The Moment a 300GB Hierarchical Cache Became 2.4TB: Diagnosing an Oversubscribed Server Launch

Message Overview

The subject message ([msg 3853]) is a single bash command and its truncated output, issued by the AI assistant during a high-stakes optimization session for an SGLang inference server serving the Kimi-K2.5 model across 8 NVIDIA RTX PRO 6000 Blackwell GPUs. The message reads:

[assistant] [bash] ssh root@10.1.230.174 'tail -30 /data/eagle3/synth_100k/logs/sglang_inference.log' [2026-02-24 11:13:23 TP3] Capture cuda graph end. Time elapsed: 13.81 s. mem usage=3.75 GB. avail mem=0.82 GB. [2026-02-24 11:13:23 TP1] Capture cuda graph end. Time elapsed: 13.81 s. mem usage=3.75 GB. avail mem=0.82 GB. [2026-02-24 11:13:23 TP6] Capture cuda graph end. Time elapsed: 13.84 s. mem usage=3.75 GB. avail mem=0.82 GB. [2026-02-24 11:13:23 TP4] Capture cuda graph end. Time elapsed: 13.85 s. mem usage=3.75 GB. avail mem=0.82 GB. [2026-02-24 11:13:23 TP0] Capture cuda graph end. Time e...

At first glance, this looks like a routine status check — the assistant is peeking at the server log to see whether the newly launched SGLang instance has finished initializing. But the output tells a deeper story. The CUDA graph capture completed successfully across all tensor-parallel ranks (TP0 through TP7, though only five are visible in the truncated output), each taking roughly 13.8 seconds and consuming 3.75 GB of GPU memory. The alarming detail is the avail mem=0.82 GB — only 820 megabytes remain free on each GPU after initialization. This is the canary in the coal mine, signaling that the aggressive memory configuration chosen in the previous round may have oversubscribed the GPU.

Context and Motivation: Why This Message Was Written

To understand why the assistant issued this particular command, we must trace the chain of reasoning that led to it. The session had been wrestling with a throughput bottleneck for the inference pipeline generating training data for an EAGLE-3 speculative decoding drafter. The server was achieving only ~850 tok/s gen throughput with roughly 50 concurrent requests, while 100+ requests sat queued. The root cause was KV cache saturation: at --mem-fraction-static 0.85, the server could hold only ~116,000 total tokens, and with each request averaging 4,000+ tokens (for the B2_opencodeinstruct dataset), only about 28 requests could fit simultaneously.

The user had given a clear directive in [msg 3848]: "Use all levers." The assistant responded by launching the server with the most aggressive configuration it could devise: --mem-fraction-static 0.93 (up from 0.85), --enable-hierarchical-cache with --hicache-size 300 (300 GB of host RAM for KV cache overflow), and --hicache-io-backend kernel for fast CPU-GPU transfers. The server was launched in [msg 3850], and [msg 3851] showed it loading checkpoint shards. Then in [msg 3852], the assistant ran a health-check loop that timed out after 600 seconds — the server never became healthy.

The subject message is the assistant's first diagnostic step after that timeout. It is asking: "What went wrong? Did the server crash during initialization, or is it still loading?" The tail -30 command reads the last 30 lines of the server log, which should contain the most recent initialization events. The output reveals that the server did finish loading — CUDA graphs were captured, which is one of the final steps before accepting requests — but the available memory per GPU was critically low at 0.82 GB.

The Thinking Process: What the Assistant Was Reasoning

The assistant's reasoning at this point, though not explicitly shown in the message itself, can be reconstructed from the surrounding context. The assistant had just waited 600 seconds (10 minutes) for a server that never responded to health checks. Its first hypothesis would be that the server crashed during initialization — perhaps an OOM (out-of-memory) error during model loading, or a hang during CUDA graph capture. The tail -30 command is the fastest way to check: if the last log lines show an error traceback, the server crashed; if they show normal initialization messages, the server might be alive but not responding for another reason.

The output shows normal completion of CUDA graph capture — no crash. This narrows the diagnosis. The server initialized successfully but is not responding to health checks. The low available memory (0.82 GB) is a strong clue: the server may be alive but unable to allocate memory for new requests, or the hierarchical cache initialization may have caused the process to hang after the log messages were written.

