TRISH pioneers silicon photonics technology that replaces electrical interconnects with light, enabling AI systems to communicate at unprecedented speeds while consuming a fraction of the energy.
Explore our technologyFlagship Technology
Our flagship photonic interconnect platform leverages fiber optics to create ultra-high-bandwidth links between GPUs, achieving data transfer rates of up to 30 terabits per second. This revolutionary approach enables over 1,000 GPUs to operate synchronously within a single rack, eliminating the bandwidth bottlenecks that have historically constrained AI training at scale. The platform integrates seamlessly with existing data center infrastructure while delivering 10x improvement in interconnect bandwidth density compared to electrical alternatives.
Our hybrid electronic-photonic computing chip represents a fundamental rearchitecting of AI inference hardware. By performing matrix multiplications and attention mechanisms using photonic circuits, the chip achieves dramatic energy efficiency gains while maintaining computational precision. The design leverages the inherent parallelism of light—multiple wavelengths carry independent computations simultaneously through the same waveguide, enabling throughput that scales without proportional power increases. This technology is specifically optimized for transformer-based models, large language models, and computer vision inference at the edge and in cloud deployments.
Performance Metrics
Interconnect Bandwidth
Maximum aggregate bandwidth per fiber bundle, achieved through dense wavelength-division multiplexing with 128 channels operating simultaneously. This represents a 10x improvement over state-of-the-art electrical interconnects and enables true all-to-all GPU communication without bandwidth bottlenecks.
Cluster Density
Synchronous operation of over one thousand GPUs within a single rack, maintaining sub-microsecond latency across all connections. This density enables training runs that previously required multiple racks of equipment, dramatically reducing infrastructure footprint and operational complexity.
Power Efficiency
Greater than 50% reduction in power consumption compared to equivalent GPU-based systems performing identical inference workloads. Photons generate negligible heat during propagation, eliminating the resistive losses inherent to electrical interconnects and reducing cooling requirements proportionally.
End-to-End Latency
Sub-100 nanosecond latency for chip-to-chip communication, approaching the physical limits imposed by the speed of light. This ultra-low latency enables real-time synchronization across massive GPU clusters, critical for maintaining training efficiency at scale.
WDM Capacity
Dense wavelength-division multiplexing enables 128 independent data channels per fiber, each operating at 200+ Gbps. This spectral efficiency maximizes the information density carried by each physical connection, reducing cable count and simplifying data center topology.
Reliability
Five-nines reliability achieved through redundant optical paths, automatic failover mechanisms, and continuous health monitoring. Photonic components have no moving parts and are immune to electromagnetic interference, providing inherent reliability advantages over electrical alternatives.
How It Works
TRISH technology leverages silicon photonics—the integration of optical components directly into silicon chips using standard semiconductor manufacturing processes. This approach enables mass production at scale while achieving performance that discrete optics cannot match.
Capabilities
Modular design enables seamless scaling from hundreds to tens of thousands of GPUs. Add capacity without redesigning network topology or suffering degraded performance. Our non-blocking switch architecture maintains full bisection bandwidth regardless of scale, ensuring that adding nodes never impacts existing workload performance. The system supports hot-swappable components for zero-downtime expansion and maintenance operations.
Guaranteed sub-microsecond latency with minimal jitter across all connections. Critical for distributed training synchronization and real-time inference applications. Our cut-through switching architecture eliminates store-and-forward delays, while dedicated optical circuits provide predictable timing that enables precise gradient synchronization in distributed deep learning frameworks like PyTorch and TensorFlow.
Compatible with existing data center infrastructure and standard GPU accelerators from NVIDIA, AMD, and Intel. No changes required to training frameworks or application code. Our transceivers use industry-standard QSFP-DD and OSFP form factors, connecting directly to existing network interfaces. Software integration through standard RDMA over Converged Ethernet (RoCE) and InfiniBand protocols ensures immediate compatibility.
