The Clean Slate: Restoring Order After Infrastructure Disruption in a Benchmarking Pipeline

Message 11283 — A Pivotal Moment of Recovery and Resumption

Model downloaded in 76s. Now let me clean up the bad results and resume benchmarks.

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``bash # Remove broken results (b8 and b12 had 0 tok/s -- service started but DDTree crashed) # Also remove tp1-b15 if it exists (was mid-run when aborted) rm -f bench_results/tp1-b8.json bench_results/tp1-b12.json bench_results/tp1-b15.json echo "Remaining results:" ls bench_results/ ``

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Remaining results: tp1-auto.json tp1-linear.json

At first glance, message 11283 appears to be a mundane housekeeping operation: deleting a few JSON files and listing what remains. But this brief exchange represents a critical inflection point in a complex, multi-phase benchmarking session. It is the moment when the assistant transitions from crisis recovery back to productive execution, drawing a clean line between corrupted data and valid results. To understand why this message matters, we must trace the chain of events that led to it and examine the reasoning, assumptions, and infrastructure knowledge embedded in this seemingly simple act of file cleanup.

The Road to Ruin: Infrastructure Collapse Mid-Benchmark

The context leading to message 11283 is a story of ambitious benchmarking abruptly interrupted by forces beyond the assistant's control. The session had been running a comprehensive evaluation of the Qwen3.6-27B model on CT200, an 8-GPU machine equipped with RTX PRO 6000 Blackwell GPUs. The benchmark plan called for a sweep of DDTree speculative decoding budgets (b8, b12, b15, b16, b32, b64) at multiple token generation lengths, comparing them against an autoregressive baseline and a DFlash linear baseline.

The benchmarks had been making excellent progress. The autoregressive baseline (tp1-auto) completed successfully, and the DFlash linear baseline (tp1-linear) showed strong results. The DDTree budget sweep was underway when disaster struck: the machine went down for "networking infrastructure maintenance" ([msg 11274]). When the assistant attempted to resume, it discovered the situation was far worse than a simple network outage. The machine had been fully rebooted ([msg 11275]). Every GPU showed 0 MiB of memory usage. All systemd services were inactive. And critically, the model that had been stored in /dev/shm — a tmpfs filesystem that is cleared on every reboot — was gone.

This was a significant setback. The Qwen3.6-27B model is a 52 GB download, and the assistant had to re-download it from HuggingFace before any benchmarks could resume. The download took 76 seconds ([msg 11282]), during which the assistant monitored progress in 30-second intervals, watching the size grow from 22 GB to 44 GB to the full 52 GB.

What Was Lost: Understanding the Broken Results

But the model download was only half the recovery story. The assistant also had to contend with the state of the benchmark results. When the machine went down, several benchmark configurations were in various states of incompletion:

• tp1-b8 and tp1-b12: These configurations had been attempted but produced 0 tok/s results. The DDTree speculative decoding service had started successfully, but the actual requests failed — a silent failure mode where the service appears healthy but cannot process queries. The assistant's earlier analysis ([msg 11266]) had traced this to a memory allocation problem: the Mamba cache configuration was incompatible with the max_running_requests=8 setting, causing the KV cache to be squeezed to only 11,893 tokens while the Mamba intermediate state consumed excessive memory. The working configuration used max_running_requests=4, which allowed a much more generous Mamba cache of 24 slots and a KV cache of 87,061 tokens. The b8 and b12 results were not just incomplete — they were fundamentally invalid, representing a broken configuration rather than a genuine performance measurement.
• tp1-b15: This configuration was mid-run when the machine went down. The assistant's earlier reasoning ([msg 11275]) reveals that b15 had been showing excellent results — 143 tok/s for the Fibonacci workload at 256 tokens, and 182.9 tok/s at 1024 tokens. But the benchmark script only saves results after completing all workloads for a given configuration, so the partial b15 data was lost in the interruption. The file that existed on disk was a partial or corrupted artifact, not a usable result.

The Reasoning Behind the Cleanup

The assistant's decision to delete these three files is rooted in a clear understanding of what constitutes a valid benchmark result. The rm -f command is not arbitrary destruction — it is a deliberate act of data hygiene.

For b8 and b12, the files represented measurements from a fundamentally broken configuration. Keeping them would risk confusing future analysis or accidentally including them in aggregate statistics. The assistant had already diagnosed the root cause (the max_running_requests parameter causing Mamba cache starvation) and had fixed the configuration in a previous edit ([msg 11268]). These results were not just wrong for the current run — they would never be correct under any configuration, because the DDTree implementation had a genuine bug or incompatibility at those budget levels. The assistant's comment "service started but DDTree crashed" in the bash comment reveals the understanding that these were not merely performance outliers but system-level failures.

