White paper

Efficient LLM Inference on Heterogeneous Hardware with TurboNext.ai

Large language model (LLM) inference is crucial for modern Al applications in both research and commercial contexts. While established frameworks like vLLM optimize inference on homogeneous GPU clusters through tensor and pipeline parallelism, strict latency-based SLAs—especially for metrics like time-to-first-token (TTFT) and inter-token latency (ITL)—are difficult to satisfy when deploying across heterogeneous hardware. This white paper introduces TurboNext.ai inference software stack that leverages resource-aware placement, graph partitioning strategies, and SLA-driven dynamic scheduling for CPUs, GPUs, TPUs, and mixed interconnects. The result is optimal hardware usage, cost efficiency, and scalability, with sustained, predictable user-facing latencies.