# Tune performance Throughput (inferences per second) and latency (time per inference) tuning techniques for Cloud AI platforms are discussed in this section. ## Key performance parameters The following parameters are key parameters that require tuning to achieve the best throughput and latency based on your use case. - Core: Cloud AI Platforms contain multiple AI cores depending on the SKU. EachAI core contains one or more scalar, vector and tensor engines which provide a rich instruction set to accelerate ML operations A model can be compiled for one or more AI cores. - Instance (a.k.a activation): The compiled binary of a model running inferences on a set of AI cores. For example, assume that bert-large compiles for two AI cores and the Cloud AI device has 14 AI cores. In this example, each instance runs on two AI cores. The device can run up to seven instances (on 14 AI cores) in parallel. - Batch size: The number of input elements inferred by an instance. - Set-size: the number of inferences that can be queued up on the host per activation. Set-size helps hide host side overhead by pipelining inferences. Models that require large input/output data to be transferred (from/to host and device) or some preprocessing/postprocessing on the host can see throughput increases with increasing set-size up to a certain value beyond which the device utilization can’t be improved. - Instance and batch size: The product of number of instances and batch size provides the total input samples that can be inferred in parallel on a single Cloud AI device. Note The product of number of instances and number of AI cores used per instance can’t exceed the total number of AI cores available on the Cloud AI platform/card. ## Optimizing for best throughput / least latency [Model Configurator](https://docs.qualcomm.com/doc/80-99100-3/topic/network-performance-tuning.html#reference-to-model-configurator) is a hardware-in-loop test tool that runs through various configurations and identifies the best configuration for a model. Model configurator tool offers two workflows - highest throughput and least latency. See the Performance-Tuning [Tutorial #1 - CV](https://github.com/quic/cloud-ai-sdk/tree/1.20/tutorials/Computer-Vision/Perfomance-Tuning-Beginner) and [Tutorial #2 - NLP](https://github.com/quic/cloud-ai-sdk/tree/1.20/tutorials/NLP/Performance-Tuning-Beginner) for step by step walkthrough for performance tuning. See the Performance tuning tutorial for the workflow for optimizing for best throughput and least latency. For least latency configuration, batch-size should be set to 1. Set-size of 1 provides the least latency. For models that require host side preprocessing, such as CV models, you can define a higher set size to improve throughput significantly with only a slight increase in latency. ## General performance tuning observations Use these general observations to compile models for better performance. ### Throughput and latency vs batch-size For an instance on a fixed number of cores, increasing the batch size from one typically improves throughput. Increasing beyond the optimal batch size causes the performance to drop. ![../../../../../../_images/throughput_latency_bs.png](data:image/png;base64,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) ### Throughput vs cores and instances Based on the total number of AI cores available on the device, a few combinations of cores and instances exist and some of these combinations provide better performance that others. This example uses the standard SKU of the Cloud AI 100 with fourteen AI cores. Throughput for different combinations of cores and instances are shown in the following figure. The batch-size is fixed. For example, C1 x I12 represents the model compiled on 1 core and 12 instances deployed on 12 cores. ![../../../../../../_images/throughput_cores_instances.png](data:image/png;base64,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) ## Next steps - See [Model configurator](https://docs.qualcomm.com/doc/80-99100-3/topic/network-performance-tuning.html#reference-to-model-configurator) for more information about using the model configurator tool to find the optimal compiler and runtime configurations. - Inspect your QPC binary with [qaic-qpc](https://docs.qualcomm.com/doc/80-99100-3/topic/index_qaic-qpc.html#ref-to-qaic-qpc). Last Published: Aug 25, 2026 [Previous Topic Compile the model](https://docs.qualcomm.com/bundle/publicresource/80-99100-3/topics/index_model-compilation.md) [Next Topic Model configurator](https://docs.qualcomm.com/bundle/publicresource/80-99100-3/topics/network-performance-tuning.md)