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# AI chip startup Etched defies skeptics, hits $10.3B valuation from big-name investors

**[TechCrunch](https://daily.dev/sources/tc)** · 5 min read · 1 upvotes · 0 comments

## Summary

Etched, an AI chip startup founded by three Harvard dropouts in 2022, has closed a $300M Series C at a $10.3B valuation — doubling from its $5B valuation just seven months prior. The round was led by Sequoia with participation from a16z, SK Hynix, Jane Street, and notable angels including Andrej Karpathy and Peter Thiel. Etched's core innovation is two custom-designed components targeting the two phases of LLM inference: a low-voltage prefill chip that packs more transistors by running cooler, and a cluster-scale shared memory system for the decode phase. Contrary to early skepticism, the systems support any AI model architecture including MoE models like DeepSeek and non-transformer designs like Mamba. The company has already booked $1B in orders, with systems currently being tested by major AI companies.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://techcrunch.com/2026/07/23/ai-chip-startup-etched-defies-skeptics-hits-10-3b-valuation-from-big-name-investors>

## Questions this post answers

### What is Etched's new valuation after its latest funding round?

Etched reached a $10.3 billion valuation after closing a $300 million Series C round led by Sequoia, with participation from Andreessen Horowitz, SK Hynix, Jane Street, and Diffusion Capital. This doubles its prior $5 billion valuation from a $500 million round raised about seven months earlier. The company claims this is the highest valuation ever for a Sequoia-led Series C.

_daily.dev tracks funding milestones like this for developers following the AI hardware market._

### Can Etched's chips only run specific AI models like the original transformer-only rumors suggested?

No, Etched's systems run any AI model, not just specific transformer-based ones. They support Mixture of Experts architectures like DeepSeek and Qwen, which split tasks across specialized sub-models, as well as non-transformer designs like Mamba, which uses a state-space model architecture rather than the standard transformer approach.

_Engineers evaluating inference hardware options can follow chip capability claims like this on daily.dev._

### How does Etched's chip design speed up AI inference compared to standard GPUs?

Etched built two custom components: a prefill chip that runs at much lower voltage than typical AI chips to generate less heat and pack in more transistors, and a new memory and interconnect technology called cluster-scale memory that lets many chips share a fast, low-latency memory pool for the decode phase, together delivering higher speeds at lower cost.

_daily.dev helps hardware-curious developers keep up with inference architecture innovations like this._

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- [AI chip startups soak up $1.1B in VC funding this week](https://daily.dev/posts/ai-chip-startups-soak-up-1-1b-in-vc-funding-this-week-sbcfggett) · The Register · 0 upvotes · 0 comments

---

Tags: [#gpu](https://daily.dev/tags/gpu)

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