The Nuclear Option: Cleaning GPU State Between Benchmarking Runs

In the high-stakes world of large-scale ML inference optimization, the difference between a clean benchmark and a contaminated one can be the difference between shipping a feature and chasing ghosts. Message [msg 5425] captures a seemingly mundane but critically important moment in a larger optimization journey: the cleanup between two benchmarking phases. The assistant issues a single bash command:

ssh root@10.1.230.174 'fuser -k /dev/nvidia* 2>/dev/null; sleep 1; nvidia-smi | head -20'

This command, executed against a remote LXC container hosting eight NVIDIA RTX PRO 6000 Blackwell GPUs, does three things in sequence: it kills every process holding any NVIDIA device file open, pauses briefly, and then verifies the GPUs are idle. The output confirms the kill was effective — ten instances of PID 156175 were terminated (one per GPU device file), and nvidia-smi reports a clean, idle system ready for the next workload.

The Strategic Context: Why This Cleanup Matters

To understand why this message exists, we must trace the reasoning back through the preceding messages. The session had reached a critical inflection point. After an arduous multi-day effort that included upgrading the entire CUDA stack from version 12.8 to 13.0, patching SGLang for Blackwell SM120 support, and enabling FlashInfer allreduce fusion, the assistant had finally made EAGLE-3 speculative decoding a net positive — 96.1 tok/s versus the baseline of 92.6 tok/s ([msg 5412]). This was a triumph after the EAGLE-3 drafter had previously been 40% slower than baseline.

But the user, thinking strategically, asked a pivotal question in [msg 5418]: "Can we tell (or mod) sglang to disable speculation at certain concurrency?" This question revealed a sophisticated understanding of speculative decoding's fundamental trade-off. Speculation (EAGLE-3) reduces per-request latency by having a lightweight draft model predict tokens that the large model then verifies in parallel. At low concurrency, when the GPU is underutilized, this is a clear win. But at high concurrency, when the GPU is already saturated with many concurrent requests, the extra compute spent on drafting and verifying can become pure overhead — the GPU would be better off just generating tokens directly for all requests.

The assistant immediately recognized the correctness of this insight and formulated a two-part plan in [msg 5419]: benchmark the baseline (no speculation) at the same concurrency levels as the EAGLE-3 server had already been tested at, then compare the results to find the crossover point where speculation stops being beneficial. This crossover point would inform a dynamic speculation disable mechanism — automatically switching off EAGLE-3 when the server is under sufficient load.

Message [msg 5425] is the cleanup step that enables the first half of this plan. The EAGLE-3 server had been running continuously since the CUDA 13 upgrade benchmarks. Before the assistant could start a baseline server (no speculation), it needed to ensure a clean state.

The Two-Phase Kill: Reasoning Behind the Approach

The assistant's cleanup strategy reveals careful thinking about the peculiarities of GPU process management. In [msg 5424], the assistant first issued a broad kill from the Proxmox host level:

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"'

This command finds all Python processes in the LXC container and kills them with SIGKILL. But the assistant recognized a potential gap: this only kills processes that show up in ps aux as python3. What if there are zombie processes, orphaned children, or non-Python processes (like NCCL watchdog threads or CUDA driver helper processes) that still hold GPU device files open? These could interfere with the new server's initialization, causing CUDA errors, memory allocation failures, or subtle performance degradation.

The fuser -k /dev/nvidia* command in [msg 5425] is the nuclear option — it doesn't care about process names or ownership. It identifies every process that has any NVIDIA device file open (including /dev/nvidia0 through /dev/nvidia7 for the eight GPUs, and /dev/nvidiactl for the control device) and kills them all. This is the most reliable way to guarantee that the GPU subsystem is completely released.

The assistant's choice to run this as a second step, after the broader Python process kill, demonstrates an understanding that different cleanup mechanisms have different failure modes. The ps aux approach might miss processes that don't match the python3 pattern (e.g., if the process name is truncated or if a subprocess was spawned with a different name). The fuser approach is exhaustive but destructive — it kills everything, including potentially legitimate system processes. By doing both, the assistant maximizes the chance of a clean state.

