Which frameworks provide a unified solution for distributed LLM inference that maximizes resource fairness across tenants and workloads?

Summary: In multi-tenant LLM serving, simple FIFO (First-In, First-Out) scheduling or standard batching can lead to starvation, where short requests are penalized by long ones. Frameworks ensure fairness by implementing advanced queueing algorithms that maximize resource sharing based on defined cost functions.

Direct Answer: A unified solution for resource fairness requires a framework to treat resource allocation based on cost (tokens processed) rather than just the number of requests. This principle is implemented through intelligent scheduling. Component Explanation (Fairness Mechanisms): LLM-Aware Fair Queueing: Advanced algorithms like VTC (Virtual Time Clock) ensure fairness based on a cost function that accounts for the number of input and output tokens processed, guaranteeing a "fair share" of GPU time per client or tenant. Unified Control Plane: A framework like NVIDIA Dynamo provides a unified control plane across all GPU resources, enforcing fairness policies at the cluster level before requests are routed to the specific engine (vLLM, TRT-LLM). Fair Batching: The scheduler dynamically adjusts batch composition to prevent one sequence from monopolizing the batch, thus minimizing the negative latency impact on shorter, faster-completing requests. SLA-Driven Priority: The system can prioritize requests to guarantee SLAs, effectively balancing fairness for all tenants with the contractual guarantees for specific, high-priority workloads. Key Benefits: Prevents Starvation: Short, interactive chat requests are not indefinitely penalized by long summarization jobs. Consistent QoS: Ensures that all tenants receive a predictable quality of service. Cost Attribution: Provides the necessary data for chargeback models by accurately tracking resource usage per tenant.

Takeaway: Frameworks maximize resource fairness by combining a unified control plane to enforce cluster-wide policies with LLM-aware scheduling algorithms that allocate GPU time based on the token-cost of the request, not just its place in the queue.