The Nuclear Cleanup: Restoring GPU Access After a Restart Loop

A Single Command That Unblocked an MTP Speculation Tuning Session

In the middle of an intensive session tuning Multi-Token Prediction (MTP) speculation parameters for a Qwen3.5-122B-A10B model deployed across four NVIDIA GPUs, the assistant encountered a frustrating infrastructure problem: the server had entered a restart loop. Each time the SGLang service tried to start, it failed because GPU memory was still held by lingering processes from the previous instance. The message at index 6538 represents the moment the assistant diagnosed this issue and executed a targeted cleanup to break the cycle.

The Message

Here is the exact message, a single bash command issued by the assistant:

[assistant] [bash] ssh root@10.1.2.6 'pct exec 129 -- bash -c "ps aux | grep python3 | grep -v grep | awk \"{print \\\$2}\" | xargs -r kill -9"' 2>&1; sleep 3; ssh root@10.1.230.174 'fuser -k /dev/nvidia* 2>/dev/null; sleep 2; nvidia-smi --query-gpu=index,memory.used --format=csv,noheader' 2>&1
 17072 17072 17072 17072 17072 170720, 0 MiB
1, 0 MiB
2, 0 MiB
3, 0 MiB

The output confirms success: all four GPUs now show 0 MiB of memory used. The path is clear.

Context: How the Restart Loop Began

To understand why this message was written, we need to trace the events that led to it. The assistant had been systematically benchmarking MTP speculation depth—the number of draft tokens the model predicts in a single forward pass before verification. Starting from speculative_num_steps=1 (baseline, ~123 tok/s single-request), the assistant progressively increased to steps=2 (~186 tok/s), steps=3 (~234 tok/s), steps=4 (~277 tok/s), and steps=5 (~282 tok/s). Each step required stopping the SGLang service, editing the systemd unit file, copying it to the remote machine, restarting the service, waiting for the ~100-second model load, and running benchmarks.

When the user requested steps=10 ([msg 6522]), the assistant deployed the change and waited for the server to come up. But the server crashed. The assistant's investigation revealed a subtle but critical issue: the systemctl stop command used in the previous iteration (steps=5 teardown) had sent a SIGTERM to the SGLang process, which began gracefully exiting with 16 pending benchmark requests. Systemd's TimeoutStopSec=60s then kicked in and sent SIGKILL before the graceful exit completed. This left zombie processes holding GPU memory allocations. When the next instance tried to start, CUDA could not allocate memory because the GPUs appeared "in use" by the dead processes.

The assistant observed the restart loop across several messages ([msg 6527] through [msg 6537]), watching PIDs change as the service repeatedly crashed and auto-restarted. Each attempt consumed more time as the 119B parameter model had to reload from disk, only to fail again because GPU memory was still occupied.

The Anatomy of the Cleanup Command

The command in message 6538 is a carefully constructed three-stage pipeline executed across two machines. Each stage serves a specific purpose in the cleanup strategy.

Stage 1: Kill all Python processes on the Proxmox container. The first SSH connection goes to the Proxmox hypervisor at 10.1.2.6 and executes a command inside container 129 (the LXC container hosting the VM that runs the SGLang server). The command uses ps aux | grep python3 | grep -v grep to find all Python processes, extracts their PIDs with awk, and kills them with kill -9 (SIGKILL). The -r flag on xargs ensures the command runs only if there are PIDs to kill. This is intentionally aggressive—it kills all Python processes, not just the SGLang server. The assumption is that no other critical Python workloads are running on this machine.

Stage 2: Kill processes holding NVIDIA device files. After a 3-second pause (allowing processes to fully terminate), the assistant connects to the target machine 10.1.230.174 (llm-two) and runs fuser -k /dev/nvidia*. The fuser command identifies which processes have file descriptors open on the NVIDIA device files (/dev/nvidia0, /dev/nvidia1, /dev/nvidia2, /dev/nvidia3, /dev/nvidia-uvm, /dev/nvidiactl, etc.) and kills them with SIGKILL. This is the most precise part of the cleanup—it targets only processes that are actively holding GPU resources, regardless of whether they appear as Python processes or have been reparented by systemd.

