The Moment of Truth: Retrieving Profiler Summaries in a GPU Inference Bottleneck Hunt

Introduction

In the long and arduous process of optimizing a 1-trillion-parameter Mixture-of-Experts (MoE) language model across eight NVIDIA RTX PRO 6000 Blackwell GPUs, there comes a moment when all the careful instrumentation, the waiting, the micro-benchmarks, and the macro-benchmarks converge into a single decisive data point. Message [msg 2445] in this opencode session captures exactly that moment: the assistant has just confirmed that torch.profiler trace files exist on the remote machine, and now it reaches for the text summaries that will reveal, at kernel-level granularity, where every microsecond of inference time is going.

The message itself is deceptively brief — a single line of excitement followed by a bash command to SSH into the remote server and cat a file. But the weight it carries is immense. This is the payoff for a profiling campaign that spanned multiple phases, required stopping and restarting a production vLLM server, consumed over 30 minutes of model loading time, and involved careful orchestration of warmup requests, profiler start/stop API calls, and file system verification. The assistant writes:

Excellent! We have both trace files and profiler summary text files! Let me get the profiler summaries (they contain the kernel-level CUDA time breakdown):

Then issues the command:

ssh root@10.1.230.174 'cat /tmp/vllm_profile/profiler_out_0.txt'

The Reasoning and Motivation

To understand why this message was written, we must trace the arc of the profiling campaign that preceded it. The session had been engaged in a deep-dive investigation of Kimi-K2.5 INT4 inference performance on an 8x Blackwell GPU system connected only via PCIe — no NVLink. Earlier in the segment ([msg 2397] onward), the user and assistant had debated whether overlapping AllReduce with computation was feasible, explored disaggregated prefill and data parallelism options in vLLM and SGLang, and conducted extensive research into SM120 GEMM optimization strategies including Marlin kernels, L2 cache pinning, and persistent fused kernels.

This research phase led to a three-part benchmarking plan: macro-level throughput and latency tests against the running vLLM server (completed in [msg 2420]), micro-benchmarks of individual GEMM operations at exact Kimi-K2.5 dimensions (completed in [msg 2424]), NCCL AllReduce burst measurements (completed in [msg 2426]), and finally — the crown jewel — a full torch.profiler capture of the actual vLLM decode loop running on real hardware with real model weights.

The macro benchmarks had already revealed a throughput plateau at approximately 1,536 tokens per second with concurrency level 128, suggesting a fundamental bottleneck. The micro-benchmarks had measured individual GEMM operations at the precise dimensions used by the Kimi-K2.5 model with TP=8 sharding (gate_up N=512, down K=256, hidden_size=7168, moe_intermediate_size=2048). The NCCL benchmarks had measured AllReduce burst timing at 6.81ms total for 122 operations at batch size 1. But none of these isolated measurements could tell the full story of where time was actually being spent inside the running inference engine. Only the torch.profiler could provide that integrated view.

The motivation for message [msg 2445] is therefore straightforward: the assistant has completed the data collection phase and is now transitioning to data analysis. The profiler trace files (.pt.trace.json.gz files, each approximately 80MB across 8 ranks) were confirmed to exist in [msg 2444]. Now the assistant needs the text-format summaries (profiler_out_0.txt through profiler_out_7.txt) which contain the aggregated kernel-level CUDA time breakdown — the "Self CUDA %" and "CUDA total" columns that would reveal the true bottleneck.

The Decision-Making Process

While this message contains only one explicit decision — "let me get the profiler summaries" — that decision rests on several implicit judgments made throughout the preceding messages.

First, the assistant decided to use vLLM's built-in --profiler-config flag rather than an external profiling tool. This was established in [msg 2428] when the assistant launched vLLM with --profiler-config "{\"profiler\": \"torch\", \"torch_profiler_dir\": \"/tmp/vllm_profile\"}". This choice was significant because it meant profiling would be integrated into the serving loop itself, capturing the exact same code path that production requests would follow, rather than running synthetic benchmarks in isolation.

Second, the assistant chose to profile at batch size 1 (single-stream decode) rather than at higher concurrency. The profiling requests sent in [msg 2443] were five sequential requests of 32 tokens each, with no concurrent load. This was a deliberate simplification: at batch size 1, the decode step is dominated by the model's forward pass and AllReduce communication, without the complication of scheduling multiple requests or batching effects. The assumption was that the single-stream profile would reveal the fundamental per-token latency breakdown, and that multi-request behavior could be extrapolated from there.

Third, the assistant decided to retrieve the text summary files (profiler_out_*.txt) rather than the full JSON trace files. The JSON traces are approximately 80MB each and contain raw event data that would require significant processing to extract meaningful aggregates. The text summaries, by contrast, are generated by vLLM's profiler integration and provide a pre-computed aggregation of CUDA kernel time by name, including "Self CUDA %" — the percentage of total CUDA time spent in each kernel, excluding child operations. This is exactly the information needed to identify bottlenecks at a glance.

Assumptions and Their Validity

Several assumptions are embedded in this message and its surrounding context.

