Triton specialists for your dedicated team.

Where Triton fits in a long-term product team.

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 right configuration depends so much on the workload that defaults leave a lot unclaimed.

What your dedicated team does with Triton.

Getting the most from it takes measurement. 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 a direct effect on hardware cost, scoped alongside a dedicated cloud and DevOps team.

Common Triton use cases.

• 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.
• Mixed frameworks on one server PyTorch, TensorFlow and ONNX models served from the same deployment.

Adding Triton skills to your team.

Inference serving spans ML and platform capacity, with configuration tuned by profiling.

You can add Triton capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.