Hi NVIDIA team,
I was reading the following blog post:
I’m very interested in the performance and scaling results presented (e.g., agent workflows, multi-agent orchestration, and DGX Spark throughput/latency improvements), and I would like to better understand how to reproduce these results in practice.
Could you please help clarify the following:
- Benchmark methodology
- What specific benchmarks or metrics were used to measure performance (e.g., tokens/sec, latency, throughput, agent task completion time)?
- Are there standardized benchmarking scripts or tools available?
- Reproducibility / setup
- Is there a reference implementation, sample code, or GitHub repo for these experiments?
- What exact software stack was used (e.g., NIM, TensorRT-LLM, specific frameworks, orchestration tools)?
- Workload configuration
- How were the autonomous agents configured (number of agents, task types, orchestration method)?
- Were these single-node DGX Spark tests or distributed setups?
- Step-by-step guidance
- Are there any official guides or best practices to reproduce similar scaling results on DGX Spark?
- For example, starting from a simple agent workflow and scaling to multi-agent systems.
From what I understand, DGX Spark enables running large models locally and supports building agent-based workflows with NVIDIA AI software stack , but I would like to better understand the practical steps to validate the results shown in the blog.
Thanks in advance for your help!