Assumptions Made

The assistant made several assumptions in this message and the decisions leading up to it:

  1. That --hicache-size 300 is a total system allocation, not per-rank. This was the critical mistake. In [msg 3854], immediately after this message, the assistant discovers that each TP rank tried to allocate 300 GB of host RAM, totaling 2.4 TB — far exceeding the 449 GB available. The --hicache-size parameter in SGLang is specified per GPU rank, not globally. The assistant assumed it was a shared pool.
  2. That the server would respond to health checks after CUDA graph capture. The server completed initialization but never became healthy. The assistant assumed that successful CUDA graph capture meant the server was ready, but the hierarchical cache allocation may have caused the process to hang or deadlock afterward.
  3. That mem_fraction_static=0.93 would leave enough headroom. With only 0.82 GB available per GPU after initialization, the server has virtually no room for prefill compute buffers, temporary allocations, or request processing. The assistant assumed that 93% memory utilization would still leave a safe margin, but the actual margin was razor-thin.
  4. That the hierarchical cache would work seamlessly with the kernel I/O backend. The --hicache-io-backend kernel option was chosen for performance, but it may have incompatibilities with the SM120 architecture or the specific CUDA version.

Mistakes and Incorrect Assumptions

The most significant mistake was the misinterpretation of --hicache-size. The assistant's earlier analysis in [msg 3842] calculated that 300 GB of host RAM would provide ~294,000 additional tokens of KV cache capacity. This calculation assumed the 300 GB was shared across all 8 GPUs. In reality, each of the 8 TP ranks attempted to allocate its own 300 GB region, resulting in a 2.4 TB demand on a system with only 449 GB of RAM. The system likely entered severe swapping or the allocation failed silently, causing the server to hang after logging the CUDA graph capture messages.

A secondary mistake was not testing the hierarchical cache configuration incrementally. The assistant jumped directly to the most aggressive settings (0.93 mem fraction + 300 GB hicache + kernel I/O backend) without first verifying that a smaller hicache allocation would work. A more cautious approach would have been to start with --hicache-size 48 (as eventually used in the winning configuration described in the chunk summary) and scale up.

The assumption that mem_fraction_static=0.93 was safe also proved incorrect. The earlier analysis in [msg 3836] calculated that 0.93 would yield ~127,000 tokens (9.4% more than 0.85), but this calculation didn't account for the memory consumed by CUDA graph capture (3.75 GB per GPU) and other runtime overheads. The actual available memory after initialization was only 0.82 GB — essentially zero headroom.

Input Knowledge Required

To fully understand this message, the reader needs knowledge of:

Output Knowledge Created

This message produces several pieces of actionable information:

  1. Confirmation that the server initialized successfully: CUDA graph capture completed on all TP ranks, meaning model loading, weight distribution, and graph compilation succeeded.
  2. Memory pressure warning: With only 0.82 GB available per GPU, the server is operating at the edge of OOM. Any request that requires temporary buffers beyond this margin will cause allocation failures.
  3. Initialization timing: CUDA graph capture took ~13.8 seconds, which is a useful benchmark for future server starts.
  4. The log file is accessible and contains recent entries: This confirms the server process is still running (not crashed), narrowing the diagnosis to a hang or unresponsive state rather than a crash.

The Broader Significance

This message sits at a pivot point in the optimization session. The assistant had tried to solve the KV cache bottleneck by throwing hardware resources at it — more GPU memory fraction and massive host RAM overflow. The failure of this approach forced a more nuanced solution. In the subsequent messages, the assistant would discover the per-rank allocation mistake, kill the hung server, and eventually settle on a winning configuration: mem_fraction_static=0.88 with --enable-hierarchical-cache --hicache-size 48 (48 GB total, or 6 GB per rank), yielding 159K GPU tokens and ~930-1350 tok/s throughput — a 2-3x improvement over the initial baseline.

The message is a textbook example of a diagnostic check in systems debugging: when a command times out, the first step is to check the logs for errors. The assistant's disciplined approach — launching the server, waiting for health, and only then reading logs — follows a systematic debugging methodology. Even though the configuration was wrong, the diagnostic process was correct.