The Memory Audit: Diagnosing GPU Utilization in a Production LLM Deployment

Message Overview

The subject message (global index 5726) is a single bash command executed on a remote server running a production SGLang inference server for the Kimi-K2.5 INT4 model. The assistant runs:

ssh root@10.1.230.174 'journalctl -u sglang-kimi -o cat --no-pager | grep -i "max_total_num_tokens\|before_init_pool\|init_memory_pool\|available_gpu_mem\|rest_memory\|total_gpu\|used_memory\|target model.*weight\|draft model.*weight\|Load weight end" | head -20'

The output returns log lines from a previous server startup showing that each GPU (across 8 RTX PRO 6000 Blackwell GPUs) had 72.33 GB consumed by model weights, leaving 21.71 GB available after weight loading. This message appears unremarkable at first glance — just another diagnostic grep. But it sits at a critical juncture in the conversation where the assistant is transitioning from deploying the model to understanding why its GPU memory is underutilized, and it represents the first step in a forensic accounting exercise that will ultimately lead to enabling hierarchical KV cache offloading.

Context and Motivation

To understand why this message was written, we must trace the conversation that led to it. The user had just deployed the Kimi-K2.5 INT4 model (a massive 547 GB model spread across 8 GPUs) as a production systemd service with EAGLE-3 speculative decoding. After verifying that the service was healthy and that tool calls and reasoning content were properly parsed, the user raised a concern in message 5720: "seems like I'm kinda low on max parallel request context, what options do we have for ram offload of kv cache?"

This question reveals the user's operational concern: the server appears to be limited in how many concurrent requests it can handle, and they suspect the bottleneck is GPU memory for the KV cache. The assistant's response in message 5721 checked nvidia-smi and free -h, revealing a striking picture: each GPU had approximately 92 GB used out of 96 GB total, leaving only ~5 GB free, while the system had 396 GB of available RAM. The GPUs appeared nearly full, but the KV cache allocation from a previous run showed only 159,277 tokens consuming 10.42 GB — a relatively modest allocation for a model of this scale.

In message 5724, the user posted logs showing something even more puzzling: a second startup log showed KV cache allocated with "K size: 0.30 GB, V size: 0.30 GB" — only 0.6 GB total per GPU for KV cache — and the user commented "seems like much more space unused or what?" This observation was astute: if the model weights consumed 72.33 GB out of 96 GB, leaving 21.71 GB, and only ~10-12 GB was being used for KV cache + draft model + overhead, then roughly 7-11 GB per GPU was simply unaccounted for.

The assistant's response in message 5725 acknowledged this: "You're right to notice that. After KV cache allocation there's still 11.18 GB free per GPU — that's a lot of wasted headroom." But the assistant recognized that the numbers from the current running service (message 5725's grep) showed only 7.94 GB available after memory pools, and the KV cache was showing K=0.30 GB, V=0.30 GB — a dramatically smaller allocation than the 10.42 GB from the previous run shown by the user. Something had changed between runs, or the memory accounting was more complex than it appeared.

This brings us to the subject message (5726). The assistant needed to reconcile two different sets of logs: the user's logs from a previous startup (showing 10.42 GB KV cache) and the current service logs (showing only 0.6 GB KV cache). To do this, the assistant needed to trace the full memory allocation pipeline — from weight loading through KV cache initialization through memory pool setup — to understand exactly how much memory was being reserved at each stage and why.

The Grep Strategy: What the Assistant Was Looking For

The grep pattern in the subject message is carefully constructed to extract every relevant log line from the memory initialization sequence. Let's examine each pattern:

Input Knowledge Required

To understand this message, one needs significant context about the deployment:

  1. The hardware: 8× NVIDIA RTX PRO 6000 Blackwell GPUs with 96 GB each, connected via PCIe Gen5 (not NVLink). This PCIe topology is critical because it limits inter-GPU communication bandwidth and influences NCCL tuning parameters.
  2. The model: Kimi-K2.5 INT4, a ~547 GB model using INT4 quantization. The model uses tensor parallelism (TP=8) across all 8 GPUs, meaning each GPU holds 1/8 of the model weights (~72 GB).
  3. The software stack: SGLang v0.5.9 with EAGLE-3 speculative decoding, CUDA 13.0, FlashInfer attention backend with allreduce fusion enabled.
  4. The configuration: --mem-fraction-static 0.88 reserves 88% of remaining GPU memory (after weights) for KV cache. --cuda-graph-max-bs 128 limits batch size for CUDA graph capture. --speculative-eagle-topk 1 uses a single draft token path.
  5. The memory layout: Each GPU has 96 GB total. Model weights consume ~72 GB. The remaining ~24 GB must be shared among: target model KV cache, draft model weights (~0.95 GB), draft model KV cache, CUDA graphs, activation memory, and framework overhead.
  6. The discrepancy: The user observed 10.42 GB KV cache in one run but only 0.6 GB in another, suggesting something changed between deployments or the logging was misleading.

The Thinking Process Visible in the Message

The subject message itself contains no explicit reasoning — it's a tool call. But the choice of grep patterns reveals the assistant's mental model of how SGLang's memory initialization works. The assistant is operating on a specific hypothesis: that the KV cache size is being limited not by the mem_fraction_static budget but by some other constraint, possibly max_total_num_tokens being capped by a different mechanism.

