The Diagnostic Snapshot: Reading the Wreckage After an OOM Disaster

Introduction

In the high-stakes world of large language model inference, few events are as disruptive as an out-of-memory (OOM) crash on an 8-GPU production server. When the SGLang inference server — configured with an incorrectly scaled hierarchical cache parameter — consumed all 449GB of system RAM and hung indefinitely, the immediate priority shifted from throughput optimization to damage assessment. Message <msg id=3856> captures this exact moment: a single bash command, dispatched over SSH, that reads the vital signs of a machine that has just been through a catastrophic memory overcommit. The output is deceptively simple — a free -g report and an nvidia-smi GPU memory dump — but it tells a story of partial cleanup, asymmetric GPU state, and the lingering ghosts of a failed server launch.

The Context: A Server That Ate 2.4TB

To understand message <msg id=3856>, one must first understand the chain of events that led to it. The assistant had been tuning the SGLang inference server for the Kimi-K2.5 model, running across 8 NVIDIA RTX PRO 6000 Blackwell GPUs. The bottleneck was clear: the KV cache could only hold ~116,000 tokens at --mem-fraction-static 0.85, limiting concurrent requests to roughly 50 despite a 150-request concurrency setting. The solution seemed obvious: enable SGLang's hierarchical cache (--enable-hierarchical-cache), which spills KV cache entries to host RAM when GPU memory runs low. With 449GB of system RAM available, the assistant calculated that 300GB of hicache could expand the effective KV capacity to ~410,000 tokens — a 3.5× improvement.

The launch command in <msg id=3850> specified --hicache-size 300. What the assistant did not realize — and what became painfully clear only after the server failed to start — is that --hicache-size is specified per tensor-parallel rank, not as a total across all GPUs. With --tp-size 8, each of the 8 ranks attempted to allocate 300GB of host memory. That is 2.4TB of requested RAM on a machine with 449GB. The result was an immediate OOM condition: system memory usage spiked to 439GB, leaving only 9GB free, and the server hung indefinitely as different ranks deadlocked waiting for allocations that could never be satisfied.

The timeout in <msg id=3852> — the bash tool terminated after exceeding 600 seconds — was the first sign of trouble. When the assistant finally inspected the logs in <msg id=3853>, it found that the server had partially initialized (CUDA graphs were captured on some ranks) but never reached the ready state. The diagnosis in <msg id=3854> correctly identified the per-rank allocation issue. The kill commands in <msg id=3855> attempted to clean up: pkill -9 -f sglang, pkill -9 python3, fuser -k /dev/nvidia*. Message <msg id=3856> is the diagnostic follow-up — the assistant checking whether the cleanup actually worked.

What the Message Actually Shows

The command is straightforward:

ssh root@10.1.230.174 'free -g; echo "---"; nvidia-smi --query-gpu=index,memory.used --format=csv,noheader'

The output contains two sections. First, the system memory report:

               total        used        free      shared  buff/cache   available
Mem:             449         318         130           0           0         130

Second, the GPU memory report:

0, 96413 MiB
1, 99 MiB
2, 96417 MiB
3, 51 MiB
4, 3 MiB
5, 3 MiB
6, 3 MiB
7, 7 MiB

This output is remarkable for what it reveals about the state of cleanup. System memory usage dropped from 439GB (the OOM peak) to 318GB — a recovery of 121GB, but still far above the ~40GB baseline that was typical before the server launch. This means that 318GB of RAM is still allocated, likely representing leaked memory from the failed server processes that pkill could not fully reclaim. The available field shows 130GB, which is the same as free — indicating no page cache or buffers, which is unusual and suggests the system is still under memory pressure.

The GPU memory picture is even more telling. GPUs 0 and 2 each show ~96GB allocated — nearly all of their 97,887 MiB capacity. GPUs 1, 3, 4, 5, 6, and 7 show negligible usage (3-99 MiB). This asymmetry is the key finding. Why are only GPUs 0 and 2 still held? The most likely explanation is that the tensor-parallel ranks assigned to those GPUs did not fully terminate. In SGLang's TP-8 configuration, each rank owns a slice of the model weights and KV cache. When the server was killed mid-initialization, some ranks may have been in different stages of the startup sequence — ranks 0 and 2 may have already allocated their full GPU memory (model weights + KV cache) before the OOM hit, while the other ranks were still initializing. The pkill commands may have missed the processes holding GPU 0 and 2, or those processes may have been in an uninterruptible sleep state (D-state) waiting for memory allocations that would never complete.

Why This Message Was Written: The Reasoning and Motivation

The assistant's motivation for dispatching this command is rooted in a critical decision point. Before attempting to restart the server with corrected parameters, the assistant needs to know whether the machine is in a clean state. Launching SGLang on a machine with leaked GPU memory would risk an immediate OOM on the GPUs, or worse, silent corruption from residual allocations. The assistant is practicing defensive operations: verify cleanup before proceeding.

There is also a diagnostic purpose. The assistant is trying to understand the failure mode. The system RAM at 318GB is a puzzle — if the processes were killed, why is so much memory still allocated? The answer could be that the kernel has not yet reclaimed the memory (slab allocator, page tables, or other kernel structures), or that some processes survived the kill. The GPU memory asymmetry on GPUs 0 and 2 is another diagnostic clue — it tells the assistant that the kill was not uniform across all ranks, and that a more aggressive cleanup strategy is needed.

The message also serves as a reality check. The assistant had been optimistic about the hierarchical cache approach, calculating that 300GB would yield 410K tokens. The failure revealed a critical misunderstanding of the configuration parameter. This diagnostic snapshot is the moment where the assistant confronts the gap between theory and practice — the clean calculations of <msg id=3842> meet the messy reality of a machine with 318GB of leaked RAM and two GPUs still locked.

