Heterogeneous Clusters

Table of Contents Introduction Background: Why Latency Matters for LLM Inference Core Challenges in Heterogeneous Multi‑GPU Environments Architectural Foundations 4.1 Model Parallelism 4.2 Pipeline Parallelism 4.3 Tensor Parallelism 4.4 Hybrid Strategies Communication Optimizations 5.1 NVLink & PCIe Topology 5.2 NCCL & Collective Algorithms 5.3 RDMA & GPUDirect 5.4 Compression & Quantization Scheduling, Load Balancing, and Straggler Mitigation Memory Management Techniques 7.1 KV‑Cache Sharding & Offloading 7.2 Activation Checkpointing for Inference Serving Patterns that Reduce Latency 8.1 Dynamic Batching 8.2 Asynchronous Request Pipelines Practical End‑to‑End Example Best‑Practice Checklist Conclusion Resources Introduction Large language models (LLMs) such as GPT‑4, LLaMA‑2, and Claude have moved from research curiosities to production‑grade services. Companies now expose these models through APIs that must deliver sub‑second response times while handling thousands of concurrent users. Achieving low inference latency is especially hard when the model does not fit on a single GPU and must be spread across a heterogeneous multi‑GPU cluster—a mix of different GPU generations, memory capacities, and interconnect topologies. ...

Introduction The rise of large‑scale neural models—such as transformer‑based language models with billions of parameters—has pushed inference workloads beyond the capacity of a single GPU or even a single server. To meet latency, throughput, and cost constraints, organizations increasingly slice models across shards (sub‑models) and spread those shards across a decentralized heterogeneous compute cluster. In such an environment, each shard may run on a different hardware accelerator (GPU, TPU, FPGA, or even CPU) and be managed by distinct orchestration layers (Kubernetes, Nomad, custom edge‑node managers, etc.). ...

Introduction Foundation models—large, pre‑trained neural networks that can be adapted to a wide range of downstream tasks—have exploded in popularity across vision, language, audio, and multimodal domains. Their sheer size (often hundreds of billions of parameters) and the need to process heterogeneous inputs (e.g., text + image + audio) make low‑latency inference a formidable engineering challenge. Enter heterogeneous inference clusters: collections of compute nodes that differ in CPU, GPU, accelerator, memory, and networking capabilities. By intelligently orchestrating these diverse resources, organizations can meet strict Service Level Objectives (SLOs) while controlling cost. ...