An inference runtime is the software layer that loads a trained machine-learning model and executes its forward pass to produce predictions on new inputs. It schedules computation across CPUs, GPUs or accelerators, applies graph optimisations such as operator fusion and quantisation, and manages memory, batching and concurrency for low-latency serving. Inference runtimes are the execution engine beneath model-serving infrastructure.
Overview
- Training produces a model graph and weights; the inference runtime is responsible only for executing that graph efficiently against incoming requests.
- Runtimes ingest models in portable formats such as ONNX or framework-native serialisations, build an optimised execution plan and dispatch kernels to the available hardware.
- Key optimisations include operator fusion, constant folding, kernel auto-tuning, mixed-precision and quantisation, all aimed at reducing latency and memory footprint.
- Runtimes expose batching, concurrency and caching controls so a single deployment can sustain high throughput under variable load.
Mechanisms
- Graph loading and validation of the serialised model.
- Hardware backend selection (CPU, CUDA, ROCm, NPU) and kernel scheduling.
- Memory management with workspace allocation and tensor reuse.
- Dynamic batching to amortise per-request overhead.
- Quantisation and precision reduction for accelerator efficiency.
Applications
- Real-time serving of language and vision models behind APIs.
- On-device inference at the edge with constrained runtimes.
- High-throughput batch scoring pipelines.
- Embedded deployment in robotics and autonomous systems.