The Silence of the Graph: Debugging CUDAGraph's Missing Replay in vLLM

Introduction

In the long arc of deploying a 744-billion-parameter GLM-5 model on eight Blackwell GPUs, few moments are as quietly revealing as a single grep command. The message at index 2003 in this conversation is a bash one-liner — an SSH command to search a vLLM server log for evidence that CUDAGraph is actually doing its job. Its output is stark: only one match for "CUDAGraph" across the entire log file, and that single match is merely the configuration line announcing that CUDAGraph mode was set to FULL_AND_PIECEWISE. There are no "graph capture" messages, no "graph replay" messages, no evidence that the CUDA graph optimization engine ever executed a single captured kernel.

This message sits at a pivotal moment in the optimization journey. The assistant had just achieved a respectable 43 tok/s single-request throughput — a 2.15x improvement over the 20 tok/s baseline — by enabling CUDAGraph. But the target was 100 tok/s, and a profiling subagent had just reported that 87% of decode time was still consumed by NCCL allreduce operations over PCIe. The assistant needed to understand why. This grep was the diagnostic that exposed a fundamental disconnect between configuration and execution.

The Message

ssh root@10.1.230.174 'grep -c "CUDAGraph" /tmp/vllm_serve_spec.log; grep "CUDAGraph\|graph capture\|graph replay\|Compil" /tmp/vllm_serve_spec.log | tail -30'

Output:

1
(EngineCore_DP0 pid=153096) INFO 02-20 14:10:07 [core.py:98] Initializing a V1 LLM engine (v0.16.0rc2.dev313+g662205d34) with config: model='/shared/glm5-gguf/GLM-5-UD-Q4_K_XL.gguf', speculative_config=SpeculativeConfig(method='ngram', model=None, num_spec_tokens=5), tokenizer='zai-org/GLM-5', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=8192, download_dir=None, load_format=gguf, tensor_parallel...

(Note: the model path and tokenizer name are preserved as they appear in the conversation; no secrets are present in these public identifiers.)

Why This Message Was Written

The assistant was in the middle of a multi-pronged optimization campaign. The previous message ([msg 2001]) had just received a subagent report revealing that NCCL allreduce consumed 87% of the 23ms decode step. The assistant's reasoning, visible in the thinking preceding this message, was: "Wait — does CUDAGraph actually capture NCCL operations?"

This question was the crux of the entire optimization effort. CUDAGraph works by recording a sequence of GPU operations (kernel launches, NCCL calls, memory copies) into a graph that can be replayed with minimal CPU dispatch overhead. If CUDAGraph was capturing NCCL allreduce calls into the graph, then those calls should execute with near-zero overhead — the 127μs per call should be the GPU execution time, not the dispatch overhead. But if CUDAGraph was not capturing those calls, then each of the 158 allreduce calls per decode step would incur full CPU-side dispatch latency, explaining the 20ms NCCL bottleneck.

The assistant's hypothesis was that CUDAGraph might be configured but not actually engaging — perhaps due to incompatibility with the custom MLA attention op, the GGUF dequantization layers, or the speculative decoding configuration. The grep was designed to test this hypothesis with minimal disruption: just read the log file from the running server.

The Thinking Process

The assistant's reasoning, visible in the preceding messages, shows a careful chain of deduction:

1. Benchmark data: CUDAGraph improved throughput from 20 to 43 tok/s. But the improvement was not as dramatic as expected if NCCL overhead was truly eliminated.
2. Profiling data: A subagent reported that NCCL allreduce still consumed 87% of decode time even with CUDAGraph enabled. This was suspicious — if CUDAGraph was working properly, the NCCL overhead should have been dramatically reduced.
3. The contradiction: The assistant had previously seen that CUDAGraph eliminated "the 21ms NCCL allreduce overhead per decode step" ([msg 1993]). But the profiling subagent ([msg 1999]) reported NCCL still taking 87% of time. Which was correct?
4. The diagnostic: The assistant needed to verify whether CUDAGraph was actually capturing and replaying graphs. The simplest way was to check the vLLM server logs for CUDAGraph activity messages. The grep command was carefully crafted: first count total matches (-c), then show the last 30 lines matching any of four patterns: "CUDAGraph", "graph capture", "graph replay", or "Compil" (to catch compilation messages). This would reveal both the volume and nature of CUDAGraph activity.

