The Stuck GPU: A Lesson in Memory Management at 93,699 MiB

The Message

[assistant] [bash] ssh root@10.1.230.174 'nvidia-smi --query-gpu=index,memory.used --format=csv,noheader; free -g'
0, 1 MiB
1, 1 MiB
2, 1 MiB
3, 99 MiB
4, 93699 MiB
5, 99 MiB
6, 3 MiB
7, 3 MiB
               total        used        free      shared  buff/cache   available
Mem:             449           3         396           0          48         445
Swap:              0           0           0

At first glance, this appears to be a routine diagnostic command — check GPU memory, check system memory, move on. But this message, message 3898 in a long coding session spanning dozens of segments and thousands of exchanges, captures a pivotal debugging moment. It is the culmination of a multi-step cleanup operation that went wrong, and its output tells a story that every engineer working with multi-GPU systems will recognize: the story of the stuck GPU.

Context: The Road to This Message

To understand why this simple nvidia-smi command was issued, we must trace the events of the preceding minutes. The session had been running a large-scale inference pipeline for the Kimi-K2.5 model using SGLang across 8 NVIDIA RTX PRO 6000 Blackwell GPUs. The server had been tuned extensively: first with mem_fraction_static=0.93 (which crashed with an OOM), then backed off to 0.88 with hierarchical cache enabled (--enable-hierarchical-cache --hicache-size 48). This configuration achieved a respectable 159,277 GPU tokens and throughput of approximately 930–1,350 tokens per second ([msg 3884], [msg 3888]).

But the assistant was not satisfied. The bottleneck was GPU KV cache capacity: with 159K tokens and an average of ~4,000 tokens per request, only about 40 concurrent decode requests could fit. The queue was growing to over 100 requests while only 35 were actively decoding ([msg 3893]). The assistant correctly identified that hierarchical cache (hicache) helps with evicted radix cache entries and prefix reuse, but does not increase the number of actively generating requests — their KV must live on GPU.

The proposed solution was --kv-cache-dtype fp8_e4m3, which would quantize the KV cache from bf16 to fp8, roughly doubling effective capacity from 159K to ~318K tokens ([msg 3894]). This is a reasonable optimization: the Kimi-K2.5 model uses Multi-head Latent Attention (MLA), where the KV cache is already a compressed latent representation, but further quantizing to FP8 is generally safe and would allow approximately 80 concurrent 4K-token requests.

The Cleanup That Went Wrong

To apply the new configuration, the assistant needed to shut down the running SGLang server and inference processes. It issued a kill command cascade: pkill -f run_inference; pkill -9 -f sglang; pkill -9 python3; sleep 3; fuser -k /dev/nvidia* ([msg 3894]). This is the nuclear option — kill by process name, kill by Python, kill anything holding NVIDIA device files.

But when the assistant checked the result ([msg 3895]), all eight GPUs still showed approximately 93,781 MiB allocated — essentially full GPU memory. The processes had been killed, but the memory was not released. The assistant's response was terse and telling: "Zombies again."

This is a well-known phenomenon in GPU computing. When a CUDA process is killed with SIGKILL (-9), the GPU memory can remain allocated because the CUDA driver's cleanup path is not triggered. The GPU contexts persist as "zombie" allocations until the driver resets them or the process that opened the CUDA context is truly reaped at the kernel level. The fuser -k /dev/nvidia* command, which kills any process holding open file handles to NVIDIA devices, is supposed to handle this — but it failed.

The assistant escalated. It tried to kill processes through the Proxmox container host (pct exec 129) using fuser directly ([msg 3896]). That also failed — the GPUs still showed 93,781 MiB. Then it tried an even more aggressive approach: ps aux | grep python3 | grep -v grep | awk "{print \$2}" | xargs -r kill -9 ([msg 3897]), which finds every Python process on the system and kills it. This ran without visible output, leaving the assistant uncertain whether it had succeeded.

What Message 3898 Reveals

This brings us to the subject message. The assistant issues a verification command: nvidia-smi --query-gpu=index,memory.used --format=csv,noheader; free -g. The output is striking in its asymmetry:

• GPUs 0, 1, 2: 1 MiB — completely clean, just the minimal overhead of the NVIDIA driver
• GPUs 6, 7: 3 MiB — essentially clean
• GPUs 3, 5: 99 MiB — a small residual allocation, possibly from a lightweight process or driver overhead
• GPU 4: 93,699 MiB — still fully allocated GPU 4 is stuck. Despite multiple kill attempts at escalating levels of aggression, one GPU refuses to release its memory. The system memory check (free -g) shows 445 GB available out of 449 GB total — the host is clean, no system memory pressure. This is purely a GPU memory problem. The 99 MiB on GPUs 3 and 5 is also interesting. This is likely the "stub" allocation that the NVIDIA driver or CUDA runtime leaves behind when a context is partially cleaned up — a small footprint that indicates the processes were killed but the driver hasn't fully reset the device state. On GPU 4, the full 93.7 GB remains, suggesting a process is still holding the context, or the driver is in a state where it cannot reclaim the memory without a full GPU reset.

Reasoning and Decision-Making

The assistant's reasoning in issuing this command is straightforward but critical: verify before proceeding. After an aggressive cleanup operation with uncertain results, the first step is always to check whether the cleanup actually worked. The assistant could have assumed the cleanup succeeded and immediately launched the new server — which would have crashed on GPU 4 with an out-of-memory error, wasting time and potentially corrupting state. Instead, it took the disciplined approach of verification.

