Triton.
Triton Inference Server is NVIDIA's serving platform supporting multiple frameworks, concurrent model execution on one GPU, dynamic batching and model ensembles, optimised for accelerator utilisation.
Where Triton fits on a long engagement.
Running several models concurrently on one GPU is the capability that changes the economics. Most individual models do not saturate a modern accelerator, and dedicating one per model leaves a large proportion of expensive hardware idle.
Dynamic batching and instance groups are the tuning levers. Multiple instances of a model on one device, with batching across them, can multiply throughput — and the configuration is workload-specific enough that defaults leave a lot unclaimed.
What an assigned team does with Triton.
Getting the most from it requires measurement rather than configuration guesswork. The performance analyser establishes the actual throughput and latency curve for a specific model and hardware combination, which is the only reliable basis for tuning.
Doing that properly is optimisation work with direct hardware cost impact, scoped alongside devops managed services.
What we use Triton for.
- Several models per GPU Concurrent execution, so expensive hardware is not dedicated to one underused model.
- Configuration tuned by measurement Instance groups and batch sizes set from profiling rather than defaults.
- Mixed frameworks on one server PyTorch, TensorFlow and ONNX models served from the same deployment.
How Triton capacity is assigned.
Inference serving is assigned across AI and platform capacity, with configuration tuned by profiling rather than left at defaults.
Tell us what your roadmap needs Triton for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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