15-LLM-Inference-SchedulerHigh-Throughput Continuous Batching & Paged KV-Cache Serving Engine

Iteration-level LLM scheduler, OS-inspired paged virtual memory allocator, and SSE token streaming control plane.

F1: Continuous Batching Engine

Iteration-level scheduling that dynamically merges new prompts and evicts completed sequences at every forward token step.

Virtual memory block allocator dividing GPU attention matrices into 16-token physical pages, reducing memory waste by 90%.

Decouples compute-bound prompt ingestion from memory-bound autoregressive decoding, protecting inter-token latency.

Tracks worker replica health, active memory pressure, and routes requests to the lowest-utilization GPU nodes.

Low-latency Server-Sent Events HTTP streaming delivering tokens to client applications incrementally as generated.

Comprehensive capacity modeling for GPU memory footprint, throughput scaling, and prompt queue management.