Stage 3: Verify GPU memory is freed. After another 2-second pause, the assistant runs nvidia-smi --query-gpu=index,memory.used --format=csv,noheader to check the memory usage of all four GPUs. The output confirms success: 0, 0 MiB, 1, 0 MiB, 2, 0 MiB, 3, 0 MiB—every GPU reports zero memory usage.

Assumptions and Reasoning

The assistant made several assumptions in crafting this command. First, it assumed that the root cause of the restart loop was stale processes holding GPU memory, not a configuration error, an OOM condition during model load, or a bug in the SGLang code itself. This was a reasonable inference given that the server had been running successfully with steps=5 earlier, and the only change was the speculation depth parameter. The crash logs showed no CUDA errors or Python tracebacks, just the process being killed by systemd after a timeout.

Second, the assistant assumed that killing all Python processes on the Proxmox container was safe. In a production environment with multiple services, this would be reckless. But in this context—a dedicated ML inference machine being actively tuned—it was a pragmatic choice. The assistant had been the only operator interacting with this machine for the duration of the session.

Third, the assistant assumed that fuser -k /dev/nvidia* would catch any remaining GPU-holding processes that the Python kill missed. This is a robust approach because fuser operates at the file descriptor level rather than the process name level. Even if a process had been reparented by systemd or renamed, fuser would still find it as long as it held an open file descriptor on the NVIDIA device.

Potential Limitations and Missed Opportunities

While the command was effective, it represents a "nuclear option" rather than a surgical fix. The assistant did not attempt to identify which specific processes were holding GPU memory before killing everything. A more measured approach might have used lsof /dev/nvidia* to list the offending processes, then killed only those. The assistant also did not investigate why the graceful shutdown was taking longer than 60 seconds—addressing that root cause could have prevented the restart loop entirely.

Additionally, the assistant did not modify the systemd service configuration to increase TimeoutStopSec or add a ExecStopPost command to automatically clean up GPU processes after a stop. Such changes would have prevented the restart loop from recurring. The assistant's approach was reactive (clean up after the problem) rather than preventive (fix the shutdown process).

Another subtle issue: the output shows 17072 repeated six times from fuser, followed by 170720 which is actually 17072 concatenated with 0, 0 MiB from nvidia-smi due to missing newline separation. The fuser -k command output and nvidia-smi output ran together on the same line because fuser printed its output without a trailing newline, or the shell's command substitution merged them. This is a minor formatting issue that doesn't affect correctness but hints at the ad-hoc nature of the debugging session.

Input Knowledge Required

To fully understand this message, a reader needs knowledge of several technical domains. First, familiarity with the NVIDIA GPU ecosystem: understanding that GPU memory is allocated per-process and persists until the process exits, that nvidia-smi can query memory usage, and that device files like /dev/nvidia0 are the interface through which CUDA programs interact with GPUs. Second, knowledge of Linux process management: understanding ps, grep, awk, xargs, fuser, and signal handling (SIGKILL vs SIGTERM). Third, familiarity with the Proxmox virtualization platform: understanding that pct exec 129 runs a command inside an LXC container. Fourth, awareness of the SGLang/vLLM serving stack and its interaction with systemd service management.

Output Knowledge Created

The primary output of this message is the confirmation that all four GPUs are clean and ready for the next service start. But the message also implicitly confirms several hypotheses: that the restart loop was caused by stale GPU memory (not a code bug or configuration error), that the cleanup strategy was effective, and that the assistant's mental model of the system's failure mode was correct. This knowledge directly informs the next steps: restarting the SGLang service with the steps=10 configuration and running the benchmark.

The Thinking Process

The reasoning visible in this message reflects a systematic debugging approach. The assistant first observed the symptom (server crashing and restarting), then gathered data (checking systemd status, examining logs, noting the SIGKILL from systemd), formed a hypothesis (stale processes holding GPU memory), and designed a targeted intervention. The three-stage cleanup shows an understanding of the infrastructure topology: the Proxmox host, the container, and the target machine are all separate layers that might harbor lingering processes. By cleaning at each layer—first the container's Python processes, then the target machine's GPU-holding processes—the assistant ensured no stone was left unturned.

This message is a small but critical moment in a larger optimization session. It represents the transition from debugging (why is the server crashing?) to recovery (how do we get it running again?). Without this cleanup, the assistant would have been stuck in an infinite loop of watching the server fail to start. With the GPUs freed, the session could continue toward the ultimate goal: finding the optimal MTP speculation depth for this model on this hardware.