The 11.18 GB Mystery: Diagnosing GPU Memory Accounting in a Production SGLang Deployment

In the middle of a complex production deployment of the Kimi-K2.5 INT4 model across 8 NVIDIA RTX PRO 6000 Blackwell GPUs, a seemingly simple observation from the user triggered a deep diagnostic investigation that would ultimately reshape the server's memory architecture. The user had noticed something odd in the server logs: after the KV cache was allocated, there appeared to be a large amount of unused GPU memory. The assistant's response at <msg id=5725> — the subject of this article — represents a pivotal moment where surface-level curiosity meets systematic forensic analysis.

The Context: A Production System Under Tension

To understand this message, we must first appreciate the state of the system. The assistant had just completed an arduous journey through speculative decoding optimization, CUDA toolkit upgrades, NCCL tuning, and systemd service creation. The Kimi-K2.5 INT4 model — a 547 GB behemoth spread across 8 GPUs — was now running as a hardened systemd service (sglang-kimi.service) with EAGLE-3 speculative decoding, topk=1 configuration, and spec_v2 overlap scheduling. The user had already asked about low parallel request context capacity, and the assistant had begun investigating KV cache offloading options via a subagent task ([msg 5723]).

Then came the user's message at <msg id=5724>: a direct quote from the server logs showing KV cache allocation details, ending with the pointed question "seems like much more space unused or what?" The logs showed that after allocating KV cache for 159,277 tokens (10.42 GB total across all layers), each GPU still reported 11.18 GB of available memory. A later log entry showed an even more puzzling picture: the KV cache was allocated with K size of only 0.30 GB and V size of 0.30 GB — a tiny fraction of what was expected. The user's intuition was correct: something in the memory accounting didn't add up.

The Message: Acknowledgment and Investigation

The assistant's response at <msg id=5725> begins with immediate validation of the user's observation:

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. The --mem-fraction-static 0.88 controls how much GPU memory is allocated for the KV cache, but let me check what's actually happening with the memory math.

This opening does several things simultaneously. First, it validates the user's perception — an important collaborative gesture that keeps the investigation moving forward rather than dismissing the observation. Second, it introduces a hypothesis: the --mem-fraction-static 0.88 parameter is the control knob for KV cache allocation, and perhaps the discrepancy lies in how that parameter interacts with the actual memory available. Third, it signals the intent to dig deeper by examining the full sequence of memory events in the journal.

The assistant then executes a bash command that greps the systemd journal for a carefully curated set of keywords: "avail mem\|mem_fraction\|Memory pool\|KV Cache is\|total_gpu_memory\|model_size\|weight end\|Capture.*cuda graph\|cuda graph end". This is not a random search — each keyword targets a specific phase of the server's initialization sequence. "avail mem" tracks remaining free memory at each step. "mem_fraction" would reveal the configured fraction. "Memory pool" marks the final allocation phase. "KV Cache is" captures the KV cache size. "total_gpu_memory" and "model_size" establish the baseline. "weight end" shows when model weights finished loading. And the CUDA graph entries track the final memory-consuming step before the server becomes ready.

The command pipes through tail -30 to get the most recent 30 matching lines from the most recent server start. The output reveals a crucial detail: the KV cache is allocated with K size of 0.30 GB and V size of 0.30 GB — only 0.60 GB total per GPU for the draft model's KV cache, not the target model's. The target model's KV cache (10.42 GB) was allocated in an earlier startup, and the current logs show the draft model's allocation during the EAGLE-3 speculative decoding initialization. This distinction — between target model KV cache and draft model KV cache — is essential to understanding the full memory picture.

The Reasoning Process: What the Assistant Is Thinking

The message reveals a multi-layered reasoning process. At the surface level, the assistant is gathering data to answer a concrete question: why is there 11.18 GB free per GPU after KV cache allocation? But beneath that, several deeper cognitive threads are visible.

First, there's the recognition that --mem-fraction-static 0.88 is the primary lever controlling KV cache size, but the actual allocated size (10.42 GB) seems smaller than the 19.1 GB that 88% of the 21.71 GB available memory would suggest. This discrepancy points to a more complex formula at work — one that the assistant will later trace through the SGLang source code in subsequent messages ([msg 5727] through [msg 5736]).

Second, the assistant is implicitly building a mental model of the full memory pipeline: total GPU memory → model weights → available memory → KV cache allocation → draft model weights → draft KV cache → CUDA graph capture → final headroom. Each phase consumes memory, and the final headroom of ~7.4 GB per GPU (after CUDA graphs) represents the safety margin the system maintains.

