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Hudson River Trading invests in Olix

Funding Provisional 95% confidence first seen

Hudson River Trading participated in Olix's $312 million Series B funding round, which valued the London-based AI chip startup at $3.3 billion. The high-frequency trading firm joined other investors including Arm, Netflix co-founder Reed Hastings, and the UK government's Sovereign AI fund in the round led by Fundomo.

The deal

Deal terms as reported in the coverage below.

Decision brief

What changed
Hudson River Trading joined a $312 million Series B funding round for UK-based AI chip startup Olix, alongside Arm, Reed Hastings, and the UK's Sovereign AI fund, valuing the company at $3.3 billion—up from a $1 billion valuation just six months earlier.
Why it matters
The rapid valuation jump and diverse investor base (from a hyperscaler-adjacent chip designer to a sovereign wealth fund) signal strong confidence in decode-accelerator chips as an alternative to HBM-dependent inference hardware, which is relevant for any organization planning AI infrastructure procurement or cost strategy. CTOs evaluating inference cost and supply-chain risk for LLM deployment should track whether Olix's claimed performance (10,000+ tokens/sec/user for 100B-parameter models) materializes at scale, as it could shift near-term AI hardware sourcing options.
Affected roles
CTO CFO CEO
Evidence
Single-source coverage from Trending Topics EU reports the funding round, investor list, and valuation figures; no independent corroboration from other outlets is provided in the given material.
What remains uncertain
The performance claims for the DX-1 chip (10,000+ tokens/sec/user) are self-reported by the company via this single article and have not been independently verified or benchmarked. It's also unclear how 'first...' plans (cut off in the summary) will be executed, and whether production timelines or customer commitments exist.
Monitor next
Watch for independent benchmarking or customer deployment announcements of Olix's DX-1 chip that would validate its performance claims against established GPU/HBM-based inference hardware.

Analytical support, not advice — assumptions and open questions stated above.

Source coverage

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