Distributed Inference Serving is the systems engineering discipline of deploying large neural network models across multiple GPUs, multiple machines, or heterogeneous accelerator fleets to serve real-time prediction requests at production-grade latency, throughput, and availability — solving the fundamental problem that frontier models are too large for any single device.

Why Single-GPU Inference Breaks

A 70B-parameter model in FP16 requires 140 GB of VRAM just for weights — more than any single GPU offers. Even models that fit in memory face throughput walls: a single GPU serving a chatbot to 1,000 concurrent users would queue requests for minutes. Distributed inference splits the model and the workload across devices.

Distribution Strategies

• Tensor Parallelism (TP): Each layer's weight matrix is split across GPUs. For a linear layer Y = XW, W is partitioned column-wise or row-wise, each GPU computes its shard, and an all-reduce synchronizes the partial results. Requires fast interconnect (NVLink/NVSwitch) because synchronization happens at every layer.
• Pipeline Parallelism (PP): Different layers are assigned to different GPUs. GPU 0 runs layers 1-20, GPU 1 runs layers 21-40, etc. Request microbatches pipeline through the stages. Higher latency for individual requests but good throughput with many concurrent requests.
• Data Parallelism / Replication: Multiple identical copies of the model serve different requests simultaneously. A load balancer routes incoming requests to the least-loaded replica. Scales throughput linearly with replicas but multiplies memory cost.

Continuous Batching and PagedAttention

Modern inference servers (vLLM, TensorRT-LLM, TGI) use continuous batching: instead of waiting for all requests in a batch to finish, new requests are inserted as soon as any slot opens. PagedAttention (vLLM) manages the KV cache as virtual memory pages, eliminating the massive memory waste from pre-allocated, fixed-length KV cache slots.

Optimization Stack

• Speculative Decoding: A small draft model generates candidate tokens quickly; the large target model verifies them in parallel. When the draft is accurate, multiple tokens are accepted per forward pass, reducing effective latency.
• Quantization: INT8/INT4 quantization halves or quarters the memory footprint, allowing larger batch sizes and reducing inter-GPU communication volume.
• Prefix Caching: For applications where many requests share a common system prompt, the KV cache for the shared prefix is computed once and reused across all requests.

Distributed Inference Serving is the infrastructure layer that makes frontier AI models accessible as real-time services — transforming massive research checkpoints from offline batch-processing artifacts into responsive, concurrent production endpoints.