When Hierarchical Cache Goes Wrong: Debugging an OOM Crash Recovery in SGLang

The Message

[assistant] Still not clean — 318GB RAM used (leaked allocations), and GPU 0/2 still held. Let me be more aggressive: [bash] ssh root@10.1.230.174 'fuser /dev/nvidia 2>&1 | head -5; fuser -k -9 /dev/nvidia 2>/dev/null; sleep 3; nvidia-smi --query-gpu=index,memory.used --format=csv,noheader' 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 message, from the 28th segment of a marathon coding session deploying the Kimi-K2.5 model with SGLang, captures a moment of system-level crisis. The assistant has just crashed an 8-GPU server by misconfiguring SGLang's hierarchical cache, and is now locked in a battle with stubborn GPU memory allocations that refuse to die. On its surface, it is a simple status check and a bash command. But beneath that lies a rich story of mistaken assumptions, resource management under pressure, and the peculiar challenges of operating large language model inference systems at scale.

Context: The Road to the Crash

To understand why this message exists, we must trace the reasoning that led to it. The session had been optimizing throughput for a large-scale synthetic data generation pipeline running on the Kimi-K2.5 model, deployed across 8 NVIDIA RTX PRO 6000 Blackwell GPUs. The assistant had identified that the KV cache was the primary bottleneck: with --mem-fraction-static 0.85, the server could hold only ~116,171 tokens in its KV cache, which meant only about 50 concurrent requests could run at once despite a concurrency setting of 150. The rest piled up in the queue. With average response lengths of 4,000+ tokens, the effective throughput was far below what the hardware could deliver.

The user's instruction in [msg 3848] was succinct and decisive: "Use all levers." This directive set the assistant on a path of aggressive optimization. The assistant had already identified two promising approaches in <msg id=3840-3843>: increasing --mem-fraction-static to 0.93 to squeeze more GPU VRAM for KV cache, and enabling SGLang's hierarchical cache (--enable-hierarchical-cache) with a large host RAM allocation to spill KV cache entries to CPU memory when GPU space ran out. The math looked compelling — with 408 GB of available host RAM and MLA's compressed KV format (only ~137 KB per token per GPU), a 300 GB hierarchical cache could add ~294,000 tokens of capacity, bringing the total to ~410,000 tokens and supporting over 100 concurrent requests at 4K average length.

The Critical Mistake

The assumption that broke everything was subtle. In [msg 3850], the assistant launched the server with --hicache-size 300, believing this would allocate 300 GB of host RAM total for the hierarchical cache. The launch command was:

python3 -m sglang.launch_server \
  --model-path /shared/kimi-k2.5-int4 \
  --trust-remote-code \
  --tp-size 8 \
  --mem-fraction-static 0.93 \
  --host 0.0.0.0 --port 8000 \
  --num-continuous-decode-steps 4 \
  --disable-custom-all-reduce \
  --enable-hierarchical-cache \
  --hicache-size 300 \
  --hicache-write-policy write_through \
  --hicache-io-backend kernel

The mistake became apparent only after the server failed to start. In [msg 3854], the assistant checked system state and found 439 GB of RAM used out of 449 GB total, with only 9 GB free. The realization hit: --hicache-size 300 meant 300 GB per rank, not 300 GB total. With tensor parallelism size 8, each of the 8 TP ranks attempted to allocate its own 300 GB chunk of host RAM, demanding a combined 2.4 TB from a system that only had 449 GB. The result was a catastrophic OOM (out-of-memory) condition that left the system in an inconsistent state.

This is a classic configuration pitfall in distributed ML inference. Many SGLang parameters are specified per-rank, but the documentation may not always make this explicit. The assistant's mental model assumed a total-system parameter, but SGLang's implementation treated it as a per-process allocation. The mistake was compounded by the fact that the assistant had done a quick calculation in [msg 3842] assuming 300 GB total across all GPUs, dividing by 8 to get per-GPU capacity. That calculation was correct for a total allocation but irrelevant if each rank independently tried to allocate 300 GB.

The Recovery Attempt

The target message shows the aftermath. The assistant has already attempted cleanup in <msg id=3855-3856>: killing Python processes with pkill -9, waiting, and using fuser -k to release NVIDIA device handles. But the check in [msg 3856] revealed the system was still dirty — 318 GB of RAM remained allocated (leaked by the crashed processes), and GPUs 0 and 2 still showed 96,413 MiB and 96,417 MiB of memory in use respectively.

The target message is the assistant's escalated response. The reasoning is clear from the opening line: "Still not clean — 318GB RAM used (leaked allocations), and GPU 0/2 still held. Let me be more aggressive." The assistant correctly diagnoses that standard process termination hasn't fully released resources. GPU memory is held by zombie processes or orphaned CUDA contexts that survived the pkill. The RAM leak of 318 GB suggests the OOM'd processes partially allocated host memory before crashing, and the kernel hasn't fully reclaimed it.

