The Profiler Capture: A Pivotal Moment in the Kimi-K2.5 Bottleneck Analysis

Introduction

In the long arc of a deep optimization campaign targeting a 1-trillion-parameter Mixture-of-Experts model running on eight PCIe-connected Blackwell GPUs, few single commands carry as much weight as the one found in message 2443. This message, a bash script executed against a running vLLM inference server, represents the culmination of a three-phase benchmarking plan designed to answer a single, pressing question: what is actually consuming the 17.3 milliseconds of each decode step? The answer would upend the team's assumptions about where the bottleneck lay and redirect the entire optimization strategy toward speculative decoding.

The Context: A Campaign to Understand the Bottleneck

The assistant and user had been working for hours to profile the Kimi-K2.5 INT4 model (moonshotai/Kimi-K2.5, compressed-tensors W4A16 with group_size=32) running on 8x NVIDIA RTX PRO 6000 Blackwell GPUs (SM120 architecture, 96GB GDDR7 each). The hardware configuration was unusual and challenging: eight powerful GPUs connected only via PCIe, without the NVLink interconnects that typically bind multi-GPU systems. This meant that every tensor parallelism (TP=8) operation required AllReduce communication across the PCIe bus — a potential bottleneck that the team suspected but had not yet quantified.

The benchmarking campaign had proceeded in careful phases. First came macro-level throughput and latency tests ([msg 2420]), which revealed a troubling plateau: single-request throughput reached about 78 tok/s (TPOT ~12.7ms), and multi-concurrency throughput saturated around 1,536 tok/s at C=128, refusing to improve further. Something was fundamentally limiting the system.

Next came micro-benchmarks of individual GEMM operations at exact Kimi-K2.5 dimensions ([msg 2424]). These measured the Marlin W4A16 dequantization kernels, the MoE gate-up-down projections, and the attention projections. The micro-benchmarks showed that individual operations were fast — the Marlin kernels were particularly efficient, eliminating the dtype-cast overhead that had plagued earlier GLM-5 NVFP4 runs. The GEMMs themselves did not appear to be the bottleneck.

Then came NCCL AllReduce burst measurements ([msg 2426]), which measured the actual communication cost of synchronizing tensors across eight GPUs. These showed that a single AllReduce operation for the hidden_size=7168 tensors took approximately 55 microseconds, and a full burst of 122 operations (one per layer) took 6.81 milliseconds total. This was significant — roughly 40% of the decode step time — but the team needed to see the actual runtime composition, not isolated benchmarks.

The final piece of the puzzle required a full torch.profiler trace captured during live inference. This is precisely what message 2443 accomplishes.

The Message: A Scripted Profiler Capture

The message is a bash script executed on the host machine (not the GPU server itself), sending HTTP requests to the vLLM server running at 10.1.230.174:8000. Let us quote it in full:

# Warmup requests
echo "Sending warmup requests..."
for i in 1 2 3; do
    curl -s http://10.1.230.174:8000/v1/chat/completions \
      -H "Content-Type: application/json" \
      -d '{"model": "/shared/kimi-k2.5-int4", "messages": [{"role": "user", "content": "Say hello"}], "max_tokens": 16, "temperature": 0}' > /dev/null 2>&1
done
echo "Warmup done"

# Start profiler
echo "Starting profiler..."
curl -s -X POST http://10.1.230.174:8000/start_profile
echo ""

# Send single-stream decode requests (batch=1, ~5 decode steps)
echo "Sending profiling requests (C=1, 5 requests of 32 tokens)..."
for i in 1 2 3 4 5; do
    curl -s http://10.1.230.174:8000/v1/chat/completions \
      -H "Content-Type: application/json" \
      -d '{"model": "/shared/kimi-k2.5-int4", "messages": [{"role": "user", "content": "Count from 1 to 30"}], "max_tokens": 32, "temperature": 0}' > /dev/null 2>&1
    echo "  Request $i done"
done

# Stop profiler
echo "Stopping profiler..."
curl -s -X POST http://10.1.230.174:8000/stop_profile
echo ""
echo "Profile capture complete"

The script follows a deliberate structure. First, three warmup requests are sent with a trivial prompt ("Say hello") generating only 16 tokens. This ensures that any lazy initialization, kernel compilation caching, or CUDAGraph capture has already occurred before profiling begins — otherwise the first request would be polluted by one-time overheads. The warmup responses are discarded (> /dev/null 2>&1).

Second, the profiler is started via an HTTP POST to /start_profile. This endpoint was enabled by passing --profiler-config "{\"profiler\": \"torch\", \"torch_profiler_dir\": \"/tmp/vllm_profile\"}" when launching the vLLM server ([msg 2428]). The profiler uses PyTorch's built-in torch.profiler to capture CUDA kernel traces, operator-level timing, and memory events across all eight GPU workers.

Third, five inference requests are sent at concurrency C=1 (single-stream), each asking the model to "Count from 1 to 30" with max_tokens=32. This generates approximately 32 decode steps per request, or about 160 decode steps total across all five requests. The single-stream concurrency is deliberate: it captures the uncontested decode performance without the confounding effects of request batching, which would interleave prefill and decode phases and make the trace harder to interpret.

Finally, the profiler is stopped and the trace files are saved to disk.

The Reasoning Behind the Design

The design of this profiler capture reveals several layers of reasoning by the assistant. First, the decision to use C=1 (single-stream) rather than higher concurrency reflects a desire to isolate the pure decode bottleneck. At higher concurrency, vLLM's scheduler would batch multiple requests together, potentially hiding the per-step overhead behind larger GEMM operations. The team already knew from macro benchmarks that throughput plateaued at high concurrency; the question was why each individual decode step was slow.