The assistant assumes that the profiler output files exist and are accessible at the expected path (/tmp/vllm_profile/profiler_out_0.txt). This is a reasonable assumption given that [msg 2444] confirmed the presence of .pt.trace.json.gz files in that directory, and vLLM's profiler integration generates both formats. However, there was a subtle risk: the profiler API endpoints (/start_profile and /stop_profile) returned empty responses in [msg 2443], which could have indicated a failure mode. The assistant wisely checked for file existence before assuming success.

The assistant assumes that the profiler captured useful data — that the warmup and profiling requests actually triggered the model's decode loop and that the profiler recorded meaningful CUDA kernel events. This assumption was validated by the file sizes (~80MB per rank), which indicate substantial event data was captured.

A deeper assumption is that single-stream profiling at batch size 1 would reveal the dominant bottleneck. This turned out to be correct — the profiler data would later show ([msg 2446] onward) that AllReduce accounted for 51.5% of decode time, or 11.17ms per step. But the assistant could not have known this in advance. The earlier hypothesis, formed during the research phase, was that the tiny MoE expert GEMMs caused by TP=8 sharding would be the primary bottleneck. The profiler would prove this hypothesis wrong in a spectacular way.

Input Knowledge Required

To fully understand this message, one needs knowledge of:

  1. The profiling infrastructure: vLLM's --profiler-config flag, the torch.profiler integration, the distinction between JSON trace files and text summary files, and the API endpoints for starting and stopping profiling.
  2. The hardware topology: Eight RTX PRO 6000 Blackwell GPUs connected via PCIe without NVLink, which constrains inter-GPU communication bandwidth and makes AllReduce a potential bottleneck.
  3. The model architecture: Kimi-K2.5 is a 1T-parameter MoE model with specific hidden dimensions (7168), intermediate sizes (2048), and TP=8 sharding that produces very small expert GEMMs (N=512, K=256).
  4. The preceding benchmark results: The macro benchmarks showing throughput plateau at ~1,536 tok/s, the micro-benchmarks of individual GEMM operations, and the NCCL AllReduce burst measurements at 6.81ms.
  5. The session history: The earlier pivot from GLM-5-NVFP4 to Kimi-K2.5 INT4, the resolution of FP8 KV cache incompatibility on SM120, and the systemd service deployment.

Output Knowledge Created

This message produces the raw profiler data that will drive the next phase of analysis. The profiler_out_0.txt file contains a table with columns including "Name" (kernel name), "Self CPU %", "Self CPU", "CPU total %", "CPU total", "CPU time avg", "Self CUDA", "Self CUDA %", "CUDA total", "CUDA time avg", and "# of Calls". This data will be parsed in [msg 2446] by a Python analysis script (analyze_k25_profile.py) that will compute the percentage breakdown of CUDA time by kernel category.

The key insight that emerges from this data — that AllReduce dominates at 51.5% of decode time — will fundamentally reshape the optimization strategy. It will rule out GEMM-level optimizations (Marlin kernels, L2 cache pinning) as insufficient, and will force the team to consider architectural solutions: expert parallelism (EP), disaggregated prefill, or — the path ultimately chosen — speculative decoding.

The Thinking Process

The assistant's thinking in this message is visible in its structure and phrasing. The opening "Excellent!" conveys genuine excitement — after approximately 20 minutes of waiting for vLLM to load the model weights and compile CUDA graphs, the profiler has finally produced results. The parenthetical "(they contain the kernel-level CUDA time breakdown)" reveals the assistant's mental model: it knows exactly what information it needs and where to find it. The text summaries are not just files; they are the answer to a question that has been driving the entire session.

The assistant also demonstrates awareness of the multi-rank profiling setup. The command cats only profiler_out_0.txt (rank 0), but the assistant knows that all 8 ranks produced output. The decision to start with rank 0 is pragmatic — the kernel-level breakdown should be similar across ranks for a TP=8 configuration, and one file is sufficient for the initial analysis.

Mistakes and Limitations

One potential limitation is that the assistant did not verify the profiler configuration was correctly applied before sending requests. The --profiler-config JSON argument was passed through the shell without proper quoting, which could have caused parsing issues (as hinted by the empty responses from the API endpoints in [msg 2443]). Fortunately, the files were generated anyway, suggesting the profiler was active.

Another limitation is the single-batch profiling approach. While this revealed the AllReduce bottleneck clearly, it may have masked other bottlenecks that only appear at higher batch sizes, such as memory bandwidth saturation or scheduler overhead. The macro benchmarks partially address this gap, but a multi-batch profiler capture would have provided a more complete picture.

Conclusion

Message [msg 2445] is a brief but pivotal moment in a complex optimization journey. It represents the transition from data collection to data analysis, from hypothesis to evidence. The profiler summaries it retrieves will overturn the team's assumptions about where time is being spent and will redirect the optimization effort toward communication rather than computation. In the broader narrative of deploying a 1T-parameter MoE model on PCIe-connected Blackwell GPUs, this message is the point at which the true bottleneck is finally unmasked.