The assistant is working through a systematic diagnostic process:

  1. Observation: The user reports low parallel request capacity (msg 5720).
  2. Initial data collection: nvidia-smi shows GPUs nearly full, but free -h shows abundant system RAM (msg 5721).
  3. Log inspection: The current service logs show only 0.6 GB KV cache per GPU (msg 5725), but the user's logs from a previous run show 10.42 GB (msg 5724).
  4. Hypothesis formation: The discrepancy suggests either (a) the memory budget calculation changed between runs, (b) the KV cache allocation is being limited by something other than available memory, or (c) the logging is showing different things (e.g., per-GPU vs. total).
  5. Data gathering (this message): Extract the complete memory initialization sequence from the logs to trace the full allocation pipeline. The assistant is implicitly assuming that the memory allocation follows a deterministic pipeline: load weights → compute available memory → apply mem_fraction_static → compute max_total_num_tokens → allocate KV cache → allocate memory pools → capture CUDA graphs. By extracting log lines at each stage, the assistant can verify whether each step produces the expected result.

What the Message Actually Revealed

The output of the grep command shows only Load weight end lines — the earliest stage of the pipeline. These lines confirm that each of the 8 tensor-parallel ranks (TP0 through TP7) loaded 72.33 GB of model weights, leaving 21.71 GB available. Critically, the timestamps show this was from a previous run on February 28, not the current March 1 run. This is because the grep matched lines from the entire journal, and the head -20 limit caught only the earliest matching entries.

This is actually a subtle issue with the diagnostic approach: by using head -20, the assistant captured the oldest matching log lines first (since journalctl outputs chronologically), rather than the most recent ones from the current service instance. The current run's memory allocation lines (which would show the smaller 0.6 GB KV cache) were further down in the log and got truncated.

The assistant would need to either reverse the journalctl output (--reverse) or increase the head limit to see the full picture. In the subsequent messages (5727-5728), the assistant does eventually piece together the complete memory accounting, but it requires additional commands to trace through the source code rather than relying solely on logs.

Assumptions and Potential Mistakes

Several assumptions underpin this message:

  1. The grep patterns would capture the full pipeline: The assistant assumed that the selected patterns would cover all relevant memory allocation stages. However, the pipeline may use different log messages than expected, or the critical lines might use slightly different formatting.
  2. Logs are deterministic and complete: The assistant assumed that the journal would contain all the relevant log lines from the initialization sequence. But if log levels differ between runs, or if some stages don't produce log output, the picture would be incomplete.
  3. The head -20 limit is sufficient: This was a practical constraint to avoid overwhelming output, but it inadvertently excluded the more recent log entries that showed the actual current memory state.
  4. Memory accounting is additive: The assistant assumed that the memory budget can be reconstructed by summing individual allocations. In practice, CUDA memory fragmentation, PyTorch caching allocator behavior, and NCCL internal buffers can cause significant divergence from simple arithmetic.
  5. The KV cache size discrepancy is meaningful: The assistant treated the difference between 10.42 GB and 0.6 GB as significant, but it's possible these are different measurements (e.g., total across all GPUs vs. per-GPU, or including vs. excluding the draft model cache).

Output Knowledge Created

This message produced concrete data: confirmation that each GPU had 72.33 GB consumed by weights with 21.71 GB remaining. This established the baseline for the memory budget calculation. The assistant now knows that 88% of 21.71 GB = ~19.1 GB should be available for KV cache, but the actual allocation is far smaller.

This knowledge directly informs the next steps. In message 5727, the assistant traces the full memory math and identifies that the KV cache is using only ~10.42 GB out of a 19.1 GB budget, with the remaining ~8.7 GB apparently unaccounted for. In message 5728, the assistant starts examining the SGLang source code to understand how max_total_num_tokens is computed, looking for the mechanism that caps the allocation below the budget.

The ultimate outcome of this investigation (visible in later messages) is the decision to enable hierarchical KV cache (--enable-hierarchical-cache --hicache-ratio 4.0), which uses the 396 GB of available system RAM as an L2 cache tier. This transforms the effective memory capacity from ~83 GB per GPU (72 GB weights + 11 GB KV cache) to effectively unlimited by offloading cold KV cache entries to CPU memory. The subject message is the first step in the diagnostic chain that leads to this architectural improvement.

Broader Significance

This message exemplifies a pattern common in production ML engineering: the gap between configured resource limits and actual resource utilization. The mem_fraction_static=0.88 parameter was intended to reserve 88% of available memory for KV cache, but the actual allocation was far smaller due to constraints in how SGLang computes max_total_num_tokens. The assistant's systematic approach — observe discrepancy, formulate hypothesis, gather data, trace source code, implement solution — is a textbook example of performance debugging in complex distributed systems.

The message also highlights the importance of log analysis in understanding system behavior. A single journalctl grep, while seemingly mundane, is often the most direct way to understand what a complex system actually did versus what its configuration intended it to do. In this case, the logs revealed that the memory allocation pipeline was producing results that diverged significantly from the simple "88% of available memory" model, prompting a deeper investigation that ultimately led to a better architecture.