Assumptions and Potential Blind Spots

The message operates on several implicit assumptions. First, the assistant assumes that the previous EAGLE-3 server was the only significant GPU-using process. This is a reasonable assumption given the controlled environment — a dedicated inference container with no other workloads. However, it's worth noting that fuser -k /dev/nvidia* would also kill any monitoring daemons, debuggers, or other tools that happen to have GPU device files open. In a production environment, this could be disruptive.

Second, the assistant assumes that killing processes holding GPU device files is sufficient to fully release GPU memory and reset the CUDA driver state. In practice, this is usually true for SIGKILL — the kernel's driver cleanup routines release GPU memory mappings when the file descriptor is closed. However, there are edge cases where CUDA contexts can persist briefly after process termination, especially with certain NCCL configurations or MPS (Multi-Process Service) setups. The sleep 1 between the kill and the verification is a pragmatic acknowledgment of this.

Third, the assistant assumes that nvidia-smi | head -20 is sufficient verification of a clean state. The output shows the driver version (590.48.01) and CUDA version (13.1), and the truncated output would show GPU utilization metrics. However, nvidia-smi doesn't always tell the complete story — there can be lingering CUDA contexts that aren't visible in the standard output. A more thorough check might involve nvidia-smi --query-gpu=memory.used --format=csv to verify all GPUs show zero used memory.

The Output: What the Message Creates

The message produces critical knowledge for the next steps. The fuser output — "156175 156175 156175 156175 156175 156175 156175 156175 156175 156175" — confirms that PID 156175 was killed on each of the ten NVIDIA device files (eight GPUs, one control device, and likely one or more NVIDIA UVM or other device files). This PID was the main EAGLE-3 server process that the ps aux kill in [msg 5424] had already targeted. The fact that fuser still found it suggests that the process might have been in a zombie state — already dead but holding file descriptors — or that the first kill hadn't fully propagated by the time fuser ran.

The nvidia-smi output confirms the driver stack is intact and the system is responsive. This is non-trivial — after killing processes that hold GPU device files, there's always a risk of destabilizing the driver. The clean output confirms the GPUs are ready for the baseline server.

Input Knowledge Required

To fully understand this message, one needs knowledge of several domains. First, the Linux device file system: /dev/nvidia* files are the user-space interface to the NVIDIA kernel driver, and processes must open these files to interact with GPUs. Second, the fuser command: it identifies and optionally kills processes using specified files or sockets. Third, the peculiarities of GPU process cleanup: SIGKILL doesn't always immediately release all GPU resources, and zombie processes can hold device files open. Fourth, the LXC container environment: the container has its own PID namespace but shares the host kernel's GPU driver, meaning GPU device files in the container correspond to the host's GPUs.

The message also assumes familiarity with the broader context: that the assistant is in the middle of a benchmarking campaign, that the EAGLE-3 server needs to be replaced with a baseline server, and that clean GPU state is essential for valid benchmark results.

The Thinking Process Visible in the Message

While the message itself is just a command and its output, the reasoning behind it is visible when we examine the sequence of actions. The assistant is thinking in layers:

Layer 1: Kill all Python processes (broad, pattern-based cleanup).
Layer 2: Kill all processes holding GPU device files (exhaustive, file-based cleanup).
Layer 3: Verify the GPUs are responsive and idle (confirmation). This layered approach reveals an understanding that no single cleanup method is perfectly reliable. The assistant is building redundancy into the process, accepting that the second command might be redundant if the first was sufficient, but preferring safety over speed. The cost of a failed cleanup — a baseline benchmark that produces misleading results due to GPU state contamination — is far higher than the cost of an extra 30 seconds of cleanup. The sleep 1 between the kill and the verification is another sign of careful thinking. The assistant understands that process termination and file descriptor release are asynchronous operations, and that querying the GPU state immediately after sending SIGKILL might produce stale or misleading results. A one-second pause is a heuristic — long enough for the kernel to process most terminations, short enough to not waste significant time.

Conclusion

Message [msg 5425] is a textbook example of operational discipline in ML infrastructure work. It's not glamorous — there's no performance breakthrough, no clever algorithm, no architectural insight. But it represents the kind of systematic, careful thinking that separates reliable benchmarks from noisy ones. The assistant recognized that the transition between two benchmarking phases required more than just killing a process — it required ensuring complete GPU state cleanup, verifying the result, and only then proceeding to the next step. This attention to the mundane details of process management is what enables the high-quality performance comparisons that drive real optimization decisions.