The Critical Transition: Freeing GPU Resources for Micro-Benchmarking in a Production ML Environment

Introduction

In the course of a comprehensive performance profiling campaign for the Kimi-K2.5 INT4 model running on 8x NVIDIA RTX PRO 6000 Blackwell GPUs, a seemingly mundane operational message marks a critical inflection point. Message 2422 is the bridge between two fundamentally different phases of benchmarking: the macro-level throughput and latency measurements that were just completed against a live vLLM inference server, and the micro-level kernel and communication benchmarks that require direct GPU access. This message, in which the assistant stops the production vLLM service and aggressively cleans up GPU processes, reveals the operational realities of multi-GPU inference profiling and the careful choreography required to transition between measurement regimes.

The Message in Full

[assistant] Now let me stop vLLM, kill zombie processes, and run the micro benchmarks. I'll also create a NCCL benchmark adapted for Kimi dimensions. Let me first stop vLLM and prepare:

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``bash ssh root@10.1.230.174 'systemctl stop vllm-kimi-k25-int4; sleep 3; ps aux | grep -E "python3|VLLM" | grep -v grep | awk "{print \$2}" | xargs kill -9 2>/dev/null; sleep 5; kill -9 $(fuser /dev/nvidia* 2>/dev/null | tr " " "\n" | sort -u | grep -v "^$") 2>/dev/null; sleep 2; nvidia-smi --query-gpu=index,memory.used --format=csv,noheader' ``

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Output: `` 0, 0 MiB 1, 0 MiB 2, 0 MiB 3, 0 MiB 4, 0 MiB 5, 0 MiB 6, 0 MiB 7, 0 MiB ``

Why This Message Was Written: The Context and Motivation

To understand why this message exists, we must trace the trajectory of the profiling campaign that preceded it. In earlier messages ([msg 2407]), the assistant laid out an ambitious three-phase profiling plan for the Kimi-K2.5 INT4 deployment. Phase 1 consisted of macro-level benchmarks against the running vLLM server—measuring single-stream tokens-per-second, multi-concurrency throughput scaling, and overall latency characteristics. Phase 2 required micro-benchmarks of individual operations: the Marlin W4A16 GEMM kernels at exact Kimi-K2.5 dimensions, and NCCL AllReduce burst measurements at the model's hidden size of 7168.

The macro benchmarks had just completed successfully ([msg 2420]), revealing a throughput plateau at approximately 1536 tok/s at concurrency level 128. This data was already written into the results document k25b6000bench1.md. But the macro benchmarks only tell part of the story—they measure end-to-end performance but cannot reveal where time is spent inside each decode step. To identify the true bottleneck (whether it is the Marlin GEMM kernels, the NCCL AllReduce communication, the MoE routing, or attention), the assistant must run micro-benchmarks that isolate individual operations.

However, there is a fundamental constraint: the micro-benchmarks require direct GPU access. The vLLM server, which was running as a systemd service (vllm-kimi-k25-int4), was consuming 96.9 GB of memory on each of the 8 GPUs ([msg 2410]). There is no spare GPU available for micro-benchmarking while vLLM is running. The assistant therefore faces an unavoidable operational decision: stop the production service to free the GPUs, run the micro-benchmarks, and then restart the service afterward.

The Decision-Making Process: How and Why

The message reveals a carefully considered, multi-layered approach to GPU resource management. The assistant does not simply run systemctl stop and proceed. Instead, it executes a four-stage cleanup pipeline:

  1. Graceful service shutdown: systemctl stop vllm-kimi-k25-int4 — This sends the proper termination signal to the vLLM service, allowing it to flush any pending requests, save state if needed, and release resources cleanly. The 3-second sleep after this command gives the service time to complete its shutdown sequence.
  2. Process-level cleanup: ps aux | grep -E "python3|VLLM" | grep -v grep | awk "{print \$2}" | xargs kill -9 — This is a brute-force sweep for any remaining Python or VLLM-related processes that might still be holding GPU memory. The use of kill -9 (SIGKILL) rather than kill (SIGTERM) indicates that the assistant expects some processes may be unresponsive to graceful termination. The 5-second sleep allows time for these processes to be reaped by the kernel.
  3. Device-level force cleanup: kill -9 $(fuser /dev/nvidia* ...) — This is the most aggressive step. fuser identifies any process that has any of the NVIDIA device files open (including /dev/nvidia0 through /dev/nvidia7, /dev/nvidiactl, /dev/nvidia-uvm, etc.). By force-killing these processes, the assistant ensures that no residual process is holding a GPU device handle, which would prevent memory release. This step is particularly important because GPU memory is reference-counted by the CUDA driver, and a process that holds a device handle but has been orphaned can keep memory allocated indefinitely.
  4. Verification: nvidia-smi --query-gpu=index,memory.used --format=csv,noheader — The final step confirms that all 8 GPUs now show 0 MiB of used memory, confirming that the cleanup was successful. This multi-stage approach reflects hard-won experience with GPU memory management. In production ML environments, it is common for processes to become "zombie" GPU processes—they appear dead to the operating system but still hold CUDA context references that keep GPU memory allocated. The CUDA driver's memory management is notoriously sticky: memory allocated by a process is not freed until the CUDA driver explicitly releases the context, which may not happen if a process is killed in the wrong order or if child processes inherit GPU contexts. The assistant's aggressive cleanup with fuser and kill -9 is a pragmatic response to this reality.

Assumptions Embedded in the Message

The message makes several implicit assumptions that are worth examining:

Assumption 1: Stopping vLLM is safe. The assistant assumes that no other users or services depend on the vLLM inference server being available. In a production deployment, stopping the service would cause service disruption. However, the context suggests this is a dedicated benchmarking environment, not a customer-facing production system. The assistant does not check for active connections or pending requests before stopping the service.

