The Moment of Diagnosis: Uncovering Auto-Started vLLM After a VM Crash

In the high-stakes world of large-scale ML training, infrastructure failures are not just interruptions — they are diagnostic crucibles. Message [msg 4196] captures one such moment: the aftermath of a catastrophic VM crash during a multi-day EAGLE-3 training pipeline for the Kimi-K2.5 model. The user had just reported that the Ceph cluster underlying their VM ran out of space "quite catastrophically," forcing a kill, a disk migration to a new 15TB NVMe drive, and a container restart. The assistant's task was to assess the damage and restart hidden state extraction. But what it discovered upon investigation reveals the hidden complexity of managing multi-service GPU environments.

The Message: A Diagnostic Pivot

The message is brief but dense with meaning:

vLLM auto-started on boot — it's running with --gpu-memory-utilization 0.95 on all 8 GPUs. That's from a systemd service or crontab. Let me kill it and check what auto-started it, then assess /data.

This is followed by a bash command that probes systemd services and crontab entries, revealing a constellation of pre-existing vLLM service files: vllm-kimi-k25-int4.service, vllm-glm5.service, vllm-kimi-k25-int4-ngram.service, vllm-kimi-k25.service, and several others.

On its surface, the message is a simple status report. But beneath it lies a critical diagnostic pivot: the assistant has identified that the very infrastructure designed to keep services running after a reboot is now the primary obstacle to recovery. The systemd services that were carefully set up in earlier sessions to auto-start vLLM inference servers have become a liability, consuming all GPU memory and blocking the SGLang-based extraction pipeline that needs to resume.

Why This Message Was Written: The Reasoning and Motivation

The assistant wrote this message in response to the user's report that the VM had been killed and restarted ([msg 4192]). The user explicitly asked the assistant to "assess what happened inside, ideally restart the hidden state extraction." The assistant had already noted in [msg 4194] that GPUs showed 75.8 GB memory usage each with 0% utilization — a clear sign that something had loaded the model but was doing no work. The assistant's first hypothesis was that "something auto-started on GPUs, probably vllm/sglang from previous setup."

Message [msg 4196] is the confirmation step. The assistant needed to:

  1. Identify the culprit: Was it vLLM or SGLang? Which service exactly?
  2. Determine the mechanism: Was it systemd, crontab, or something else?
  3. Plan the remediation: Kill the service, disable auto-start, free GPUs
  4. Assess collateral impact: Had the extraction data survived the crash? Was /data intact? The motivation is fundamentally about re-establishing control over the GPU resources. In a typical ML workflow, GPU memory is the most precious resource. Eight RTX PRO 6000 Blackwell GPUs with 96GB each represent a massive computational asset, but only if the software stack correctly manages their allocation. The assistant's reasoning is that before any extraction or training can resume, the GPU memory must be freed from the vLLM process that grabbed it at boot.

How Decisions Were Made

The message reveals a deliberate investigative methodology. The assistant did not simply kill all Python processes and restart. Instead, it followed a systematic chain:

  1. Observe symptoms ([msg 4194]): nvidia-smi showed high memory usage but zero utilization across all 8 GPUs. This ruled out active computation and pointed to a loaded-but-idle model.
  2. Identify processes ([msg 4195]): ps aux revealed vLLM's OpenAI API server running with --tensor-parallel-size 8 --gpu-memory-utilization 0.95. The process was consuming ~1 GB of system memory and holding all 8 GPUs.
  3. Trace the mechanism ([msg 4196]): Rather than killing the process immediately, the assistant checked systemd and crontab to understand why it started. This is a crucial decision — killing the process without disabling the auto-start mechanism would only lead to the same problem after the next reboot.
  4. Assess the scope: The systemctl list-units and file listing commands revealed multiple vLLM service files, indicating this was a pre-existing setup from earlier in the conversation's history (segment 0 mentions deploying GLM-5-NVFP4 using SGLang, and there were clearly vLLM deployments as well). The decision to check systemd before killing the process is particularly noteworthy. It reflects an understanding that in production-like environments, services have persistence mechanisms that must be addressed at the root. A junior engineer might simply kill the process and move on, only to be surprised when the same problem recurs after the next unexpected reboot.

Assumptions Made by the Assistant

Several assumptions underpin this message:

That vLLM was the only GPU consumer: The assistant assumed that vLLM was the sole occupant of GPU memory. This was a reasonable inference from the ps aux output showing only one vLLM process, but it's worth noting that the SGLang server from the extraction pipeline might also have left residual state in /dev/shm/sglang_hs/ (as seen in [msg 4189] showing dump directories req_22391 and req_22392).

That systemd was the mechanism: The assistant's phrasing "That's from a systemd service or crontab" hedges slightly, but the subsequent commands focus entirely on systemd. This assumption proved correct — the output shows vllm-kimi-k25-int4.service is "loaded active running." The crontab check returned empty, confirming systemd as the culprit.

That the extraction data survived: The assistant planned to "assess /data" after dealing with vLLM. This assumes the new NVMe disk was properly mounted and the hidden state files (3.5 TB of extracted data) were intact. Given the "catastrophic" Ceph failure, this was not guaranteed — the data might have been corrupted or partially written.

