The Phantom GPU Memory: Debugging a CUDA Context Leak in the Shadow of a 547GB Model Deployment

In the middle of a high-stakes model deployment session — pivoting from a successfully benchmarked MiniMax-M2.5 FP8 deployment to the native INT4 variant of Kimi-K2.5, a 547GB, 1-trillion-parameter beast — there is a moment that could easily be overlooked. It appears as message [msg 2352], a brief exchange where the assistant, having just stopped a systemd service and verified that no vLLM processes are running, discovers that all 8 NVIDIA RTX PRO 6000 Blackwell GPUs still report approximately 96GB of memory used each. The message reads:

[assistant] No vLLM processes. But GPUs still show 96GB used. Must be a driver/context leak. Let me try: [bash] ssh root@10.1.230.174 "fuser -v /dev/nvidia* 2>/dev/null; echo '---'; nvidia-smi pmon -c 1 2>/dev/null | head -15"

This is a classic debugging moment in systems engineering: the moment when the expected state does not match the observed state, and the engineer must formulate a hypothesis, gather evidence, and iteratively close the gap. The message is short — barely a paragraph — but it sits at a critical inflection point in the session, and understanding it requires unpacking the full context of what came before and what follows.

The Context: Why This Message Exists

To understand why the assistant writes this message, we must trace the events of the preceding minutes. The session has been a whirlwind tour of deploying and benchmarking 1-trillion-parameter models on 8x Blackwell GPUs. In [msg 2337], the user issued a clear directive: "Deploy and benchmark native kimi k2.5 - https://huggingface.co/moonshotai/Kimi-K2.5 that is int4; Free disk space first probably." This was a pivot from the NVFP4 variant of Kimi-K2.5 (which had been abandoned earlier due to PCIe allreduce bottlenecks and poor throughput) and from MiniMax-M2.5 (which had just achieved an impressive ~4,000 tok/s at high concurrency with TP=8+EP).

The assistant's response was methodical. First, it checked disk space ([msg 2339]), finding 929GB available but with 540GB consumed by the NVFP4 model. It inspected the HuggingFace repository for the INT4 model (<msg id=2340-2341>), noting that it was 595GB across 64 safetensors shards, using compressed-tensors INT4 quantization with group_size=32, and critically, that only the MoE routed expert weights were quantized — attention layers remained in full precision. The assistant also noted with relief that kv_cache_scheme: null meant no FP8 KV cache, avoiding the SM120 compatibility issues that had plagued earlier deployments.

The NVFP4 model was deleted ([msg 2342]), freeing 1.3TB. The download commenced ([msg 2344]), completing in approximately 28 minutes at 547GB ([msg 2346]). Then came the moment to stop the MiniMax service and launch the new model.

In [msg 2348], the assistant attempted to kill any MiniMax vLLM processes. But when it checked nvidia-smi in [msg 2349], all 8 GPUs still showed ~96,800 MiB used — essentially full. The assistant's first hypothesis was that the systemd service was still running ([msg 2350]): "MiniMax is still loaded. The systemd service must be running." It stopped and disabled the vllm-minimax-m25 service, waited 5 seconds, and checked again. The GPUs still showed 96GB used. This was puzzling.

In [msg 2351], the assistant checked for lingering processes with pgrep -af vllm and pgrep -af &#39;python3.*api_server&#39;. The result was essentially empty — no vLLM processes found. This deepened the mystery: if no processes were holding the GPU, why was memory still allocated?

The Message Itself: Hypothesis Formation

This brings us to [msg 2352]. The assistant now faces a contradiction: no vLLM processes visible, yet 96GB of GPU memory consumed per device. Its reasoning is explicit: "Must be a driver/context leak." This is a sophisticated diagnostic leap. The assistant is not assuming that its process detection was wrong; instead, it is reasoning about the mechanism by which GPU memory could remain allocated after all owning processes have exited.

In CUDA, when a process that has allocated GPU memory terminates abnormally — or when child processes (worker processes spawned by a distributed runtime like vLLM's Ray or multiprocessing backend) are killed without proper cleanup — the CUDA driver can retain the memory context. This is known as a "zombie context" or "stale context" leak. The NVIDIA driver holds the memory until the process that created the context is definitively reaped by the kernel, or until the GPU is reset. The assistant's hypothesis is precisely this: the worker processes that MiniMax's vLLM deployment spawned were killed abruptly (via kill -9), and their CUDA contexts were not properly cleaned up.

To test this hypothesis, the assistant runs two commands. First, fuser -v /dev/nvidia* — a Linux utility that identifies which processes have file descriptors open on the NVIDIA device files. This is a lower-level check than pgrep, because it interrogates the kernel's process-file descriptor table directly. If any process (even a zombie or a process with a different name) holds /dev/nvidia0 through /dev/nvidia7 open, fuser will find it. Second, nvidia-smi pmon -c 1 — a command that polls the GPU process monitor, showing which processes are currently running on each GPU. This is the GPU driver's own accounting, independent of the Linux process table.

The output that comes back is garbled — the fuser output appears corrupted, with repeated "kernel" strings and PID numbers bleeding together. This is itself a clue: the output suggests that the SSH session or terminal handling is mangling the response, possibly due to binary data or control characters in the fuser output. Despite the corruption, the assistant can extract useful information: PIDs like 215004, 233458, and 233723-233730 are visible.