The command chosen — fuser -k -9 /dev/nvidia* — is a blunt instrument. fuser identifies processes using specific files or sockets, and -k -9 sends SIGKILL to those processes. By targeting /dev/nvidia*, it aims to kill any process holding NVIDIA device files open, including orphaned CUDA contexts, monitoring tools, or leftover Python processes that pkill might have missed. The 2>&1 | head -5 captures any error messages from processes that refuse to die. The sleep 3 gives the kernel time to clean up, and the final nvidia-smi query verifies the result.

But the result shows no improvement: GPUs 0 and 2 still report 96,413 MiB and 96,417 MiB used. The fuser command either found no processes to kill (because the CUDA contexts were already detached from their parent processes) or the GPU memory is held by kernel-level state that fuser cannot release. This is a known frustration in GPU computing: when a CUDA application crashes without properly destroying its contexts, the GPU memory can remain allocated until the GPU is reset or the driver is reloaded. No amount of process killing will free it — the memory is orphaned at the driver level.

Input Knowledge Required

To fully understand this message, one needs knowledge of several domains. First, the SGLang inference server and its configuration parameters: --tp-size for tensor parallelism, --mem-fraction-static for GPU memory allocation, and --enable-hierarchical-cache with --hicache-size for host RAM KV cache spillover. Second, the concept of KV cache in transformer inference — how it stores key-value pairs from previous tokens to avoid recomputation, and how its size limits concurrent request handling. Third, Linux process management and GPU resource cleanup: pkill, fuser, nvidia-smi, and the behavior of CUDA contexts when parent processes are killed. Fourth, the specific hardware configuration: 8 NVIDIA RTX PRO 6000 GPUs with 96 GB VRAM each, and 449 GB of host RAM.

Output Knowledge Created

This message produces several important pieces of knowledge. It confirms that fuser -k -9 /dev/nvidia* is insufficient to release GPU memory held by crashed CUDA processes — the memory remained allocated even after killing all processes holding NVIDIA device files. It documents that GPU memory can become orphaned at the driver level, requiring a GPU reset or driver reload to recover. It also establishes that the hierarchical cache misconfiguration (per-rank vs. total allocation) leads to an unrecoverable system state that simple process termination cannot fix. For the ongoing session, this message sets the stage for the next recovery attempt — the assistant will need to either reset the GPUs via nvidia-smi --gpu-reset or reboot the machine entirely.

The Thinking Process

The assistant's reasoning in this message reveals a methodical approach to failure recovery. The first step is assessment: checking the current state with free -g and nvidia-smi to quantify the damage. The second step is diagnosis: identifying that 318 GB of leaked RAM and two stuck GPUs mean standard cleanup wasn't sufficient. The third step is escalation: trying a more aggressive tool (fuser -k -9) that targets the resource directly rather than process names. The fourth step is verification: re-checking with nvidia-smi to see if the intervention worked.

The message also shows the assistant's mental model of the system. The phrase "GPU 0/2 still held" indicates the assistant understands that GPU memory is a resource that processes "hold" and must explicitly release. The expectation that fuser -k -9 /dev/nvidia* would work suggests the assistant believes all GPU resource holders are processes with open file handles to NVIDIA devices — an assumption that turns out to be incomplete when CUDA contexts become orphaned.

The tone is pragmatic and slightly frustrated ("Let me be more aggressive"), but the approach remains systematic. The assistant doesn't panic or try random commands — it escalates in a logical progression from pkill to fuser to (implicitly, in the next step) GPU reset. This is the hallmark of experienced systems debugging: trying the least destructive intervention first, then escalating only when it fails.

Broader Implications

This message, while seemingly minor, illustrates several important principles for large-scale ML inference operations. First, configuration parameters in distributed systems often have subtle semantics — "size" might mean per-rank, per-node, or total, and getting this wrong can crash the entire system. Second, GPU memory management is fragile: a crashed process can leave GPU memory in an unrecoverable state that requires hardware-level intervention. Third, when pushing systems to their limits ("use all levers"), the margin between success and catastrophic failure is thin — the assistant went from a promising throughput optimization to a completely hung server in a single misconfigured parameter.

The hierarchical cache feature itself is a powerful optimization, and the assistant's reasoning about its potential was sound. The mistake was in the magnitude, not the concept. A correctly configured --hicache-size of, say, 37.5 GB (300 GB / 8 ranks) might have worked perfectly, giving the system the additional KV cache capacity it needed without exhausting host memory. The lesson is not that hierarchical cache is dangerous, but that understanding the unit semantics of distributed system parameters is critical before deployment.

In the end, this message captures a moment of tension between ambition and reality in ML systems engineering — the moment when a well-reasoned optimization plan meets the unforgiving constraints of real hardware, and the engineer must pivot from optimization to recovery.