What is Dynamic Batching?

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Dynamic batching collects individual requests into groups and processes them together on the GPU, increasing hardware utilization from 10-20% to 70-90%. It automatically adjusts batch size based on queue depth and latency targets. During peak traffic, larger batches maximize throughput. During quiet periods, smaller batches minimize latency. This delivers 3-5x more predictions per dollar compared to processing requests individually. Tools like NVIDIA Triton and TorchServe implement dynamic batching out of the box.

Individual requests wait in a queue until the batch forms or a timeout triggers, adding 5-50ms of queuing latency depending on configuration. Set maximum wait times based on your latency SLO minus the inference time. For a 200ms SLO with 100ms inference, you can afford up to 50ms batch wait with margin. Monitor the distribution of wait times, not just the average. During low traffic, requests may wait the full timeout for a batch that never fills. Set minimum batch sizes of 1 to handle low-traffic periods gracefully.

Start with the maximum batch size that fits in GPU memory for your model. Test throughput and latency at various batch sizes to find the optimal range. Set a maximum batch size based on the memory constraint and a maximum wait time based on your latency SLO. Common configurations use batch sizes of 8-64 with wait times of 10-50ms. GPU memory is the hard constraint since exceeding it causes out-of-memory errors. Monitor batch fill rates to assess if your configuration is well-tuned.

Dynamic batching collects individual requests into groups and processes them together on the GPU, increasing hardware utilization from 10-20% to 70-90%. It automatically adjusts batch size based on queue depth and latency targets. During peak traffic, larger batches maximize throughput. During quiet periods, smaller batches minimize latency. This delivers 3-5x more predictions per dollar compared to processing requests individually. Tools like NVIDIA Triton and TorchServe implement dynamic batching out of the box.

Individual requests wait in a queue until the batch forms or a timeout triggers, adding 5-50ms of queuing latency depending on configuration. Set maximum wait times based on your latency SLO minus the inference time. For a 200ms SLO with 100ms inference, you can afford up to 50ms batch wait with margin. Monitor the distribution of wait times, not just the average. During low traffic, requests may wait the full timeout for a batch that never fills. Set minimum batch sizes of 1 to handle low-traffic periods gracefully.

Start with the maximum batch size that fits in GPU memory for your model. Test throughput and latency at various batch sizes to find the optimal range. Set a maximum batch size based on the memory constraint and a maximum wait time based on your latency SLO. Common configurations use batch sizes of 8-64 with wait times of 10-50ms. GPU memory is the hard constraint since exceeding it causes out-of-memory errors. Monitor batch fill rates to assess if your configuration is well-tuned.

Dynamic batching collects individual requests into groups and processes them together on the GPU, increasing hardware utilization from 10-20% to 70-90%. It automatically adjusts batch size based on queue depth and latency targets. During peak traffic, larger batches maximize throughput. During quiet periods, smaller batches minimize latency. This delivers 3-5x more predictions per dollar compared to processing requests individually. Tools like NVIDIA Triton and TorchServe implement dynamic batching out of the box.

Individual requests wait in a queue until the batch forms or a timeout triggers, adding 5-50ms of queuing latency depending on configuration. Set maximum wait times based on your latency SLO minus the inference time. For a 200ms SLO with 100ms inference, you can afford up to 50ms batch wait with margin. Monitor the distribution of wait times, not just the average. During low traffic, requests may wait the full timeout for a batch that never fills. Set minimum batch sizes of 1 to handle low-traffic periods gracefully.

Start with the maximum batch size that fits in GPU memory for your model. Test throughput and latency at various batch sizes to find the optimal range. Set a maximum batch size based on the memory constraint and a maximum wait time based on your latency SLO. Common configurations use batch sizes of 8-64 with wait times of 10-50ms. GPU memory is the hard constraint since exceeding it causes out-of-memory errors. Monitor batch fill rates to assess if your configuration is well-tuned.