Volantis raises $88 million Series A to develop photonic AI inference infrastructure. Led by Lachy Groom and Abstract, the round brings Volantis' total funding to $97 million as the company develops optical interconnect technology for high-speed AI inference. Prefer on Google Discover more Hire Engineers AI infrastructure company Volantis has raised $88 million in a Series A funding round led by Lachy Groom and Abstract, bringing the company's total funding to $97 million. Learn AI Tools The company said the financing will support development of a photonics-based system designed to address memory capacity and bandwidth constraints in AI inference. Existing investors and individual backers include Sam Altman, Jeff Dean, Dylan Patel and John Doerr. Targeting high-speed AI inference. Volantis is developing infrastructure designed to run large AI models at higher inference speeds. The company said its system is designed to support models exceeding 10 trillion parameters and inference speeds of up to 10,000 tokens per second per user. Volantis said faster inference could support applications such as real-time AI systems and coding agents that complete tasks more quickly. Addressing the memory wall. The company is focused on what it describes as the "memory wall," a limitation created by the relationship between memory capacity and memory bandwidth in existing computing architectures. According to Volantis, systems based on on-chip SRAM provide high memory bandwidth but have limited capacity, while HBM-based GPUs and TPUs provide greater memory capacity but lower bandwidth. The company is developing an optical interconnect architecture intended to increase both memory capacity and bandwidth. Discover more Earn a CS Degree Learn Physics Volantis said its technology could increase both metrics by more than an order of magnitude while also reducing the cost per token through optical memory pooling and the use of lower-cost memory. Learn Engineering Developing an optical fabric. The company's architecture uses what it calls an "optical fabric," a technology stack designed to support high-bandwidth connections between computing and memory resources. Volantis said its approach uses integrated micro-VCSELs instead of external lasers. The company is also developing alternatives to conventional optical fiber to increase bandwidth density. The company said these technologies are intended to enable larger memory pools and higher data-transfer rates for AI inference workloads. Team with semiconductor experience. Volantis was founded by engineers with previous experience at companies including NVIDIA, AMD, Broadcom and Ayar Labs. The team has worked on semiconductor and photonics technologies including CoWoS packaging, silicon photonics co-packaged optics systems and tunable VCSELs. The company is applying that experience to its photonic infrastructure platform for AI workloads. "As AI agents take on more work, how fast they complete that work will increasingly determine how fast companies can operate," said Tapa Ghosh, CEO and co-founder of Volantis. "Today's hardware forces a tradeoff between running the largest, most sophisticated models and running them fast. We started Volantis to eliminate that tradeoff." Volantis plans to use the new funding to continue developing its photonic architecture and expand its team as it works toward commercializing the technology for AI inference.

Volantis raises $88M to to build photonic memory layer for next-gen AI. October 2, 2026 * Volantis has raised $88 million in Series A funding co-led by Lachy Groom and Abstract Ventures to develop a new photonic architecture for AI inference. * Its A-1 system is designed to run models exceeding 20 trillion parameters at up to 10,000 tokens per second per user, while reducing inference costs. * The San Francisco semiconductor startup plans to deliver its first integrated inference engines to customers in 2027 as AI workloads put increasing pressure on memory infrastructure. Volantis has raised $88 million in Series A funding. The round was co-led by Lachy Groom and Abstract Ventures, with participation from John Doerr, VXI Capital, Triatomic and Susa Ventures. Angel investors including Dwarkesh Patel, Naveen Rao and Sholto Douglas also participated. Volantis will use the funding to develop and commercialise its A-1 inference system, expand its engineering team and move towards customer deployments. The company expects to deliver its first integrated inference engines in 2027. Building A-1 for trillion-parameter models. Volantis was founded by Tapa Ghosh and Roy Meade. Ghosh is a Thiel Fellow and former Y Combinator founder with four patents, while Meade previously led Micron's HBM programme and served as a vice president at Ayar Labs. "As AI agents take on more work, how fast they complete that work will increasingly determine how fast companies can operate," said Tapa Ghosh, CEO and co-founder of Volantis. "Today's hardware forces a tradeoff between running the largest, most sophisticated models and running them fast. We started Volantis to eliminate that tradeoff." Volantis says A-1 is being designed to support models exceeding 20 trillion parameters at up to 10,000 tokens per second per user. These figures represent the company's product targets as it moves towards commercialisation, rather than performance from a deployed system. Rewiring the connection between compute and memory. Large AI models require enormous memory capacity to hold model weights and context, alongside high bandwidth to continuously move that data into compute. Existing architectures typically force a compromise. On-chip SRAM delivers high bandwidth but has limited capacity, while GPUs and other systems using high-bandwidth memory offer substantially more capacity but remain constrained by bandwidth, cost and energy requirements. Volantis argues that even emerging approaches such as 3D DRAM remain on the same underlying trade-off curve. Its solution is a photonic interconnect designed specifically to connect compute chips with memory. The company's optical fabric creates a unified pool of memory, aggregating bandwidth as memory is added. This allows Volantis to increase memory capacity and bandwidth together, rather than scaling one at the expense of the other. The architecture uses custom micro-VCSELs instead of external lasers. Volantis says this approach leverages the established gallium arsenide VCSEL supply chain while avoiding some of the constraints associated with indium phosphide. The company targets end-to-end optical links consuming less than one picojoule per bit. The photonics race heats up. Volantis enters a growing AI infrastructure market attracting major funding. Lightmatter raised $400 million at a $4.4 billion valuation to develop photonic computing and interconnect technologies for AI data centres. Ayar Labs raised $500 million in Series E funding at a $3.8 billion valuation, with its optical interconnect technology designed to tackle data movement between chips. Celestial AI raised $250 million at a $2.5 billion valuation for its Photonic Fabric platform, which targets bandwidth and data-movement bottlenecks in AI infrastructure. Volantis is approaching the problem from the memory side, betting that photonics can help AI systems scale both capacity and bandwidth as models become larger and inference workloads more demanding. With A-1 systems targeted for customer delivery in 2027, the next test will be turning that architecture into a commercially deployable inference platform.