Third, the assistant is operating under the assumption that the memory accounting in SGLang is correct but potentially suboptimal. The phrase "wasted headroom" carries a normative judgment — the assistant believes this memory could be put to productive use for KV cache, increasing the server's capacity for concurrent requests. This assumption drives the subsequent investigation into whether mem_fraction_static can be increased or whether hierarchical cache should be enabled.

Input Knowledge Required

To fully understand this message, several pieces of input knowledge are necessary:

  1. The system architecture: 8 NVIDIA RTX PRO 6000 Blackwell GPUs with 96 GB each, connected via PCIe Gen5, running CUDA 13.0 with FlashInfer attention backend.
  2. The model configuration: Kimi-K2.5 INT4 quantized model with EAGLE-3 speculative decoding, requiring both a target model (547 GB across 8 GPUs, ~72 GB per GPU) and a draft model (~0.95 GB per GPU).
  3. SGLang's memory management model: The --mem-fraction-static parameter controls what fraction of remaining GPU memory (after weights) is reserved for KV cache, but the actual formula involves subtracting a reserve from total GPU memory: rest_memory = available_gpu_memory - total_gpu_memory * (1 - mem_fraction_static).
  4. The server's initialization sequence: Model weights load first, then KV cache is allocated, then draft model weights load, then draft KV cache is allocated, then CUDA graphs are captured. Each phase reports available memory.
  5. The distinction between target and draft model KV caches: The log lines showing 0.30 GB K and V sizes are for the draft model (which has far fewer layers), not the target model's 10.42 GB KV cache.

Output Knowledge Created

This message creates several valuable outputs:

  1. A diagnostic trace: The grep command output provides a timestamped sequence of memory states throughout server initialization, enabling precise accounting of where memory goes at each phase.
  2. A confirmed discrepancy: The data validates the user's observation — there is indeed significant free memory (eventually ~7.4 GB per GPU) that appears underutilized.
  3. A framing for further investigation: The message establishes that the mem_fraction_static formula is the key to understanding the discrepancy, setting the stage for the deep source code analysis that follows in subsequent messages.
  4. A collaborative diagnostic pattern: The assistant models a systematic approach to performance investigation — validate the observation, form a hypothesis, gather data, trace the root cause.

Assumptions and Potential Mistakes

The assistant makes several assumptions in this message that warrant examination.

The primary assumption is that the free memory represents "wasted headroom" that could be reclaimed for KV cache. This assumes that the safety margin built into mem_fraction_static is larger than necessary. In reality, that 12% reserve (11.47 GB per GPU) serves critical functions: it accommodates activation memory spikes during inference, provides space for CUDA graph capture (which temporarily requires additional memory), and guards against OOM errors under peak load. The assistant's later analysis ([msg 5736]) acknowledges this risk, noting that pushing to 0.92 "could OOM under load."

A secondary assumption is that the KV cache size is primarily constrained by the mem_fraction_static formula rather than by other limits like max_running_requests or max_total_tokens. The assistant subsequently checks this by examining the SGLang source code for max_total_num_tokens computation ([msg 5727]–[msg 5730]), finding that the profile function does indeed compute the token budget from memory.

The assistant also assumes that the user's primary concern is about active KV cache capacity (concurrent tokens) rather than prefix cache efficiency. This assumption is reasonable given the user's phrasing ("low on max parallel request context"), but the distinction becomes important when the assistant later recommends hierarchical cache, which primarily helps with prefix reuse rather than active capacity.

The Broader Significance

This message represents a classic moment in systems engineering: the transition from "it works" to "it works optimally." The Kimi-K2.5 deployment was already functional — the systemd service was running, the model was responding, tool calls and reasoning were parsed correctly. But the user's observation about unused memory opened a new dimension of optimization. The assistant's response at <msg id=5725> is the first step down this path, one that would ultimately lead to enabling hierarchical KV cache with a 4:1 CPU-to-GPU ratio, dramatically expanding the effective KV cache capacity using the system's 396 GB of free RAM.

The message also illustrates a crucial principle in AI-assisted system administration: the assistant's willingness to treat user observations as valid starting points for investigation, even when the user's framing is incomplete. Rather than explaining away the 11.18 GB as expected behavior, the assistant validates the observation and commits to understanding it. This collaborative curiosity is what transforms a routine deployment into an optimized one.

In the messages that follow, the assistant will trace the exact memory formula in the SGLang source code, compute the precise allocation at each phase, and present four concrete options for increasing KV cache capacity. But all of that analysis traces back to this single moment — a grep command launched in response to a user's perceptive question about a log line that didn't quite add up.