Comprehensive monitoring of optical power, wavelength stability, bit error rates, and thermal conditions across every link. Predictive analytics identify potential failures before they impact operations. Our management platform integrates with standard data center infrastructure management (DCIM) tools and provides RESTful APIs for custom automation. Per-port power monitoring enables precise energy accounting.
Pre-configured systems ship ready for rack installation with plug-and-play fiber connectivity. Full cluster activation in days rather than months. Factory-terminated fiber assemblies eliminate field splicing, while automated configuration tools discover network topology and optimize routing without manual intervention. Our deployment team provides on-site installation support and validation testing.
Hardware-enforced traffic isolation enables secure multi-tenant deployments without performance interference. Perfect for cloud providers serving multiple enterprise customers. Wavelength-level isolation provides cryptographic-grade separation—traffic on different wavelengths cannot interact physically. Quality-of-service guarantees ensure that tenant workloads receive committed bandwidth regardless of network utilization.
Use Cases
Train frontier models with hundreds of billions of parameters on clusters of thousands of GPUs. Our interconnect eliminates the communication bottleneck that limits scaling efficiency in distributed data-parallel and model-parallel training configurations. Achieve near-linear scaling to unprecedented cluster sizes while maintaining training stability through deterministic gradient synchronization.
Serve billions of inference requests with consistent sub-millisecond latency. The TRISH inference engine enables real-time responses for interactive AI applications including conversational agents, recommendation systems, and autonomous decision-making. Energy efficiency reduces cost-per-inference by more than half compared to GPU-only deployments while improving response time consistency.
Purpose-built for cloud service providers managing exponentially growing AI workloads. Our technology addresses the power density and cooling constraints that limit traditional data center expansion. Deploy more compute capacity within existing facility power envelopes while reducing total cost of ownership through lower energy consumption and simplified infrastructure management.
Accelerate climate modeling, drug discovery, genomics analysis, and physics simulations with computing clusters that scale beyond previous limits. The combination of high bandwidth and low latency enables algorithms that require tight coupling between compute nodes—perfect for molecular dynamics, computational fluid dynamics, and other HPC workloads transitioning to AI-accelerated methods.
Deploy sophisticated AI models at the edge with our energy-efficient inference chips. Enable real-time computer vision, natural language processing, and predictive analytics in power-constrained environments. Ideal for autonomous vehicles, smart infrastructure, industrial IoT, and mobile devices where thermal and power budgets are critical constraints on AI capability.
Power next-generation AI systems that combine language, vision, audio, and other modalities. These models require massive parameter counts and complex cross-attention mechanisms that stress interconnect bandwidth. TRISH technology enables training and deployment of multi-modal foundation models that would be impractical with conventional infrastructure.
Performance Analysis
TRISH was founded by a team of MIT alumni who recognized that the exponential growth of artificial intelligence was on a collision course with the physical limits of electrical interconnects. Traditional copper-based data transmission cannot scale to meet the bandwidth demands of next-generation AI systems without consuming unsustainable amounts of energy.
We are building the infrastructure layer that will power the AI revolution. Our silicon photonics technology represents a fundamental shift in how computing systems communicate—replacing electrons with photons to achieve speeds and efficiencies that electronics alone cannot match. By integrating optical components directly into silicon chips using standard semiconductor manufacturing, we bring the transformative potential of photonics to mainstream computing.
Our technology targets the hyperscale cloud providers who manage the world's AI infrastructure. These organizations face an urgent challenge: the power consumption of AI training and inference is growing faster than their ability to build new data centers. TRISH provides a path forward—delivering dramatic improvements in performance and efficiency that enable continued AI scaling within sustainable power envelopes.
We believe that the transition from electrical to optical computing is inevitable. The question is not whether it will happen, but who will lead it. At TRISH, we are committed to making light the foundation of intelligent computing, enabling AI systems that are faster, more efficient, and more capable than anything possible with electronics alone.◆
Get in Touch
Whether you're planning a new AI cluster deployment, evaluating interconnect technologies, or exploring how silicon photonics can address your specific challenges, our team is ready to help. Reach out to discuss your requirements and learn how TRISH technology can accelerate your AI initiatives.