Research focus areas
Memory Architecture
CXL-based memory disaggregation for AI inference. KV-cache orchestration and paged attention mechanisms. Memory tiering strategies for heterogeneous AI systems.
Scheduling Algorithms
Gang scheduling, backfill, and fairshare for distributed AI training. Network-topology-aware placement for collective communication workloads.
Storage Systems
Training data pipeline optimization. Distributed checkpoint protocols with fast recovery. Prefetching strategies for large-scale ML training workloads.
Distributed Systems
Collective communication primitives for distributed AI. Control plane design for heterogeneous compute clusters. Consensus and coordination in AI infrastructure platforms.
Research philosophy
Ignara Labs operates on the conviction that lasting advances in AI depend on advances in infrastructure. The field has made extraordinary progress at the model layer. The infrastructure layer has not kept pace.
We conduct research that is grounded in published systems literature, validated empirically, and designed to produce artifacts — prototypes, reference designs, and technical documentation — not just papers.
We publish our findings where appropriate and contribute to the broader infrastructure research community. The hard problems in AI infrastructure are too important to solve in isolation.
Work with Ignara Labs
We welcome conversations with researchers, engineers, and institutions working on related problems in AI infrastructure.
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