Assumption 2: Micro-benchmarks will not interfere with vLLM's state. The assistant assumes that after stopping vLLM, running micro-benchmarks, and restarting, the vLLM service will resume normally. This is not guaranteed—if the micro-benchmarks leave GPU memory in an unexpected state (e.g., by allocating and freeing memory in patterns that fragment the CUDA memory allocator), or if they modify CUDA driver state (e.g., by changing MPS settings or NCCL environment variables), the subsequent vLLM restart could behave differently.

Assumption 3: The aggressive cleanup is necessary and sufficient. The assistant assumes that the multi-stage kill pipeline is both necessary (i.e., a simple systemctl stop would not suffice) and sufficient (i.e., the cleanup will fully release all GPU memory). The verification step confirms sufficiency for this particular case (all GPUs show 0 MiB), but the necessity assumption is harder to validate—perhaps a cleaner shutdown path exists that the assistant has not explored.

Assumption 4: The NCCL benchmark can be adapted from existing work. The message mentions "I'll also create a NCCL benchmark adapted for Kimi dimensions," implying that the assistant has a template NCCL benchmark from previous work (likely the GLM-5 profiling campaign documented in glm5findings.md). This is a reasonable assumption given the continuity of the session.

Potential Mistakes and Incorrect Assumptions

While the message is operationally sound, several aspects warrant critical examination:

The shotgun approach to cleanup. Using kill -9 on all processes matching python3|VLLM is indiscriminate. If there were any other Python processes running on the system (e.g., monitoring scripts, log aggregators, or even the SSH session itself), they could be caught by this filter. The grep -v grep exclusion prevents killing the grep process itself, but any Python process whose name contains "python3" would be targeted. In a more carefully managed environment, one would use process IDs or service names rather than pattern matching.

The fuser approach has side effects. fuser /dev/nvidia* identifies any process with an open file descriptor on NVIDIA device files. This could include monitoring tools like nvidia-smi (which opens device files for queries), the systemd journal, or even the SSH session if it has somehow inherited a file descriptor. The kill -9 on these processes could disrupt legitimate system monitoring.

No state preservation. The assistant does not save or checkpoint any vLLM state before stopping the service. If the service had any in-flight requests, those would be lost. For a benchmarking session this is acceptable, but in a production context this would be a critical oversight.

The assumption that "0 MiB" means "fully clean." While nvidia-smi reports 0 MiB of used memory, this does not necessarily mean the GPU is in a pristine state. The CUDA driver maintains various caches (e.g., the CUDA memory allocator's primary cache, the L2 cache, the translation lookaside buffer for unified memory) that persist across process lifetimes. A process that allocates and frees memory can leave the GPU's memory management subsystem in a fragmented or cached state that affects subsequent allocations. The assistant does not reset the GPU (e.g., by unloading and reloading the NVIDIA driver or by using nvidia-smi --gpu-reset) to ensure a truly clean state.

Input Knowledge Required to Understand This Message

To fully grasp the significance of this message, the reader needs:

  1. Understanding of the profiling campaign structure. The message is the transition point between Phase 1 (macro benchmarks) and Phase 2 (micro benchmarks) of a three-phase profiling plan. Without knowing this structure, the message appears to be just a routine service stop.
  2. Knowledge of GPU memory management in CUDA. The aggressive cleanup strategy reflects an understanding that GPU memory is not automatically freed when a process exits—the CUDA driver's context management, reference counting, and asynchronous cleanup can leave memory allocated long after the owning process has terminated.
  3. Awareness of the hardware topology. The system has 8 GPUs connected via PCIe (no NVLink), which is the fundamental constraint driving the entire profiling effort. The NCCL benchmark mentioned in the message is specifically designed to measure the PCIe allreduce bottleneck that was hypothesized to be the primary throughput limiter.
  4. Familiarity with the Kimi-K2.5 model architecture. The "Kimi dimensions" referenced in the NCCL benchmark context (hidden_size=7168, 122 allreduces per token across 61 layers) are specific to this model's architecture.

Output Knowledge Created by This Message

The message produces several concrete outputs:

  1. A verified clean GPU state. The output confirms that all 8 GPUs are free (0 MiB used), enabling the micro-benchmarking phase to proceed.
  2. A documented cleanup methodology. The multi-stage pipeline (systemctl stop → process kill → device fuser kill → verification) serves as a reusable pattern for transitioning between GPU workloads. This methodology is particularly valuable in environments where GPU processes tend to leave residual allocations.
  3. A clear transition point in the benchmarking timeline. The message establishes that macro benchmarks are complete and micro benchmarks are about to begin, providing a checkpoint in the profiling campaign.
  4. An implicit validation of the vLLM service architecture. The fact that systemctl stop successfully terminates the service (even though additional cleanup is needed) confirms that the systemd service unit is properly configured for basic lifecycle management.

The Thinking Process Visible in the Reasoning

The assistant's reasoning, while not explicitly stated as a chain-of-thought, is visible in the structure of the command itself. The multi-stage pipeline reveals a mental model of GPU resource cleanup as a layered problem:

Conclusion

Message 2422 is a masterclass in operational pragmatism. It is not glamorous—it does not produce breakthrough performance numbers or reveal architectural insights. But it is the essential plumbing that makes the rest of the profiling campaign possible. The assistant's methodical, layered approach to freeing GPU resources reflects a deep understanding of the CUDA driver's memory management model and the practical realities of multi-GPU inference systems. The message also reveals the tension between the clean, abstract world of benchmarking plans and the messy, concrete world of process management, where kill -9 and fuser are sometimes the only reliable tools. For anyone who has worked with multi-GPU systems, this message will resonate as a familiar and necessary ritual—the careful dance of reclaiming GPU memory from processes that do not want to let go.