That killing vLLM was safe: The assistant assumed that abruptly terminating the vLLM server would not cause issues. This is generally safe for inference servers (no persistent state to corrupt), but it's an assumption worth noting.

Mistakes and Incorrect Assumptions

The most significant potential mistake is the assumption that the extraction data was complete or recoverable. The extraction had been at approximately 18,420 out of 37,312 samples (49%) when the crash occurred ([msg 4186]). The assistant's plan to "restart hidden state extraction" implicitly assumes the extraction script's resume capability (skipping existing .pt files) would work correctly after the crash. However, the crash might have left partially written files, corrupted metadata, or inconsistent state in the server's hidden state dump directory.

A more subtle issue is the assumption about the SGLang server state. The extraction pipeline relied on a patched SGLang server with hidden state dumping enabled. The crash would have killed that server. The assistant's plan to "re-apply HS dump patch if needed, restart SGLang extraction server" (from the todo list in [msg 4193]) acknowledges this, but the message itself doesn't verify whether the SGLang server configuration survived the disk migration.

Additionally, the assistant did not immediately check whether the new NVMe disk was properly mounted at /data. The df -h check from [msg 4188] showed disk usage before the crash, but after the migration, the mount point might have changed. The message defers this to "then assess /data," which is reasonable sequencing but leaves a critical dependency unverified.

Input Knowledge Required

To fully understand this message, one needs:

Knowledge of the systemd service management: Understanding that .service files in /etc/systemd/system/ define auto-start behavior, and that systemctl list-units shows running services. Without this, the assistant's investigative approach would seem opaque.

Awareness of the GPU memory hierarchy: The significance of --gpu-memory-utilization 0.95 — that vLLM reserves 95% of each GPU's 96GB VRAM, leaving only ~4.8GB per GPU for other processes. This explains why the extraction server (which needs to load the same model) cannot start while vLLM is running.

Context from the broader conversation: The reader must know that the EAGLE-3 pipeline involves:

Output Knowledge Created

This message produces several concrete pieces of knowledge:

The specific auto-start mechanism: The systemd service vllm-kimi-k25-int4.service is identified as the active service. The listing of multiple vLLM service files (vllm-glm5.service, vllm-kimi-k25-int4-ngram.service, vllm-kimi-k25.service, etc.) reveals the full scope of pre-existing inference deployments. This is valuable for cleanup — the assistant now knows which services to disable to prevent future conflicts.

The absence of crontab-based auto-start: The empty crontab output confirms that only systemd is responsible, simplifying the remediation to systemctl disable commands.

The state of the GPU memory: The message confirms that vLLM is consuming nearly all GPU memory on all 8 GPUs, with 0% utilization. This is the critical finding that drives the next actions: kill vLLM, free GPUs, start SGLang.

The service file inventory: The listing of service files provides a map of what models were previously deployed (Kimi-K2.5 INT4, Kimi-K2.5, GLM-5, MiniMax-M25, etc.). This is useful context for understanding the machine's history and for deciding which services to preserve versus disable.

The Thinking Process Visible in the Reasoning

The message reveals a structured diagnostic thought process, visible in the sequence of actions and the language used:

Hypothesis formation: "vLLM auto-started on boot" — this is stated as a conclusion, not a guess. The assistant has already ruled out SGLang (the extraction server) because the extraction script was stuck, not running. The GPU memory pattern (high usage, zero utilization) is consistent with a loaded-but-idle model, which is exactly what vLLM does when it starts but receives no requests.

Mechanism tracing: "That's from a systemd service or crontab" — the assistant considers two common auto-start mechanisms and designs the bash command to check both simultaneously. The systemctl list-units command checks running services, while crontab -l checks scheduled tasks. The ls /etc/systemd/system/*vllm* command inventories all installed service files, whether or not they're currently active.

Prioritization: "Let me kill it and check what auto-started it, then assess /data" — this reveals the assistant's priority ordering: (1) free GPU resources, (2) understand the auto-start mechanism to prevent recurrence, (3) verify data integrity. This is sound reasoning because the data assessment (which involves scanning 3.5 TB of files) is slow and should not be done while GPU resources are blocked.

Forward-looking remediation: The assistant doesn't just solve the immediate problem (vLLM occupying GPUs). It traces the root cause (systemd service) so it can disable auto-start and prevent the same problem after future reboots. This is the difference between a quick fix and a durable solution.

The bash command itself is carefully constructed. It uses grep -iE "vllm|sglang|llm|model" to catch any related services regardless of naming convention. It checks three locations in parallel: running services, crontab, and service files. The output reveals a richer picture than any single check would provide.

Conclusion

Message [msg 4196] is a masterclass in systematic troubleshooting under pressure. In the wake of a catastrophic storage failure, VM kill, disk migration, and container restart, the assistant could have rushed to restart the extraction pipeline. Instead, it paused to diagnose why the GPUs were occupied, traced the mechanism to systemd, and laid the groundwork for a clean recovery. The message transforms a moment of crisis into an opportunity for understanding — and in doing so, it demonstrates that the most valuable diagnostic skill is not speed, but thoroughness.