What Follows: The Resolution

The next message ([msg 2353]) shows the assistant acting on this information. It identifies these as "zombie worker processes still holding GPU memory" and kills them with kill -9. After this, most GPUs free their memory — but GPU 5 stubbornly retains 96,798 MiB. This leads to a more aggressive cleanup in [msg 2354], where the assistant uses fuser to find all processes holding any NVIDIA device file and kills them all at once. Finally, all 8 GPUs report 0 MiB used.

This sequence is a textbook example of layered debugging: starting with high-level process detection (pgrep), moving to service management (systemctl), then to kernel-level file descriptor inspection (fuser), and finally to brute-force process termination. Each layer reveals more information and narrows the gap between expected and observed state.

Assumptions and Potential Mistakes

The assistant makes several assumptions in this message. First, it assumes that the GPU memory leak is a "driver/context leak" rather than, say, a persistent CUDA IPC handle or a shared memory segment. This assumption is reasonable — context leaks are the most common cause of phantom GPU memory after process termination — but it is not the only possibility. For instance, NVIDIA's CUDA IPC mechanism can leave shared memory handles in /dev/shm that keep GPU allocations alive. The assistant does not check for this until later (in [msg 2327], it cleaned /dev/shm/psm_* and similar files before launching MiniMax with EP, but does not re-check here).

Second, the assistant assumes that the PIDs visible in the garbled fuser output are the culprits. This turns out to be correct — killing them frees most GPUs — but the garbled output could have been misleading. The assistant's willingness to proceed despite corrupted terminal output is a practical skill: in real systems debugging, you often work with imperfect data and must make judgment calls.

Third, the assistant assumes that killing worker processes with kill -9 is safe. In a production deployment, this could leave shared state (like NCCL communicators or IPC handles) in an inconsistent state, potentially requiring a GPU reset. The assistant implicitly accepts this risk because the GPUs will be immediately re-initialized by the new model load.

Input and Output Knowledge

To fully understand this message, the reader needs several pieces of input knowledge. First, familiarity with the CUDA programming model and the concept of GPU contexts — the idea that a process "attaches" to a GPU and allocates memory that persists until the process exits or the context is destroyed. Second, knowledge of the Linux fuser command and its role in identifying processes holding specific file descriptors. Third, understanding of distributed inference architectures like vLLM, where a leader process spawns multiple worker processes (one per GPU) that each hold their own CUDA context. Fourth, awareness of the systemd service management model and the distinction between stopping a service and ensuring all child processes are reaped.

The output knowledge created by this message is the set of PIDs holding GPU resources. This information is immediately actionable — it enables the assistant to proceed to the next step of forcibly terminating those processes. More broadly, the message creates a diagnostic pattern: when GPU memory persists after process termination, check at multiple levels (process table, file descriptors, GPU driver state) before concluding that a hardware reset is needed.

The Thinking Process

The assistant's reasoning in this message is concise but reveals a clear mental model. The sequence is:

  1. Observe anomaly: GPUs show 96GB used despite no vLLM processes.
  2. Form hypothesis: "Must be a driver/context leak." This connects the observation to a known failure mode.
  3. Design experiment: Run fuser -v /dev/nvidia* to find processes holding GPU device files at the kernel level, and nvidia-smi pmon to check the GPU driver's own process list.
  4. Execute and interpret: Run the commands, parse the garbled output, extract PIDs. The thinking is notable for what it does not do. The assistant does not immediately jump to "reset the GPUs" or "reboot the machine" — the nuclear options. Instead, it attempts a targeted investigation, using tools that probe different layers of the system stack. This reflects an engineering mindset of minimizing disruption: a GPU reset would require re-initializing all CUDA contexts when the new model loads, which is time-consuming. Killing a few zombie processes is far cheaper.

Significance in the Larger Narrative

This message, while brief, marks a critical transition in the session. The assistant is in the process of swapping one 1T-parameter model for another — a delicate operation involving 547GB of disk I/O, 96GB × 8 = 768GB of GPU memory deallocation and reallocation, and complex distributed runtime state. The phantom GPU memory is a obstacle that, if not resolved, would prevent the new model from loading (vLLM would see insufficient free memory and crash). The assistant's successful diagnosis and cleanup of the zombie contexts is a necessary precondition for the next phase: loading and benchmarking the INT4 Kimi-K2.5.

The message also illustrates a recurring theme in this session: the tension between the high-level orchestration of model deployment (downloading, configuring, launching) and the low-level systems debugging required when things go wrong. The assistant must fluidly switch between these modes, from reading HuggingFace model cards to parsing garbled fuser output, all within the span of a few messages.

In the end, the zombie processes are killed, the GPUs are freed, and the INT4 Kimi-K2.5 loads successfully — achieving 82 tok/s single-stream and scaling to 2,276 tok/s at high concurrency, ultimately deployed as a production systemd service. But none of that would have been possible without the diagnostic work in this unassuming message, where the assistant confronted a contradiction, formed a hypothesis, and dug deeper to find the truth hiding in the kernel's file descriptor table.