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ai-infraTensorRT-LLMTriton Inference ServerLlama 3 8BGPU UtilizationBatch Scheduling

Evaluating Inflight Batching and Scheduler Policies in TensorRT-LLM Triton Backend

Configured inflight batching in TensorRT-LLM Triton backend for Llama 3 8B model, tuning parameters to optimize GPU utilization and balance latency and throughput under bursty loads.

April 20, 2026·1 min read

Summary

Configured inflight batching in TensorRT-LLM Triton backend for Llama 3 8B model, tuning parameters to optimize GPU utilization and balance latency and throughput under bursty loads.

What I Did

Replaced static batching with TRT-LLM inflight batching. Tuned max_num_tokens and batch_scheduler_policy. Compared guaranteed_no_evict vs max_utilization scheduler policies. Monitored queue depth and GPU SM utilization at varying arrival rates.

Key Technical Findings

  • Inflights batching significantly improves throughput compared to static batching.
  • guaranteed_no_evict is preferable for SLA-sensitive APIs, while max_utilization suits batch jobs.
  • High GPU utilization (above 85%) indicates efficient scheduling.
  • Set max_num_tokens to 70-80% of the GPU memory budget.

Commands Used

tritonserver --model-repository=/models
curl -X POST http://localhost:8000/v2/models/ensemble/generate -d '{"text_input":"explain CUDA streams","max_tokens":256}'
python load_test.py --arrival_rate 50 --duration 60 --output_len 256 --concurrency 64
python plot_inflight_stats.py --log triton_metrics.log

Next Steps

  • Implement prometheus metrics scraping from Triton.
  • Test chunked prefill with inflight batching.