Q: How do you use Triton Model Analyzer to find the mathematically optimal combination of `max_batch_size`, `concurrency`, and `instance_group` counts for a computer vision model under a strict 35ms p99 SLA, and how do you translate the results into cluster capacity sizing?
Using NVIDIA Triton Model Analyzer to systematically profile model throughput against p99 latency constraints, automate batch size selection, and execute data-driven GPU capacity planning.
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🛠️ Production Runbook & Step-by-Step Resolution
Define Model Analyzer Profiling Configuration
Construct a `profile_config.yaml` specifying search spaces: target model, client concurrency range (e.g., 1 to 64), batch size sweeps (1, 2, 4, 8, 16, 32), and instance group configurations (1, 2, or 4 model instances per GPU). Set the hard latency constraint (`latency_budget: 35`).
# profile_config.yaml
model_repository: /models
profile_models:
resnet50_plan:
model_config_search_max_concurrency: 64
model_config_search_max_instance_count: 4
parameters:
dynamic_batching:
max_queue_delay_microseconds: [1000, 3000, 5000]
constraints:
perf_latency_p99: { max: 35 }Execute Automated Empirical Profiling Runs
Run `model-analyzer profile`. Model Analyzer automatically orchestrates Triton server instances, drives synthetic load via Perf Analyzer, collects GPU metrics via DCGM, and charts the Pareto frontier of Throughput (infer/sec) versus p99 Latency (ms).
model-analyzer profile -f profile_config.yaml
model-analyzer report --report-model-gpu-metricsGenerate Capacity Planning Model for Production Cluster Sizing
Analyze the generated report: suppose the optimal configuration achieves 420 inferences/second per GPU while maintaining a 28ms p99 latency (within the 35ms budget). To handle an anticipated production peak load of 5,000 requests/second with N+1 redundancy: Required GPUs = ceil(5000 / 420) + 1 = 13 GPUs. Size the Kubernetes node pool accordingly with confidence.
# Model Analyzer Output Summary:
# Top Config: max_batch_size: 16, instance_count: 2, max_queue_delay: 3000us
# Throughput: 422 infer/sec | p99 Latency: 28.4 ms | VRAM: 4.8 GiB- Guessing batch sizes leads to either wasted GPU capacity or breached latency SLAs.
- We run Triton Model Analyzer to systematically profile models across batch size and concurrency combinations under our strict 35ms p99 SLA.
- The resulting Pareto frontier identified the exact configuration yielding 420 inferences/second per GPU, allowing us to size our production cluster with mathematical precision.