Distributed inference is the execution of a machine learning model’s forward pass across multiple devices or machines so that models too large or too demanding for a single accelerator can serve predictions. It partitions the model and its computation using strategies such as tensor, pipeline and data parallelism, and coordinates the resulting workers with high-bandwidth interconnects. Distributed inference is essential for serving very large language and vision models at acceptable latency and throughput.
Overview
- As model sizes grow beyond the memory and compute of one accelerator, inference must be spread across several devices, each holding a slice of the model or processing part of the request.
- The challenge is to partition the computation while minimising the communication overhead that arises when slices must exchange intermediate activations.
- Effective distributed inference balances latency, throughput and cost, choosing parallelism strategies that fit the model shape and the available interconnect.
Key aspects
- Tensor parallelism: splitting individual layers’ matrix operations across devices.
- Pipeline parallelism: assigning consecutive layer groups to different devices in stages.
- Communication: collective operations that synchronise partitioned activations.
- State management: distributing the KV Cache for long-context generation.
- Resource orchestration: scheduling and placement across a cluster of accelerators.
Mechanisms
- Tensor Parallelism partitions weight matrices so each device computes a shard and results are combined by collective reductions.
- Pipeline Parallelism streams micro-batches through staged layer groups to keep all devices busy.
- High-bandwidth links carry intermediate tensors between workers with minimal stalling.
- A coordination layer rooted in Distributed Systems handles failure, routing and load balancing.
Applications
- Serving very large Large Language Model and multimodal models at scale.
- High-throughput batched inference behind production APIs.
- Long-context generation requiring partitioned attention state.
- Cost-efficient deployment by spreading a model across commodity accelerators.