Below I answer your two requests one by one, based on the trace: /home/ubuntu/adi/ncprof/ncompass/examples/vllm_example/.nsys_traces/large_traces/nsys_h200_vllm_128x256_TP.nsys-rep

1) Device/stream breakdown: how to focus on the relevant GPU kernel activity

A. First, the “many devices” part is real: you have 4 GPUs

Your trace contains 4 CUDA devices (deviceId 0–3) and they all do substantial work.

From the kernel table aggregated by device:

So: this is multi-GPU tensor-parallel style execution (consistent with your filename TP), where most major kernels appear on all GPUs.

B. Streams: despite “many”, the work concentrates heavily in a few streams

If your goal is “understand what kernels are being launched on the GPUs”, you can ignore most streams and focus on the dominant ones by total kernel time.

Top (deviceId, streamId) by total kernel time:

What this means practically:

• Stream 7 on every GPU is the main compute stream and accounts for the overwhelming majority of kernel time.
• There are also “high-count / low-duration” streams (e.g., 175/223) that run lots of tiny kernels (avg ~8 µs). These are usually auxiliary work (small elementwise, bookkeeping, etc.). Useful later, but not where the big time goes initially.

C. A simple “focus plan” for kernel understanding

When you’re overwhelmed by devices/streams, do this:

1. Pick one representative GPU first (say deviceId=0) since most heavy kernels replicate across GPUs in TP.
2. Filter to the dominant stream (here: streamId=7) to understand the main forward pass.
3. Only after that, look at:
   • NCCL kernels (communication),
   • “tiny kernel streams” (scheduling / quant / cache maintenance),
   • memcpy/memset tables (not covered yet here).