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Olix (Olix Computing Ltd) — Research Notes

Compiled 2026-08-10

Company overview

  • London chip startup, founded March 2024 by James Dacombe (b. 2000, Harrogate; second-time founder — also founded CoMind, non-invasive brain-monitoring neurotech).
  • Building rack-scale AI inference systems, designed end to end: chips, lasers, and the network connecting them.
  • Two contrarian bets:
    1. No HBM — on-chip SRAM instead (closer in spirit to Groq/Cerebras).
    2. No copper interconnect — a novel photonic interconnect links the chips.
  • Also skips advanced packaging (no CoWoS-style interposers) — deliberately avoids the parts of the supply chain with multi-year queues.
  • First chip: DX-1 (X-1 platform) — a decode accelerator for the output-generation stage of LLM inference. Claim: >10,000 tokens/sec/user on ~100B-parameter models, better throughput/watt than general-purpose GPUs. Each chip in the platform is specialized for one stage of the inference pipeline.
  • First customer access targeted H2 2027 (pre-silicon as of Aug 2026).

Funding

Round Date Amount Valuation
Series A Feb 2026 $220M ~$1B
Series B Aug 2026 $312M $3.3B
  • Series B is the largest chip funding round in European history (~$530M total raised).
  • Backers: Arm, Hudson River Trading, Fundomo, Reed Hastings (Netflix co-founder), UK government's Sovereign AI venture fund.
  • Key people: Prof. Nick McKeown (Stanford, ex-Intel SVP) joined the board; Matt Briers (ex-Wise CFO) is CFO.

Patents

No company-published list exists. Google Patents (applicant = "Olix Computing") shows two published applications, both PCT/GB filings, same inventor, same priority date — filed ~5 months after founding:

Publication Title Priority Filed Published Inventor
WO2026033226A1 Method and system for optical computing 2024-08-08 2025-08-08 2026-02-12 Dawid Borycki
WO2026033227A1 Method and system for optical computing 2024-08-08 2025-08-08 2026-02-12 Dawid Borycki
  • WO2026033226A1 = PCT/GB2025/051752. Classifications: G02F1/21, G02F1/225, G02F1/212 (Mach-Zehnder-type interference optics), G02B6/28 (optical coupling/bus waveguides).
  • Substance (226): an optical data combiner using frequency-swept coherent light — light split into two paths, each phase-modulated by an input data stream, one path delayed, then recombined; the interference pattern encodes the combination and can implement logic gates and arithmetic (add/subtract/multiply/divide). Notably this is genuine optical computing, not just interconnect.
  • Both have GB counterparts; legal status pending.
  • Caveat: applications publish 18 months after filing, so anything filed after ~Feb 2025 is still invisible. Re-check Google Patents for "olix computing" every few months.
  • Local copies: WO2026033226A1.pdf / WO2026033227A1.pdf (full text incl. description + claims, rendered from Google Patents pages; the .html files are the originals). Official WO pamphlet PDFs were not yet available on Google Patents / WIPO as of this date.

Comparison vs Lightmatter

Olix Lightmatter
Founded 2024, London 2017, MIT spinout (Boston)
Valuation $3.3B (Aug 2026) $4.4B (Series D, Oct 2024)
Total raised ~$530M ~$850M
Stage Pre-silicon; customers H2 2027 Passage interconnect (32–64 Tbps CPO) shipping 2026; Synopsys/Cadence integrations; in Nvidia NVLink Fusion ecosystem
Model Vertically integrated inference systems — competes with Nvidia Sells photonic interconnect into the GPU ecosystem — partners with Nvidia
Photonics role Interconnect fabric (compute is electronic/SRAM) Interconnect (pivoted away from photonic compute / Envise)

Similar valuation league, very different risk: Lightmatter is ~FEED-complete with vendors signed; Olix is a well-funded conceptual design package. Market prices them similarly because Olix's addressable prize (whole systems) is larger.

Why they moved so fast

  1. Narrow problem — decode-only accelerator, not a general GPU; small design space per chip.
  2. Design choices dodge slow supply chains — no HBM, no advanced packaging; novelty concentrated in the interconnect, individual dies comparatively conventional.
  3. Capital + talent compress schedules — $530M in 18 months, veteran hires, second-time founder; peak inference demand + UK sovereign-AI push meant money arrived fast.
  4. Calibration — the fastest-moving thing is capital formation; performance figures are projections, no shipped silicon. Cf. Graphcore at the same stage.

Sources