The Delicate Art of Memory Budgeting: A Status Check in the SGLang KV Cache Tuning Saga

Introduction

In the middle of a marathon session tuning SGLang's throughput for the Kimi-K2.5 model on an 8-GPU system, message [msg 3883] appears as a quiet but pivotal checkpoint. The assistant issues a single bash command — a health check combined with a log grep — to determine whether the server has survived a restart after a catastrophic out-of-memory (OOM) crash. The output reveals that CUDA graph capture is progressing with healthy memory headroom, a small but significant victory in a longer battle to balance GPU memory allocation against throughput.

This message, on its surface, is mundane: a status probe. But it sits at the intersection of several critical threads — crash recovery, memory budgeting, hierarchical cache configuration, and the iterative process of stabilizing a large language model inference server under real production constraints. To understand why this message was written and what it reveals, we must examine the chain of events that led to it, the assumptions baked into the configuration choices, and the knowledge it produces for the next steps.

Context: The Crash and the Recovery

The immediate predecessor to this message is a server crash. In [msg 3877], the user tersely reports "sglang crashed," and the assistant confirms the cause in <msg id=3878-3879>: an OOM triggered during a prefill batch. The root cause was --mem-fraction-static 0.93, which left only 0.82 GB of free GPU memory per rank after CUDA graph capture — insufficient for the transient allocations required during batch prefill.

This was not the first OOM in this session. Earlier, in [msg 3854], the assistant had discovered that --hicache-size 300 was interpreted per-rank, causing 8 ranks to collectively attempt allocating 2.4 TB of host memory on a system with only 449 GB. That misconfiguration had to be killed and cleaned up via the Proxmox host (see [msg 3860]). The assistant then calculated a safe per-rank hicache size of 48 GB, based on 445 GB available host memory minus ~40 GB for model loading overhead and a safety margin, divided by 8 ranks.

The restart in [msg 3881] used --mem-fraction-static 0.88 combined with --enable-hierarchical-cache --hicache-size 48. This was a deliberate retreat from the aggressive 0.93 that caused the crash, while still aiming for better throughput than the earlier baseline of 0.85. The assistant's reasoning, visible in [msg 3879], was that 0.88 would leave approximately 5 GB of headroom per GPU for transient allocations — a guess based on the observation that CUDA graph capture consumed about 3.75 GB and left 0.82 GB free at 0.93.

The Message Itself: What It Checks and Why

The command in [msg 3883] is:

ssh root@10.1.230.174 'curl -s http://localhost:8000/health; echo; grep -E "max_total_num|Allocating|READY|Error|error|OOM|avail mem" /data/eagle3/synth_100k/logs/sglang_inference.log | tail -20'

This does two things simultaneously. First, it probes the HTTP health endpoint at localhost:8000/health — the standard SGLang readiness indicator. Second, it greps the server log for a carefully chosen set of keywords that reveal the server's state:

max_total_num: The total number of KV cache tokens allocated, which directly reflects the effective GPU memory utilization. This is the single most important metric for understanding whether the mem_fraction_static adjustment produced the desired capacity increase.
Allocating: Captures memory allocation messages, particularly the hierarchical cache host memory allocation.
READY: The server's own readiness signal.
Error/error/OOM: Any error conditions that would indicate the new configuration is also failing.
avail mem: The available GPU memory after each major allocation step, critical for understanding headroom. The choice of these grep terms reveals the assistant's mental model of what could go wrong. After the crash at 0.93, the primary concern is whether 0.88 is stable. The secondary concern is whether the hierarchical cache allocated correctly — the earlier misconfiguration with --hicache-size 300 had demonstrated that this parameter is easy to get wrong.

The Output: A Partial Picture

The output shows CUDA graph capture in progress across the 8 TP ranks:

[2026-02-24 11:55:44 TP7] Capture cuda graph begin. This can take up to several minutes. avail mem=9.26 GB
[2026-02-24 11:55:44 TP1] Capture cuda graph begin. This can take up to several minutes. avail mem=9.21 GB
[2026-02-24 11:55:44 TP4] Capture cuda graph begin. This can take up to several minutes. avail mem=9.21 GB
[2026-02-24 11:55:58 TP7] Capture cuda graph end. Time elapsed: 13.98 s. mem usage=3.75 GB. avail mem=5.51 GB.
[2026-02-24 11:55:58 TP5] Capture cuda graph end. Time elapsed: 14....

The critical numbers here are the avail mem values. Before CUDA graph capture, each rank has approximately 9.2 GB of available GPU memory. After capture, the available memory drops to about 5.5 GB — a consumption of roughly 3.75 GB for the CUDA graphs themselves. This is dramatically better than the 0.82 GB that remained at mem_fraction_static=0.93. The 5.5 GB of headroom provides a comfortable buffer for prefill allocations, suggesting the 0.88 configuration is stable.

The output is truncated — the health check response is not visible in the message, and the grep results for max_total_num and other keywords are cut off. This truncation is itself informative: the assistant likely received the full output but the conversation display only captured the beginning. The presence of CUDA graph capture messages at the top of the tail output indicates that the server is still in its startup phase, not yet ready to serve requests.

Assumptions Embedded in This Message

Several assumptions underlie the assistant's approach in this message:

Assumption 1: The OOM was caused by insufficient headroom, not by a fundamental incompatibility. The assistant assumes that reducing mem_fraction_static from 0.93 to 0.88 will resolve the crash without introducing new problems. This is a reasonable engineering judgment, but it assumes the OOM was purely a memory budget issue rather than, say, a memory leak or a fragmentation problem that would recur regardless of the budget.

