The Kernel Mount Revelation: Diagnosing Stuck GPU Memory in an SGLang Inference Pipeline

The Message

In the midst of a complex optimization session for SGLang inference throughput on an 8-GPU system, the assistant executed a single, deceptively simple command:

ssh root@10.1.230.174 'fuser -v /dev/nvidia0 /dev/nvidia2 2>&1'

The output was equally sparse:

                     USER        PID ACCESS COMMAND
/dev/nvidia0:        root     kernel mount /dev/nvidia0
/dev/nvidia2:        root     kernel mount /dev/nvidia2

This message — message 3858 in the conversation — is a diagnostic probe. It is not a solution, not a configuration change, and not a triumphant breakthrough. It is a moment of investigation, a check of assumptions, and a pivot point that reveals the nature of a stubborn problem. Understanding why this particular command was chosen, what it reveals, and how it reshapes the debugging strategy offers a window into the disciplined, methodical work of systems engineering at scale.

The Context: A Server That Won't Die Cleanly

To understand message 3858, we must first understand the events that led to it. The session had been a marathon of optimization. The assistant and user were deploying the Kimi-K2.5 model (a massive 671B-parameter Mixture-of-Experts architecture) using SGLang on a machine with 8 NVIDIA RTX PRO 6000 Blackwell GPUs. The inference pipeline was generating synthetic training data for an EAGLE-3 speculative decoding drafter, and throughput was the critical metric.

The bottleneck was clear: KV cache capacity. With --mem-fraction-static 0.85, SGLang could hold approximately 116,000 tokens in GPU KV cache. With requests averaging 4,000+ tokens each, only about 28 concurrent requests could fit at steady state. The scheduler was blocking new prefills while waiting for existing requests to complete and free KV cache space. Effective throughput was stuck at roughly 400-800 tok/s, far below what the hardware should deliver.

The user's directive was unambiguous: "Use all levers." The assistant identified two primary knobs: increasing --mem-fraction-static to allocate more GPU memory to KV cache, and enabling SGLang's hierarchical cache (--enable-hierarchical-cache) to spill KV cache entries to host RAM. The machine had 449GB of system memory, of which roughly 408GB was available — a massive reservoir for KV cache overflow.

The assistant's first attempt was bold: launch with --mem-fraction-static 0.93 and --hicache-size 300, expecting 300GB of host RAM to be used for hierarchical cache. The server started loading, but the health check timed out after 600 seconds. When the assistant investigated, it discovered the system had consumed 439GB of RAM (out of 449GB total), with only 9GB free. Worse, some TP (tensor parallelism) worker processes were defunct.

The critical realization came in message 3854: "That's a problem — each TP rank is trying to allocate 300GB. 8 × 300GB = 2.4TB, far more than the 449GB total RAM." The --hicache-size parameter was being interpreted per-rank, not as a total allocation. This was an incorrect assumption about SGLang's configuration semantics — an expensive one that consumed nearly all system memory and left the GPUs in an unclean state.

The Stubborn GPU: Why Memory Won't Release

After killing the SGLang processes with pkill -9 -f sglang and pkill -9 python3, and attempting to free the NVIDIA devices with fuser -k /dev/nvidia*, the assistant checked GPU memory with nvidia-smi. The result was troubling: GPUs 0 and 2 still showed 96,413 MiB allocated each (message 3856). The kill commands had apparently not freed the GPU memory.

This is a common but frustrating scenario in GPU computing. When a CUDA application crashes or is killed forcefully, the CUDA driver context may not be properly destroyed. The GPU memory remains allocated to the process even though the process itself is gone. Standard kill signals — even SIGKILL — cannot always clean up this state because the memory is held by the kernel-mode driver, not by the user-space process.

The assistant tried fuser -k -9 /dev/nvidia* again (message 3857), which should kill any processes using those device files. But the GPU memory remained stubbornly allocated. This is where message 3858 enters the picture.

Why fuser -v Was the Right Tool

The assistant's choice of fuser -v (verbose mode) on the specific device files /dev/nvidia0 and /dev/nvidia2 was a deliberate diagnostic decision. The standard fuser command identifies which processes are using specified files or sockets. The -v flag produces verbose output, showing the user, PID, access type, and command for each process using the file.

The assistant had already tried fuser -k (kill mode) on all NVIDIA devices, which should have terminated any processes holding those files. When that failed to free memory, the natural next step was to investigate why the kill didn't work. The -v flag provides the diagnostic information needed to understand the nature of the lock.

The output revealed something crucial: the only "process" holding /dev/nvidia0 and /dev/nvidia2 was "kernel mount" — a kernel-level reference, not a user-space process. There was no PID listed, no user-space command to kill. This is fundamentally different from a zombie process or a stuck application. The GPU memory is being held by the NVIDIA kernel driver itself, likely due to a CUDA context that was not properly released during the abrupt termination of the SGLang server.

The Assumption That Was Tested and Refuted

Message 3858 tests a critical assumption: that the GPU memory is held by a user-space process that can be killed. The assistant had been operating under this assumption, repeatedly sending kill signals to Python processes and using fuser -k on NVIDIA device files. The fuser -v output refutes this assumption definitively.

