I’m looking for community insights on optimizing AI agents on NVIDIA A100 GPUs for production-scale workloads. Specifically, I’d like feedback on best practices for improving scalability, throughput, and latency.
Key questions:
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Are you using single-GPU, multi-GPU, or multi-node setups?
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How are you leveraging NVLink, MIG, or distributed training frameworks?
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What optimization techniques (TensorRT, FP16/BF16, INT8 quantization) have delivered measurable gains?
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How do you manage GPU memory and batch sizes efficiently?
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Are you deploying via Triton Inference Server or Kubernetes?
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What tools (Nsight, profiling utilities) help monitor performance?
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How do you balance cost vs. utilization?
I’m especially interested in real benchmarks and lessons learned in AI Agent Development on A100 infrastructure.