Second, the choice of 32 tokens per request (matching the macro benchmark's max_tokens=32) ensures comparability across the three benchmarking phases. The macro benchmarks measured 32-token generations; the micro benchmarks measured individual operations at Kimi dimensions; now the profiler would show how those operations compose in a real 32-token decode sequence.

Third, the warmup phase reflects an understanding of vLLM's execution model. The V1 engine uses CUDAGraphs to capture and replay sequences of CUDA operations, avoiding Python-level scheduling overhead during decode. The first request after server startup typically triggers CUDAGraph capture, which is significantly slower than subsequent requests. By sending warmup requests first, the assistant ensures the profiler captures steady-state performance, not one-time initialization.

Assumptions and Their Validity

The message makes several assumptions, most of which proved correct. It assumes that the vLLM server is running and healthy — confirmed by the preceding health checks ([msg 2441] showed the server responding). It assumes the profiler endpoints (/start_profile and /stop_profile) exist and function correctly — these are part of vLLM's OpenAI-compatible API extension and were verified to be available via --help earlier ([msg 2432]). It assumes that five requests provide sufficient profiling data — in practice, the generated trace files were ~80MB each, containing thousands of kernel events across eight GPUs, which proved more than adequate.

One subtle assumption is that the profiler itself does not distort the measurements. torch.profiler adds overhead to record kernel launches and synchronization events, which can inflate measured times. The assistant implicitly assumes this overhead is small relative to the decode step time (~17ms), which is reasonable for CUDA-level profiling on modern hardware.

A more significant assumption is that single-stream profiling captures the bottleneck relevant to production. The production service would serve many concurrent users, and the assistant had already observed throughput plateauing at C=128. The single-stream profile would reveal the per-step cost, but the interaction between concurrency and AllReduce overhead (which grows with batch size) would require additional analysis.

What Was Created: The Profiler Traces

The output of this message is not visible in the message itself — the curl commands suppress response bodies. However, the subsequent message ([msg 2444]) reveals what was produced:

-rw-r--r-- 1 root root 80933770 dp0_pp0_tp0_dcp0_ep0_rank0.pt.trace.json.gz
-rw-r--r-- 1 root root 80218679 dp0_pp0_tp1_dcp0_ep1_rank1.pt.trace.json.gz
-rw-r--r-- 1 root root 80791338 dp0_pp0_tp2_dcp0_ep2_rank2.pt.trace.json.gz
-rw-r--r-- 1 root root 79609303 dp0_pp0_tp3_dcp0_ep3_rank3.pt.trace.json.gz
...

Eight trace files, each approximately 80MB, one per GPU rank. The naming convention encodes the distributed topology: dp0 (data parallel rank 0), pp0 (pipeline parallel rank 0), tp0 through tp7 (tensor parallel ranks 0-7), dcp0 (data communication parallel rank 0), ep0 through ep7 (expert parallel ranks). This confirms that the model is deployed with TP=8 and EP=8 — each GPU holds a distinct set of expert weights, and MoE routing scatters tokens across experts.

These trace files are the crown jewel of the profiling campaign. They contain the complete CUDA kernel trace for every decode step across all eight GPUs, including kernel names, durations, input sizes, CUDA stream IDs, and memory events. Analyzing these traces would reveal the exact breakdown of decode time — and indeed, the subsequent analysis showed that AllReduce accounted for 51.5% of decode time (11.17ms per step), dwarfing the GEMM operations that the team had initially suspected.

The Thinking Process Visible in the Message

Although the message itself is a straightforward bash script, the thinking process is visible in its structure and in the surrounding conversation. The assistant is executing a carefully planned measurement protocol. The sequence — warmup, start profiler, send requests, stop profiler — mirrors the standard torch.profiler workflow, adapted for a distributed inference server.

The choice of prompt ("Count from 1 to 30") is itself revealing. It is a simple, deterministic task that produces predictable token-by-token generation without branching or tool calls. This ensures the decode steps are uniform and comparable, avoiding the variance introduced by variable-length responses or reasoning traces.

The assistant also chose to suppress all output from the curl commands (> /dev/null 2>&1), keeping the terminal output clean and focused on progress indicators. This is a practical decision for a long-running script, but it means the message does not show the actual model responses — those are irrelevant to the profiling goal.

The Broader Significance

This message sits at the inflection point of the entire optimization campaign. Before it, the team suspected that MoE expert GEMMs (tiny matrix multiplications caused by TP=8 sharding) were the primary bottleneck. After it, the profiler traces would reveal the truth: the GEMMs were fast thanks to Marlin W4A16 kernels, but the PCIe AllReduce was consuming over half of each decode step. This finding would lead the team to abandon further GEMM optimization and pivot to speculative decoding as the most promising software-only path forward.

The message also demonstrates a key principle of performance engineering: measure before optimizing. The assistant had hypotheses (tiny GEMMs are slow, AllReduce is expensive) but refused to act on them without data. The profiler capture was the final, decisive measurement that would validate or refute those hypotheses. In this case, it refuted the GEMM hypothesis and confirmed the AllReduce hypothesis — but only because the measurement was designed correctly, with warmup, controlled concurrency, and full kernel-level tracing.

Conclusion

Message 2443 is, on its surface, a simple bash script that sends a few HTTP requests to a vLLM server. But in the context of the broader optimization campaign, it is the decisive experiment — the measurement that would reveal the true bottleneck and redirect weeks of engineering effort. The script's design reflects careful reasoning about warmup, concurrency, and trace quality. Its execution produced 640MB of profiler traces that would take hours to analyze but would ultimately answer the central question: why is this system not faster? The answer — PCIe AllReduce at 51.5% of decode time — was not what the team expected, but it was the truth they needed to move forward.