How do I run batch inference jobs without wasting GPU capacity?

To avoid wasting GPU capacity during batch inference, the goal is to keep the GPU busy continuously while minimizing idle time from data loading, padding, small batches, or poorly sized jobs. A good approach is to optimize at three levels: job sizing, input batching, and runtime execution.

1) Right-size the batch and model workload

• Use the largest batch size that fits in memory without causing OOM.
• If requests vary in length, bucket by similar sequence lengths to reduce padding waste.
• For image/audio tasks, group by similar shapes or durations when possible.
• Avoid launching many tiny jobs; they often underutilize the GPU due to overhead.

2) Pack work efficiently

• Dynamically batch incoming examples into micro-batches.
• Use padding-aware batching so you don’t pad everything to the longest item in the whole dataset.
• For transformer models, use token-based batching instead of example-based batching when sequence lengths vary a lot.
• If you have a large offline dataset, pre-shard it into chunks that match GPU throughput and memory.

3) Keep the GPU fed

• Use multiple dataloader workers and prefetching so CPU I/O doesn’t stall the GPU.
• Store data on fast local storage when possible.
• Prefer pinned memory and asynchronous host-to-device transfers.
• Overlap:
  • CPU preprocessing
  • data transfer
  • GPU compute

4) Use inference-specific optimizations

• Run the model in evaluation mode and disable gradients.
• Use mixed precision if supported:
  • fp16/bf16 often increases throughput significantly.
• Consider quantization if accuracy allows.
• Use optimized runtimes when available:
  • TensorRT
  • ONNX Runtime
  • vLLM / TGI for LLM serving
  • Torch compile / fused kernels

5) Avoid fragmentation and dead time

• Instead of one job per GPU, use work queues and persistent workers so the GPU immediately gets the next batch.
• If one batch is too small to saturate the GPU, combine multiple datasets or streams into a single inference pipeline.
• If batches are large and variable, use length-aware scheduling or dynamic batching.

6) Measure and tune

Track:

• GPU utilization
• memory utilization
• tokens/sec or samples/sec
• dataloader time vs compute time
• average batch latency
• padding ratio

If GPU utilization is low:

• Increase batch size
• Reduce padding
• Increase preprocessing parallelism
• Improve I/O
• Use a more optimized inference engine

Practical rule of thumb

For batch inference, the most common waste comes from:

1. Too-small batches
2. Excessive padding
3. CPU/I/O bottlenecks
4. Jobs not queued back-to-back

A good pattern is:

• shard input data,
• bucket similar-length items,
• dynamically batch to the largest safe size,
• keep a persistent worker on each GPU,
• and profile the pipeline end-to-end.

If you want, I can give you:

1. a PyTorch batch inference template,
2. a scheduler design for multiple GPUs, or
3. a checklist to diagnose low GPU utilization.