Breaking LLM Inference Silos
A unified framework for efficient LLM serving across workload types
Niyama introduces a novel approach that unifies LLM inference serving across both interactive and batch workloads, addressing critical inefficiencies in current systems.
- Eliminates resource silos between different LLM workload types
- Enables fine-grained Quality-of-Service (QoS) differentiation
- Improves resource utilization and resilience during traffic surges
- Reduces operational costs through intelligent workload management
This engineering breakthrough matters because it allows organizations to serve diverse LLM applications with varying latency requirements on unified infrastructure, potentially reducing hardware requirements and operational complexity.