The Stuck GPU Memory: A Diagnostic Pivot in the Kimi-K2.5 NVFP4 Deployment

Introduction

In the course of deploying the massive nvidia/Kimi-K2.5-NVFP4 model — a 1-trillion-parameter MoE (Mixture-of-Experts) language model quantized with NVIDIA's NVFP4 format — across eight RTX PRO 6000 Blackwell GPUs, the assistant encountered a stubborn operational problem: after killing the vLLM inference server process, the GPU memory remained pinned at 96.6 GiB per GPU, refusing to release. Message [msg 2160] captures the diagnostic pivot that resolved this mystery. It is a short but dense message — a single bash command and its revealing output — yet it embodies a crucial troubleshooting insight that turned a frustrating dead end into a path forward.

This article examines message [msg 2160] in depth: why it was written, what assumptions it challenged, what knowledge it produced, and how its reasoning reflects the realities of deploying large language models in containerized GPU environments.

The Message Quoted

The subject message reads in full:

[assistant] GPU memory is stuck. This is a known issue with CUDA contexts in LXC containers — sometimes GPU memory doesn't free after process death. Let me check for any remaining CUDA contexts:

>

[bash] ssh root@10.1.230.174 'nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv,noheader 2>&1; echo "---"; fuser /dev/nvidia* 2>&1'

>

212702, VLLM::Worker_TP0, 96610 MiB 212703, VLLM::Worker_TP1, 96610 MiB 212704, VLLM::Worker_TP2, 96610 MiB 212705, VLLM::Worker_TP3, 96610 MiB 212706, VLLM::Worker_TP4, 96610 MiB 212707, VLLM::Worker_TP5, 96610 MiB 212708, VLLM::Worker_TP6, 96610 MiB 212709, VLLM::Worker_TP7, 96610 MiB --- /dev/nvidia-uvm: 202515 212437 212702m 212703m 212704m 212705m 212706m 212707m 212708m 212709m /dev/nvidia0: 202515 212437m 212702m 212703m 212704m 212705m 212706m 212707m 212708m 212709m /dev/nvid...

The message is truncated at the end — the fuser output was cut off by the tool's output limits — but the critical information is already visible.

Context: The Road to This Message

To understand why this message was written, we must trace the preceding events. The assistant had just completed a successful deployment of the Kimi-K2.5-NVFP4 model using a manual vLLM process ([msg 2144] through [msg 2147]), achieving an impressive ~60 tok/s single-request throughput. The next logical step was to convert this manual launch into a production-grade systemd service for reliability and automatic restarts.

The assistant created a systemd service unit file (vllm-kimi-k25.service) and attempted to start it ([msg 2150]). However, the service hung in "activating (start-pre)" state because of an ExecStartPre script that checked whether GPU memory was free. The problem was a shell escaping issue: in systemd service files, the $ character must be escaped as $$ to prevent systemd from interpreting it as a variable reference. The assistant's pre-check script used $free to capture the maximum GPU memory usage, but systemd was expanding $free as an empty variable, causing the check to always see "max used: MiB" and never exit successfully.

After fixing the escaping ([msg 2153]), the assistant reloaded the service and tried again ([msg 2154]). But now a new problem emerged: the service failed immediately, and the GPU memory was stuck at 96.6 GiB per GPU ([msg 2156]). The assistant attempted to kill all vLLM processes with pkill -9 -f "python3.*vllm" ([msg 2157]), but the memory stubbornly refused to budge ([msg 2158], [msg 2159]).

This is the moment message [msg 2160] arrives. The assistant is staring at eight GPUs, each showing 96,619 MiB of used memory, with no visible vLLM process running. The natural assumption is that this is a known quirk of CUDA contexts in LXC containers — a frustrating but well-documented phenomenon where GPU memory allocations survive process death.

The Diagnostic Leap

The assistant's first hypothesis is stated explicitly: "This is a known issue with CUDA contexts in LXC containers — sometimes GPU memory doesn't free after process death." This is a reasonable assumption. LXC (Linux Containers) and similar container environments can exhibit sticky CUDA memory behavior because the CUDA driver's memory management operates at the kernel level, and process termination doesn't always trigger proper cleanup when there are lingering references or file descriptors.

However, rather than accepting this explanation and resorting to a full GPU reset (which would disrupt other workloads), the assistant chooses to investigate further. The tool of choice is nvidia-smi --query-compute-apps, which lists all running CUDA compute processes along with their PIDs, names, and memory usage. This is a more precise diagnostic than the earlier nvidia-smi --query-gpu=memory.used command, which only shows aggregate memory consumption per GPU. The assistant also uses fuser /dev/nvidia* to check which processes hold the NVIDIA device files open.

This decision to dig deeper rather than accept the surface-level explanation is the key reasoning move in this message. It reflects an understanding that in complex deployment scenarios, the obvious explanation is often wrong, and that precise instrumentation is worth the extra effort.

What the Output Revealed

The output was revelatory. The nvidia-smi --query-compute-apps output showed eight processes still running:

212702, VLLM::Worker_TP0, 96610 MiB
212703, VLLM::Worker_TP1, 96610 MiB
...
212709, VLLM::Worker_TP7, 96610 MiB

The vLLM worker processes were still alive. They hadn't been killed by the pkill -9 -f "python3.*vllm" command because their process names were VLLM::Worker_TP0 through VLLM::Worker_TP7 — not matching the grep pattern python3.*vllm. The pkill command was looking for processes whose command line contained "python3" followed by "vllm", but these worker processes had renamed themselves (a common practice in multi-process GPU applications to aid debugging), and their names no longer contained "python3".