The choice of nvidia-smi --query-gpu=index,memory.used --format=csv,noheader is deliberate. The --query-gpu=index,memory.used flag requests only the GPU index and memory usage columns, and --format=csv,noheader produces machine-parseable output without headers. This is designed for script consumption — the assistant could easily parse this output programmatically to detect anomalies. The addition of free -g checks system memory in gigabytes, providing a complete picture of both GPU and host memory health in a single command.

Assumptions and Potential Mistakes

The assistant made several assumptions that deserve scrutiny:

1. That killing processes would release GPU memory. This is the fundamental assumption underlying the entire cleanup operation. In practice, SIGKILL does not always trigger CUDA's cleanup handlers. The CUDA driver maintains per-process contexts, and if the kernel's process reaping is delayed or incomplete, the GPU memory remains allocated. The assistant's escalation from pkill to fuser to xargs kill -9 reflects a growing awareness that standard kill methods were insufficient.
2. That the zombie processes were Python processes. The assistant repeatedly targeted Python processes (pkill -9 python3, ps aux | grep python3). But the zombie could be a non-Python process — perhaps a CUDA runtime worker thread, a NCCL helper process, or even a kernel module that opened a CUDA context. By narrowing the search to Python, the assistant may have missed the actual holder of GPU 4's memory.
3. That fuser -k /dev/nvidia* would be sufficient. The fuser command kills processes holding specific files open. NVIDIA GPU devices appear as /dev/nvidia0 through /dev/nvidia7 and /dev/nvidiactl. Killing processes holding these files should release GPU contexts. But if the process has already been killed and the file handle is held by a defunct process in a zombie state, fuser may not be able to act.
4. That the Proxmox container escape was necessary. The assistant switched from ssh root@10.1.230.174 (direct to the container) to ssh root@10.1.2.6 'pct exec 129 ...' (via the Proxmox host). This assumes the container-level kill was insufficient and host-level intervention was needed. While this is a reasonable escalation, it introduces additional complexity and potential for error.

Input Knowledge Required

To fully understand this message, one needs:

• Understanding of GPU memory management: How CUDA contexts work, that GPU memory is allocated per-process and persists until the process exits or the context is destroyed, and that SIGKILL can leave orphaned memory.
• Knowledge of nvidia-smi: The --query-gpu flag for structured output, the --format=csv,noheader option for machine parsing, and the interpretation of memory usage values (1 MiB = clean, 93,699 MiB = fully allocated on a 96 GB GPU).
• Awareness of the SGLang architecture: That SGLang uses tensor parallelism across 8 GPUs, meaning all GPUs are loaded with model weights simultaneously, and a clean restart requires all GPUs to be free.
• Familiarity with Proxmox container management: The pct exec command for executing commands inside a Proxmox container from the host.
• The concept of "zombie" GPU processes: A known pain point in multi-GPU ML environments where killed processes leave behind GPU memory allocations that require a driver reset or system reboot to clear.

Output Knowledge Created

This message produces several pieces of actionable knowledge:

1. GPU 4 is stuck. The most important finding. Any attempt to restart SGLang will fail unless GPU 4 is freed. The assistant must now decide: try a GPU reset via nvidia-smi --gpu-reset, reboot the container, or reboot the entire host.
2. GPUs 0-2 and 6-7 are clean. These GPUs are ready for a new server. If the assistant could somehow exclude GPU 4 (e.g., by using --tp-size 7 or --visible-devices), it could start a degraded server. But SGLang with tensor parallelism typically requires all GPUs in the topology to be available.
3. System memory is abundant. With 445 GB available, the host is not under memory pressure. The problem is isolated to the GPU subsystem.
4. The cleanup escalation path was partially effective. GPUs 0-2 and 6-7 were freed by the aggressive kill sequence, suggesting that the fuser and xargs approach works for most processes. GPU 4 represents a special case that requires further investigation.

The Thinking Process

The assistant's thinking, visible across the sequence of messages, follows a classic debugging pattern:

1. Observe symptom: Throughput dropped to 606 tok/s, KV cache saturated at 0.94 usage.
2. Hypothesize root cause: GPU KV cache capacity limits concurrent decode requests.
3. Propose solution: FP8 KV cache quantization to double capacity.
4. Execute solution: Kill current server, restart with new flag.
5. Encounter obstacle: GPU memory not released after kill.
6. Escalate: Try stronger kill methods (pkill → fuser → xargs).
7. Verify: Check if cleanup worked (message 3898).
8. Discover new symptom: GPU 4 stuck at 93,699 MiB. This is the essence of systems debugging: each attempt to fix one problem reveals another, deeper problem. The assistant is now at step 8, faced with a GPU that refuses to release its memory. The next steps would likely involve nvidia-smi --gpu-reset, checking for lingering processes with lsof /dev/nvidia*, or ultimately a reboot.

Broader Significance

This message, for all its apparent simplicity, encapsulates a fundamental challenge in large-scale ML inference: memory management is hard, and cleanup is harder than setup. Setting up an 8-GPU inference server with SGLang is a well-documented process. But tearing it down cleanly, especially after a crash or forced kill, requires deep understanding of the CUDA driver model, process management, and system administration.

The asymmetry of the output — six GPUs clean, one GPU partially clean, one GPU fully stuck — is a perfect illustration of why "just kill the process" is never a complete answer in GPU computing. Each GPU can end up in a different state depending on which processes held its context, how those processes were terminated, and the timing of the termination signals.

For engineers working with multi-GPU systems, message 3898 serves as a reminder: always verify your cleanup, and never assume that killing a process frees its GPU memory. The 93,699 MiB on GPU 4 is a silent witness to the gap between what we intend and what the system actually does.