Assumption 2: The hierarchical cache configuration is correct. The assistant assumes that --hicache-size 48 (per rank) and the associated write_through policy and kernel IO backend will function correctly with the new memory fraction. This assumption is grounded in the earlier code inspection (see <msg id=3862-3866>) where the assistant read the SGLang source to confirm that hicache size is per-rank and that the allocation logic checks psutil.virtual_memory().available.

Assumption 3: The health endpoint is the correct readiness indicator. The assistant uses curl -s http://localhost:8000/health to check readiness, but the output shows CUDA graph capture still in progress. The health endpoint may return 200 OK before the server is fully ready to handle inference requests, as suggested by the earlier experience in [msg 3871] where the server was responding to health checks but still in early prefill stages.

Assumption 4: The grep pattern will capture all relevant state. The keyword list is carefully chosen but not exhaustive. For instance, it does not include CUDA out of memory (the exact PyTorch error message) or RuntimeError (the exception type seen in the crash traceback in [msg 3878]). If the server crashed with a different error signature, the grep might miss it.

Mistakes and Incorrect Assumptions

The most significant mistake visible in the surrounding context is the initial --hicache-size 300 configuration. The assistant assumed this was a total system value, but the SGLang code interprets it per-rank. This led to an attempt to allocate 2.4 TB of host memory on a 449 GB system, causing a system-wide OOM that required intervention from the Proxmox host to reset the GPUs (see [msg 3860]). The assistant corrected this by reading the source code, but the initial assumption cost significant time and required a hard reset.

A subtler issue is the assumption that mem_fraction_static=0.93 would work because the earlier calculation in [msg 3866] estimated the device pool at ~127K tokens. The assistant did not account for the CUDA graph capture memory consumption (3.75 GB) in the initial budget, leading to the crash. The correction to 0.88 incorporated this lesson, but the initial oversight reveals a gap in the mental model: the assistant was thinking about steady-state memory usage without fully accounting for initialization overhead.

The truncated output in this message also reflects a minor operational mistake: the assistant did not capture the health check response or the max_total_num value in the visible output. This means the message does not definitively answer whether the server is ready — it only shows that CUDA graph capture is progressing. The assistant would need to issue a follow-up command to confirm readiness, which it does in subsequent messages.

Input Knowledge Required

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

SGLang server architecture: Understanding that SGLang uses tensor parallelism (TP) across multiple GPUs, that each TP rank has its own memory pool, and that the server goes through distinct startup phases (model loading, memory pool allocation, CUDA graph capture, then serving).
KV cache mechanics: Knowledge that the KV cache is the dominant memory consumer in LLM inference, that mem_fraction_static controls the fraction of GPU memory allocated to it, and that the hierarchical cache extends this to host RAM for overflow.
CUDA graph capture: Understanding that SGLang uses CUDA graphs to accelerate inference by capturing and replaying GPU operations, and that this process requires temporary memory allocation during capture that is freed afterward.
The Kimi-K2.5 model characteristics: The model uses Multi-head Latent Attention (MLA) with a 7168-dim hidden state and has 64 checkpoint shards, requiring significant memory for both weights and KV cache.
The system hardware: 8× RTX PRO 6000 Blackwell GPUs (each with 96 GB VRAM) and 449 GB host RAM, running in a Proxmox container environment.
The earlier crash history: The failed attempt with mem_fraction_static=0.93 and the hicache size misconfiguration, which inform the current configuration choices.

Output Knowledge Created

This message produces several pieces of actionable knowledge:

Confirmation that CUDA graph capture is proceeding normally: The capture completed in ~14 seconds with 3.75 GB memory consumption, consistent with healthy operation.
Memory headroom validation: The 5.5 GB of available memory after capture (down from 9.2 GB) confirms that mem_fraction_static=0.88 provides adequate headroom for transient allocations, unlike the 0.82 GB that remained at 0.93.
Partial readiness indication: The presence of CUDA graph capture messages rather than error messages suggests the server has not crashed, but it is not yet ready to serve. The health check response (not visible in the truncated output) would provide the definitive readiness signal.
A baseline for further tuning: The assistant now knows that 0.88 is stable. The next question — answered in subsequent messages — is whether this configuration yields the desired throughput improvement over the earlier baseline.

The Thinking Process

The assistant's reasoning in this message reflects a systematic debugging methodology. After the crash, the assistant did not simply reduce mem_fraction_static arbitrarily. It analyzed the crash traceback (see [msg 3878]), identified the OOM in the deepseek_v2.py forward pass, calculated the headroom needed based on the observed CUDA graph memory consumption, and chose 0.88 as a conservative retreat that still improves on the 0.85 baseline.

The choice of grep keywords is itself a window into the assistant's diagnostic priorities. By including both READY and OOM in the same pattern, the assistant is simultaneously checking for success and failure — a binary classification that efficiently answers the most pressing question: "Is the server alive or dead?" The inclusion of max_total_num and Allocating adds a quantitative dimension, allowing the assistant to assess not just survival but performance.

The fact that the assistant issues this check via SSH to the remote server, rather than running it locally, reflects the distributed nature of the setup. The assistant is operating from a different machine, managing the inference server remotely. This adds latency to each check but is necessary for the multi-machine architecture.

Conclusion

Message [msg 3883] is a brief but crucial status check in a complex server optimization process. It captures the moment when the assistant verifies that a crash-recovery configuration is working, before proceeding to the next phase of throughput benchmarking and data generation. The output — showing CUDA graph capture completing with 5.5 GB of headroom — provides the confidence needed to move forward.

This message exemplifies the iterative, empirical nature of ML infrastructure tuning. Each configuration change is a hypothesis tested against reality, and each crash is a learning opportunity that refines the mental model of how memory is consumed across the complex stack of model weights, KV cache, CUDA graphs, and transient allocations. The assistant's methodical approach — diagnose, adjust, verify, iterate — is the essence of production engineering for large-scale inference systems.