For b15, the situation is different. The partial run had shown promising numbers, but the file on disk was incomplete. The benchmark script's design — saving results only after all workloads complete — meant that the b15.json file contained no useful data. More importantly, the assistant needed to re-run b15 from scratch to ensure consistent conditions with the other configurations. Using a partially cached result would introduce unknown variables: different GPU temperature states, different memory fragmentation patterns, different system load. By deleting the partial file, the assistant ensures that b15 will be re-run under identical conditions to the other budget configurations.

What Survived: The Valid Baselines

The assistant's cleanup leaves two files intact: tp1-auto.json and tp1-linear.json. These are the completed, validated baselines. The autoregressive result represents the model's raw performance without any speculative decoding acceleration — the fundamental speed of the Qwen3.6-27B model on a single Blackwell GPU. The DFlash linear result represents the baseline speculative decoding performance using the standard DFlash algorithm without the DDTree tree-structured speculation.

These two files are the anchor points for the entire benchmark. Every DDTree budget configuration will be compared against these baselines to compute speedup ratios. The assistant's decision to preserve them reflects confidence that these results were collected under correct, stable conditions before the infrastructure disruption. They survived the reboot because they were stored on persistent disk (the bench_results/ directory on the local machine, not on the CT200's tmpfs).

Assumptions Embedded in the Cleanup

The cleanup operation rests on several assumptions that are worth examining:

First, the assistant assumes that the b8 and b12 failures are inherent to those configurations, not transient errors caused by the machine's state at the time. This is a reasonable assumption given the earlier diagnostic work, but it is not proven. A different memory state, a different GPU clock frequency, or a different CUDA kernel compilation path could theoretically produce different results. The assistant is implicitly trusting its earlier analysis that the max_running_requests=8 configuration was the root cause.

Second, the assistant assumes that re-running b15 from scratch will produce results comparable to the promising partial data seen earlier. This assumes that the model download and service startup are deterministic processes — that the same configuration will produce the same performance characteristics. In practice, GPU benchmarking can be sensitive to thermal state, power capping, and PCIe bus contention, but the assistant's assumption is reasonable for a controlled environment.

Third, the assistant assumes that the two surviving result files are complete and valid. It does not verify their contents beyond checking that they exist. A corrupted JSON file could contain partial data or NaN values, but the assistant trusts the file system integrity.

The Broader Significance

Message 11283 is a textbook example of how real-world machine learning engineering operates. The glamorous work of model benchmarking and performance optimization is punctuated by mundane infrastructure failures — network maintenance, server reboots, cleared tmpfs filesystems. The ability to recover gracefully from these disruptions, to distinguish between valid and corrupted data, and to resume work without losing momentum is a critical skill.

The assistant's approach here is methodical: diagnose the damage, restore the prerequisites (the model download), clean up the corrupted artifacts, and then proceed. The cleanup is not just about file management — it is about maintaining the integrity of the experimental record. By explicitly deleting the bad results and commenting on why they were bad, the assistant creates a clear audit trail. Anyone reviewing the benchmark later will see that b8 and b12 were attempted but failed, and that b15 was re-run after a clean restart.

This message also reveals the assistant's understanding of the benchmark pipeline's architecture. The decision to delete files locally (on the machine running the benchmark script) rather than on the CT200 server shows awareness of where results are stored. The use of rm -f (force delete, suppressing errors if files don't exist) shows defensive programming — the assistant handles the case where b15.json might not exist at all.

Conclusion

Message 11283 is a small but essential gear in a large machine. It represents the transition from recovery to productivity, from chaos to order. The assistant has weathered an infrastructure disruption, re-downloaded a 52 GB model, diagnosed why certain configurations failed, and is now ready to resume the benchmark sweep. The cleanup of three JSON files is the final step before the real work can continue — a clean slate for the next phase of the experiment.

In the broader narrative of the session, this message marks the point where the assistant stops reacting to problems and starts executing its plan again. The benchmarks that follow — the TP1 DDTree budget sweep, the TP4 and TP8 concurrency scaling tests, and eventually the pivot to Kimi K2.6 — all depend on the foundation laid in this moment. Without the cleanup, the assistant would risk contaminating its analysis with invalid data. With it, the path forward is clear.