Assumptions Made

Several assumptions underpin this message, some explicit and some implicit. The primary assumption is that the pkill and fuser commands in <msg id=3855> actually terminated all SGLang processes. The output of this message directly tests that assumption — and finds it partially false. The assistant assumed that killing the Python processes would release all GPU memory, but GPUs 0 and 2 remain allocated.

Another assumption is that the free -g output accurately reflects reclaimable memory. The 318GB figure may include kernel slab allocations, cached pages, or memory-mapped files that the kernel will release under pressure. The available field (130GB) is the more relevant metric for whether new allocations will succeed, but the assistant does not comment on this distinction.

The assistant also assumes that the asymmetric GPU state is a cleanup artifact rather than hardware damage or persistent CUDA context leakage. This is a reasonable assumption — CUDA contexts can survive process termination if the GPU driver does not fully reset the state — but it is an assumption nonetheless.

Input Knowledge Required

To fully understand this message, the reader needs substantial context. One must know that the server was configured with --tp-size 8, explaining why 8 ranks exist and why GPU memory allocation might be asymmetric. One must understand that --hicache-size 300 was specified per-rank, leading to the 2.4TB overcommit. One must know the baseline memory usage: before the server launch, system RAM was at ~40GB (from <msg id=3843>), and each GPU had ~92GB allocated during normal operation (from <msg id=3835>). The contrast between the 92GB baseline and the 96GB peak on GPUs 0 and 2 tells the reader that those GPUs still hold the model weights plus some residual KV cache.

Knowledge of SGLang's architecture is also required. The hierarchical cache feature is relatively new, and the fact that --hicache-size is per-rank rather than total is not obvious from the parameter name. The reader must understand tensor parallelism and how model weights are sharded across GPUs to interpret why some GPUs are clean while others are not.

Output Knowledge Created

This message creates critical diagnostic knowledge. It establishes that the cleanup was incomplete — system RAM is still 318GB, and GPUs 0 and 2 are still fully allocated. This knowledge directly drives the next action: the assistant must escalate the cleanup. In the subsequent message <msg id=3857>, the assistant attempts fuser -k -9 /dev/nvidia* with the kill-9 flag, which is more aggressive than the previous fuser -k. When that also fails to clear GPUs 0 and 2, the assistant will need to resort to nvidia-smi GPU reset or even a system reboot.

The message also creates knowledge about the failure mode of the hierarchical cache feature. The per-rank allocation behavior is now empirically demonstrated — 8 ranks × 300GB = 2.4TB request on a 449GB machine. This is a concrete data point that will inform future configuration: --hicache-size must be divided by the TP size, so for TP-8, a total of 300GB means --hicache-size 37.5 per rank.

The Thinking Process Visible in the Reasoning

The assistant's reasoning is visible in the choice of this specific command. Rather than checking a single metric, the assistant checks both system memory and GPU memory in a single SSH invocation. This reveals a systems-thinking approach: the OOM affected both host RAM and GPU VRAM, and both must be verified before proceeding. The use of free -g (gigabyte units) rather than free -m (megabyte units) is a deliberate choice for readability at scale — when dealing with 449GB totals, megabyte precision is noise.

The assistant is also implicitly comparing the output against expected baselines. The 318GB system RAM figure is compared against the pre-launch baseline of ~40GB. The GPU figures are compared against the expected clean state of ~3-7 MiB (as seen on GPUs 4-7). The asymmetry between GPUs 0/2 and the rest is a pattern that immediately signals a non-uniform cleanup.

The thinking also reveals a debugging methodology: kill first, then verify. This is the classic "observe → intervene → observe" loop of systems debugging. The assistant does not assume the kill succeeded; it verifies empirically. This is a mature operational practice.

Mistakes and Incorrect Assumptions

The most significant mistake visible in the chain is the incorrect assumption about --hicache-size semantics. The assistant calculated in <msg id=3842> that 300GB would yield 294K additional tokens, but this calculation assumed the 300GB was shared across all ranks. In reality, each rank independently allocates its own hicache pool, so the total host memory consumed would be 8 × 300GB = 2.4TB. The assistant's calculation should have divided by TP size: for TP-8, --hicache-size 37.5 would give the intended 300GB total.

A secondary mistake is the assumption that pkill -9 would reliably release all GPU memory. CUDA contexts can persist in the GPU driver even after the owning process is killed, especially if the process was in the middle of a memory allocation at the time of death. The asymmetric cleanup on GPUs 0 and 2 suggests that those ranks were in a different initialization phase — perhaps they had already called cudaMalloc for the KV cache while other ranks were still loading model weights.

The assistant also did not anticipate that the hierarchical cache feature would have per-rank allocation semantics. The parameter name --hicache-size does not suggest per-rank behavior, and the documentation (as seen in the help output of <msg id=3840>) does not clarify this. This is a legitimate documentation gap in SGLang, but the assistant's failure to test with a small allocation first — say, --hicache-size 10 — is an operational oversight.

Conclusion

Message <msg id=3856> is a deceptively simple diagnostic command that captures a pivotal moment in a complex inference server debugging session. The free -g and nvidia-smi outputs tell a story of partial recovery from an OOM disaster, revealing asymmetric GPU state, leaked system memory, and the gap between calculated expectations and operational reality. The assistant's decision to verify cleanup before proceeding reflects sound systems engineering practice, even as the output reveals that the cleanup was incomplete. This message serves as a reminder that in distributed GPU inference, configuration parameters have non-obvious semantics, and the most dangerous assumptions are the ones that seem most natural. The 318GB of leaked RAM and the two locked GPUs are not just data points — they are the fingerprints of a failure mode that will inform every subsequent configuration decision in this session.