The assistant's earlier approach — escalating kill signals from pkill to fuser -k to fuser -k -9 — was based on the mental model that some process was stubbornly holding the GPU. Each escalation was a reasonable step in that model. But the model itself was wrong. No amount of process killing would free memory held by the kernel driver.

This is a classic debugging pattern: when a fix doesn't work, you don't just try harder — you investigate why it doesn't work. The fuser -v command is the investigation, and its output provides the evidence needed to reject the current hypothesis and formulate a new one.

Input Knowledge Required

To understand this message, one needs several pieces of contextual knowledge:

  1. The GPU memory model: CUDA applications allocate GPU memory through the driver. When a process is killed, the driver should release its memory, but in practice, crashes and abrupt terminations can leave orphaned contexts.
  2. The fuser command semantics: fuser identifies processes using files or sockets. The -v flag provides verbose output including access type. The "kernel mount" access type indicates a kernel-level reference that cannot be killed with user-space signals.
  3. The SGLang architecture: SGLang uses tensor parallelism (TP), where the model is sharded across multiple GPUs. Each TP rank is a separate process. The --hicache-size parameter is interpreted per-rank, which was the original configuration mistake.
  4. The history of failed cleanup: The assistant had already attempted multiple kill strategies. The persistence of GPU memory on GPUs 0 and 2 specifically (not all 8 GPUs) suggests an asymmetry in how the processes terminated — perhaps some ranks crashed differently than others.
  5. The system's memory state: From message 3856, we know 318GB of RAM was still in use after the kill attempts, indicating leaked allocations from the failed hicache experiment.

Output Knowledge Created

The output of message 3858 is a single, unambiguous piece of information: the GPU devices are held by kernel mount references, not by user processes. This knowledge fundamentally changes the debugging strategy:

  1. Process killing is futile: No amount of pkill, kill, or fuser -k will free these GPUs. The solution must operate at the kernel or driver level.
  2. The fix must be a GPU reset: The appropriate next step is nvidia-smi --gpu-reset for the affected GPUs, or potentially unloading and reloading the NVIDIA kernel module (nvidia_uvm, nvidia_drm, nvidia_modeset, nvidia). This is a more invasive operation that may affect other GPU workloads on the system.
  3. The hicache configuration must be revised: The original plan of --hicache-size 300 is invalid. The assistant must determine the correct per-rank allocation — likely 300 / 8 = 37.5GB, or more conservatively, a value that leaves headroom for the OS and other processes.
  4. Cleanup procedures need revision: Future experiments with hierarchical cache should include a cleanup plan that accounts for the possibility of kernel-level GPU locks.

The Thinking Process Visible in the Reasoning

While message 3858 itself contains only a bash command and its output, the reasoning behind it is visible in the sequence of messages leading up to it. The assistant's thinking follows a clear pattern:

Hypothesis → Test → Result → New Hypothesis

The initial hypothesis was "GPU memory is held by a user-space process." The test was repeated kill commands. The result was persistent memory allocation. The new hypothesis, formed implicitly, was "maybe the lock is at a different level." The test for this new hypothesis was fuser -v — specifically targeting the two stuck GPUs.

The choice of fuser -v over alternatives (like checking /proc entries, using lsof, or examining CUDA driver state) reveals the assistant's mental model. The assistant expected to find a PID, perhaps a zombie process or a process that had detached from its parent. The verbose flag was chosen to get the full picture — user, PID, access type, and command — all in one line per device.

The output's sparseness is itself informative. A normal fuser -v output for a GPU in use by a running process would show something like:

                     USER        PID ACCESS COMMAND
/dev/nvidia0:        root     123456 F.... python3

The absence of a PID and the presence of "kernel mount" instead of a command name is the diagnostic signal. The assistant can immediately conclude that this is a kernel-level issue, not a process-level issue.

Broader Implications for Systems Debugging

Message 3858 exemplifies a debugging principle that extends far beyond GPU computing: when a corrective action fails, the failure itself is data. The assistant could have continued escalating — trying kill -9 with different process IDs, rebooting the SGLang server without cleaning the GPUs, or simply giving up and asking the user for help. Instead, it chose to investigate the nature of the resistance.

This is the difference between debugging by trying things and debugging by understanding. The fuser -v command is cheap, fast, and diagnostic. It doesn't fix the problem, but it reveals the problem's true nature. Once you know the GPU is held at the kernel level, you stop wasting time on process-level solutions and move to driver-level interventions.

The message also illustrates the importance of reading tool documentation carefully. The --hicache-size 300 mistake — interpreting a per-rank parameter as a total — cost the assistant significant time and nearly exhausted system memory. A more careful reading of SGLang's configuration semantics might have prevented the entire episode. But the assistant's response to the mistake was methodical: diagnose, understand, and adapt.

Conclusion

Message 3858 is a small message with outsized significance. It is the moment when the assistant shifts from trying to fix a problem to understanding it. The fuser -v command reveals that the GPU memory lock is at the kernel level, not the process level — a finding that invalidates the previous debugging approach and points toward a new strategy involving GPU resets or driver reloads.

In the broader narrative of the coding session, this message represents a necessary pause. The assistant had been moving fast — launching servers, killing processes, checking memory — but the persistent GPU allocation forced a moment of reflection. The output of fuser -v is the evidence that the assistant needs to change tactics. It is a reminder that in complex systems engineering, the most valuable tool is not the biggest hammer but the most precise diagnostic instrument.