Assumptions Made

The assistant made several assumptions in this diagnostic:

1. That CUDAGraph activity would be logged: vLLM's CUDAGraph implementation logs graph capture and replay events. This is a reasonable assumption based on the codebase's logging practices.
2. That the log file contained the full server lifetime: The server was started with --disable-log-requests but standard logging was enabled. The log should contain all lifecycle events.
3. That the speculative decoding server was representative: The server being queried was running with ngram speculative decoding (k=5). The assistant assumed CUDAGraph behavior would be similar with or without speculation.
4. That a single grep was sufficient: The assistant assumed that if CUDAGraph was working, there would be multiple "graph capture" or "graph replay" log entries, not just the initialization config line.

Input Knowledge Required

To understand this message, one needs:

• CUDAGraph architecture: Knowledge that vLLM's CUDAGraph implementation captures GPU operations into replayable graphs, and that successful graph capture produces log messages.
• vLLM V1 engine logging: Familiarity with vLLM's logging patterns — that engine initialization logs the full config (including cudagraph_mode), and that subsequent graph captures produce distinct log entries.
• NCCL allreduce overhead: Understanding that in tensor-parallel inference, each layer's output must be synchronized across GPUs via allreduce, and that on PCIe-connected GPUs (without NVLink), this communication is expensive.
• The optimization landscape: Context that the team was pursuing multiple avenues simultaneously — CUDAGraph, speculative decoding, custom allreduce, and NCCL protocol tuning — and needed to prioritize based on bottleneck analysis.

Output Knowledge Created

This message produced a critical piece of negative evidence: CUDAGraph was not actually capturing graphs during inference. The single match was the initialization config line, which merely announced the mode setting. There were zero "graph capture" or "graph replay" messages, indicating that either:

• The CUDAGraph capture was failing silently, or
• The graph was never being triggered (perhaps because the model's dynamic shapes or custom ops prevented capture), or
• The capture happened but produced no log output (unlikely given vLLM's logging patterns). This finding fundamentally changed the optimization strategy. If CUDAGraph wasn't actually capturing graphs, then the 43 tok/s throughput was being achieved through other mechanisms (perhaps piecewise graph mode or simply the absence of --enforce-eager overhead). The NCCL bottleneck was still real and unaddressed.

Mistakes and Incorrect Assumptions

The assistant's assumption that CUDAGraph was working based on the 43 tok/s benchmark was premature. The throughput improvement from 20 to 43 tok/s could have come from other factors:

• Removing --enforce-eager: Even without CUDAGraph capture, disabling enforce-eager allows PyTorch to use its own graph caching and kernel fusion, which can improve throughput.
• Piecewise graph mode: vLLM's FULL_AND_PIECEWISE mode can capture subgraphs even if full-graph capture fails. These piecewise captures might not produce prominent log messages.
• CUDA graph caching in PyTorch: PyTorch's own CUDA graph caching (not vLLM's CUDAGraph) can provide some of the same benefits. The assistant also assumed that a single grep would be definitive. In reality, CUDAGraph capture messages might appear only once during the first decode step, and if the log was truncated or rotated, they might be missed. A more thorough investigation would have involved checking the vLLM source code for CUDAGraph logging patterns, or adding debug logging.

Broader Significance

This message exemplifies a recurring pattern in systems debugging: the moment when a hypothesis is tested against raw evidence and found wanting. The assistant had built a narrative — "CUDAGraph is working, NCCL overhead is eliminated, we need to optimize GPU compute" — but the log file told a different story.

The silence of the graph — the absence of capture and replay messages — was itself a signal. It redirected the optimization effort toward understanding why CUDAGraph wasn't capturing, and toward alternative approaches like NCCL protocol tuning (NCCL_PROTO=LL), which would later yield further improvements to 57 tok/s.

In the broader context of deploying novel models on novel hardware, this message demonstrates the importance of verifying that optimization features are actually engaging. Configuration flags can be set, code paths can be enabled, but only runtime evidence — log messages, profiler traces, benchmark numbers — can confirm that the intended optimization is actually executing. The grep command, humble as it is, remains one of the most powerful tools in the systems debugger's arsenal.