Infosys has introduced Topaz Fabric, a new generative AI infrastructure designed to help enterprises scale and secure their AI workloads. Built on open standards and optimized for hybrid cloud, the platform targets complex enterprise environments that require performance, control, and compliance.
Topaz Fabric combines compute, networking, and storage into a cohesive layer that accelerates data preparation, model training, and inference. The launch reflects Infosys strategy to anchor its AI services on a unified, vendor neutral fabric that customers can manage end to end.
Topaz Fabric Core Capabilities
Topaz Fabric brings together infrastructure primitives and orchestration tools to streamline the AI lifecycle. It is engineered to support multi model workloads, diverse data sources, and strict governance requirements typical of large enterprises.
| Feature | Description | Enterprise Impact | Differentiator |
|---|---|---|---|
| Unified Compute Fabric | Aggregates GPUs, CPUs, and accelerators across on premises and cloud | Improves utilization and reduces siloed environments | Single pane of glass for heterogeneous hardware |
| AI Network Fabric | High bandwidth, low latency networking optimized for distributed training | Reduces training time and communication bottlenecks | Congestion aware adaptive routing |
| Data Preparation Layer | Integrated pipelines for cleaning, labeling, and transforming enterprise data | Speeds time to value for model development | Built in data quality and lineage |
| Governance and Compliance | Policy driven controls, audit trails, and role based access | Aligns AI deployments with regulatory and internal standards | Compliance templates mapped to regional frameworks |
| Model Serving and Orchestration | Scalable serving layer for inference with traffic management | Enables consistent performance under variable loads | Auto scaling and cost aware scheduling |
Accelerating Enterprise AI Workloads
Topaz Fabric focuses on reducing friction across data, models, and infrastructure. By unifying resource discovery, scheduling, and performance monitoring, it aims to cut manual effort that typically delays AI initiatives.
Enterprises can use the fabric to run demanding training jobs while maintaining visibility into costs and SLAs. The architecture is designed to scale from pilot projects to production grade deployments without requiring a complete rebuild of existing pipelines.
Integrating with Existing Cloud and On Premises Setups
The platform supports hybrid deployments, allowing organizations to extend their current investments rather than replace them. It connects to popular cloud regions and on premises racks through a consistent control plane.
IT teams can manage nodes, storage, and networks through familiar tools while benefiting from AI specific optimizations. This approach lowers the barrier to adoption and gives flexibility in how and where workloads run.
Security, Compliance, and Risk Management
Security and compliance are embedded into Topaz Fabric from the design phase. Encryption in transit and at rest, fine grained access policies, and detailed audit logs address key enterprise concerns.
Infosys has aligned the platform with major regulatory frameworks, helping customers demonstrate compliance during audits. Organizations gain controls that map to internal risk policies, making it easier to govern AI across business units.
Strategic Roadmap and Adoption Guidance
Infosys positions Topaz Fabric as a long term platform for enterprise AI, with clear milestones for performance, interoperability, and ecosystem expansion. The roadmap emphasizes measurable outcomes for customers.
- Define clear AI objectives aligned to business outcomes before choosing infrastructure
- Start with a controlled pilot that exercises data pipelines and model serving
- Validate performance, security, and compliance requirements in the target environment
- Leverage open standards and APIs to avoid vendor lock in and enable future scaling
- Establish governance policies and operational playbooks early in adoption
FAQ
Reader questions
How does Topaz Fabric improve training efficiency for large models?
Topaz Fabric uses high bandwidth networking and intelligent scheduling to reduce idle time, enabling faster completion of distributed training jobs. Its data preparation layer also cuts preprocessing overhead.
Can Topaz Fabric integrate with existing Kubernetes based AI pipelines?
Yes, the platform is built to interoperate with Kubernetes and common orchestration tools, allowing teams to incorporate it into their current workflows with minimal disruption.
What governance features does Topaz Fabric provide for regulated industries?
It offers policy driven access control, immutable audit trails, data lineage tracking, and compliance templates tailored for financial services, healthcare, and public sector requirements.
Is Topaz Fabric optimized for both cloud and on premises deployments?
Absolutely, the fabric supports hybrid topologies and a unified control plane, enabling consistent management and workload placement across on premises infrastructure and multiple cloud providers.