet.

Key facts (all self-reported, same source)

  • Founded 2023 by Uberti (ex-kernels engineer, Harvard dropout) and Wachen; CTO Mark Ross (ex-Cypress Semi CTO); rack lead Brian Leyler (built Nvidia HGX/DGX systems).
  • Fabbed at TSMC N4; own factory in Taiwan; CM (not JDM) model; "different HBM than Rubin."
  • Claims: >$1B customer demand, ~$800M raised, silicon-to-rack-inference in 40 days (vs a named-anonymous competitor's 10 months), chips running at "under half the voltage of any other AI chip."
  • Strategy: no compiler, no CUDA/PyTorch/ONNX support — kernels-first for "under 100 models that actually matter"; HFT firms cited as culturally aligned early believers/hires.

Open questions

  • Independent benchmarks of the concurrency-per-megawatt claim (order of magnitude vs GPUs at a given interactivity) — nothing public yet.
  • Does gen-2 stay off the leading node, or does scaling force it into contention with Rubin for the same wafers/HBM (which would break the "additive" claim)?

Sources

Related

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