The fuser output confirmed this: the worker PIDs (212702-212709) were listed with m suffixes, indicating they had mmap'd the NVIDIA device files. Additionally, PIDs 202515 and 212437 were also holding references — likely the parent vLLM process and the leftover resource tracker process identified earlier.

Assumptions Challenged and Corrected

This message reveals several assumptions that were implicitly held and then corrected:

Assumption 1: pkill -9 -f "python3.*vllm" kills all vLLM processes. This turned out to be false. The worker processes had renamed themselves to VLLM::Worker_TP*, which didn't match the grep pattern. This is a classic pitfall of process-name-based killing: it only works if you know exactly how processes name themselves.

Assumption 2: Stuck GPU memory in LXC is a known driver issue. While this phenomenon does exist, it was not the cause here. The assistant correctly reserved judgment and investigated further before concluding.

Assumption 3: If no vLLM process is visible in ps aux, the processes are dead. The earlier ps aux | grep -E "python|vllm" command ([msg 2158]) showed only the resource tracker process, leading the assistant to believe the workers were gone. But nvidia-smi --query-compute-apps revealed they were still there — they just weren't matching the grep pattern because their names had changed.

Input Knowledge Required

To fully understand this message, the reader needs knowledge of several domains:

  1. CUDA process management: Understanding that nvidia-smi --query-compute-apps lists CUDA processes separately from system process listings, and that it shows the process's self-declared name rather than the executable path.
  2. Linux device file semantics: Understanding that fuser /dev/nvidia* shows which processes have the NVIDIA device files open, and that the m suffix indicates memory-mapped access.
  3. vLLM architecture: Knowing that vLLM spawns multiple worker processes (one per GPU for tensor parallelism) and that these workers may rename themselves for debugging purposes.
  4. LXC container behavior: Understanding that GPU memory management in containerized environments can have quirks, but also knowing not to jump to that conclusion prematurely.
  5. Shell process management: Understanding the limitations of pkill -f with pattern matching, especially when processes change their comm or cmdline values.

Output Knowledge Created

This message produced several valuable pieces of knowledge:

  1. The exact PIDs of the surviving worker processes (212702-212709), enabling targeted killing in the subsequent message ([msg 2161]).
  2. The process name pattern used by vLLM workers (VLLM::Worker_TP*), which explains why the earlier pkill failed and informs future kill commands.
  3. Confirmation that the GPU memory was not actually "stuck" — it was being legitimately held by live processes. This ruled out the LXC driver issue hypothesis and pointed to a simple process management fix.
  4. A diagnostic methodology: The combination of nvidia-smi --query-compute-apps and fuser proved more effective than ps aux or nvidia-smi --query-gpu=memory.used alone for debugging stuck GPU memory.

The Thinking Process

The reasoning visible in this message follows a clear pattern:

  1. Observe symptom: GPU memory is stuck at 96.6 GiB per GPU despite killing vLLM processes.
  2. Form initial hypothesis: "This is a known issue with CUDA contexts in LXC containers." This is a reasonable first guess based on experience.
  3. Design diagnostic: Rather than acting on the hypothesis (e.g., by resetting the GPUs), the assistant designs a targeted diagnostic: check for remaining CUDA contexts using nvidia-smi --query-compute-apps and check device file ownership using fuser.
  4. Execute and interpret: The output immediately refutes the initial hypothesis. The processes are alive, just under different names.
  5. Implicit conclusion: The solution is now clear — kill these specific PIDs — which the assistant does in the very next message ([msg 2161]). This thinking process exemplifies a core principle of systems troubleshooting: always verify your assumptions before acting on them. The assistant could have wasted time resetting GPUs or rebooting the machine. Instead, a 30-second diagnostic command revealed the true cause and pointed to a trivial fix.

Broader Significance

While this message is small in scope, it illustrates a recurring pattern in large-model deployment: the gap between "the process was killed" and "the resources were released." Modern GPU inference frameworks like vLLM use complex multi-process architectures with worker processes, resource trackers, and shared memory segments. Killing the parent process doesn't always clean up the children, and pattern-based process killing is fragile when processes rename themselves.

The message also highlights the importance of having multiple diagnostic tools available. ps aux failed to find the workers; nvidia-smi --query-compute-apps succeeded. The fuser command provided corroborating evidence. In GPU-accelerated environments, the standard Linux process inspection tools can be misleading because CUDA processes register themselves with the driver in ways that aren't always visible to the process table.

Resolution

The resolution came swiftly in the following message ([msg 2161]). Armed with the exact PIDs, the assistant issued:

kill -9 212702 212703 212704 212705 212706 212707 212708 212709 212437 202515

After a five-second wait, all GPUs showed 0 MiB used. The service was then started successfully ([msg 2162], [msg 2163]), and the Kimi-K2.5-NVFP4 model was deployed as a production systemd service.

Conclusion

Message [msg 2160] is a masterclass in targeted diagnostics. In a single bash command, it refuted a plausible but incorrect hypothesis, revealed the true cause of a blocking issue, and provided the exact information needed for a fix. It demonstrates that in complex GPU deployment scenarios, the right diagnostic tool applied at the right moment can save hours of fruitless debugging. The message also serves as a cautionary tale about the assumptions we make when managing processes in modern ML inference stacks — assumptions that can silently fail when processes rename themselves, when container environments introduce quirks, and when our mental models don't match reality.