Learning outcomes
- Explain the operating model behind Distributed Inference Parallelism.
- Evaluate trade-offs and failure modes for Distributed Inference Parallelism.
- Apply Distributed Inference Parallelism to a production decision.
Mental model
Parallelism partitions either requests, model computation, layers, or experts; each choice moves the bottleneck between memory, compute, and communication.
Learning outcomes
- Explain the mechanism and ownership boundaries behind Distributed Inference Parallelism.
- Compare the main design alternatives and their operational trade-offs.
- Diagnose common failures and select evidence for a production decision.
Theory
Replicate when one model fits and request parallelism dominates. Partition when weights or per-request compute require multiple devices. Model topology, interconnect, collective cost, placement, and partial failure must be measured together.
Trade-offs
Partitioning enables larger models but makes each request depend on more devices. Replication isolates failures and scales requests but duplicates model memory.
Failure modes and misconceptions
Assuming more GPUs reduce latency; crossing slow network links; mismatched tensor dimensions; no topology-aware placement; correlated replicas; and ignoring collective tail latency.
Decision scenario
A model barely exceeds one GPU's memory while traffic is moderate. Compare quantization, tensor parallelism, and smaller-model routing before choosing a topology.
When is replication preferable to tensor parallelism for serving?
Primary sources
vllm-v0102-model-runnerkubernetes-resource-management
Evidence assessment
Theory and decision mastery
Decision scenario
A production team must adopt Distributed Inference Parallelism while meeting quality, latency, security, and operating constraints.
Which decision process is most defensible?
Relationships
Distributed Inference Parallelism builds on Distributed AI Systems.
Distributed Inference Parallelism informs governed production decisions and review evidence.
Primary sources
- vLLM V1 GPU model runner at v0.10.2 — vLLM Project, verified 2026-07-21
- Kubernetes resource management for Pods and containers — Kubernetes, verified 2026-07-21