· Venture Capital Tracker · investment-strategies · 2 min read
iPronics’ $125M Series B: Optical Circuit Switching for AI Clusters
Maverick Silicon and Light Street Capital co-led iPronics’ $125M Series B — with NVIDIA participating — to scale programmable silicon-photonics switches for AI data-center networks.
VCT data record
Funding event facts
Source-backed financing and transaction details. Unknown terms remain undisclosed rather than estimated.
iPronics raises $125M Series B for optical AI networking
Maverick Silicon and Light Street Capital co-led iPronics’ $125M Series B with NVIDIA participating to commercialize programmable optical circuit switches for AI data centers; total funding $177M.
- Event type
- Funding Round
- Event date
- Sep 2, 2026
- Stage / label
- Series B
- Amount
- $125M
- Confidence
- Company Disclosed
Company / target: iPronics
Lead: Maverick Silicon , Light Street Capital
Participants: NVIDIA , Triatomic Capital , Bosch Ventures , Catalight Capital , European Innovation Council Fund
Sources: unite.ai
Maverick Silicon and Light Street Capital co-led iPronics’ $125 million Series B announced September 2, 2026, with NVIDIA joining Triatomic Capital, Bosch Ventures, Catalight Capital, the European Innovation Council Fund, and existing backers (Unite.AI, Reuters). Total funding reaches $177 million.
Spine: adding GPUs only helps if bits can move between them — optical circuit switching is becoming a first-class AI infra category, not a photonics science project.
Key facts
| Field | Detail |
|---|---|
| Company | iPronics (Valencia, Spain) |
| Round | $125M Series B |
| Co-leads | Maverick Silicon, Light Street Capital |
| Strategic | NVIDIA |
| Total | $177M |
| Product | ONE Series optical circuit switches (32→256 ports roadmap) |
| Mfg | Dedicated Fabrinet line (announced Mar 2026) |
| Customers | First 10 hyperscaler / AI-cluster OEM engagements (company, Mar 2026) |
Who uses the product — and for what job
Users: hyperscalers and AI-cluster OEMs designing scale-up/scale-out GPU fabrics where copper/electronics switching burns power, latency, and complexity.
Job: establish programmable optical paths between accelerators that can reconfigure as workloads change — iPronics cites sub-300-microsecond reconfiguration and sub-30-nanosecond latency on ONE-32 materials — instead of static cabling and ever-larger electronic packet fabrics.
Why now
- Cluster sizes push networking from “plumbing” to a limiter of useful FLOPs.
- March 2026 Fabrinet manufacturing line was the commercialization gate; Series B finances volume and U.S. presence (Santa Clara).
- NVIDIA’s check is the signal: interconnect is part of the GPU system roadmap.
Why Maverick / Light Street / NVIDIA — portfolio fit
Maverick Silicon is already active in AI silicon-adjacent hardware (see also Lyte). Light Street brings public/private markets hardware discipline. NVIDIA invests where networking unlocks more GPU attach.
Likely founder rationale: raise commercialization capital with silicon-specialist leads plus the ecosystem vendor every AI cluster buyer already standardizes on.
Maverick Silicon and Light Street are not in our /fund/ directory.
| Dimension | Fit |
|---|---|
| Stage | Series B commercialization |
| Thesis | Photonic OCS for AI clusters |
| Geography | EU deep-tech → U.S. go-to-market |
| Risk | Ethernet/proprietary AI fabrics; manufacturing yield; long sales cycles |
Competitive map
| Player | Lane |
|---|---|
| Classic Ethernet AI NICs/switches | Packet-centric scale-out |
| Other OCS / silicon photonics startups | Direct category peers |
| Vendor-integrated GPU fabrics | Vertical lock-in alternative |
Takeaway
iPronics’ $125M Series B is a bet that programmable light becomes as strategic as the accelerators it connects. The unresolved proof: volume deployments at hyperscale economics — not lab demos — against incumbent networking roadmaps.
Sources
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Editorial note: AI tools assisted with research, structure, or drafting. Venture Capital Tracker retains human editorial responsibility for factual accuracy, relevance, and source quality before publication.
