Tracing the Missing KV Cache: A Deep Dive into SGLang's Memory Accounting

[bash] ssh root@10.1.230.174 'grep -rn "max_total_num_tokens\s*=" /root/sglang/python/sglang/srt/ | grep -v ".pyc" | grep -v "test" | head -20'
grep: /root/sglang/python/sglang/srt/managers/__pycache__/scheduler_runtime_checker_mixin.cpython-312.pyc: binary file matches
grep: /root/sglang/python/sglang/srt/managers/__pycache__/scheduler.cpython-312.pyc: binary file matches
/root/sglang/python/sglang/srt/managers/scheduler_runtime_checker_mixin.py:160:        token_msg = f"{self.max_total_num_tokens=}, {available_size=}, {evictable_size=}, {protected_size=}\n"
/root/sglang/python/sglang/srt/managers/scheduler_runtime_checker_mixin.py:211...

The Mystery of the Missing GPU Memory

At first glance, this message appears to be a simple grep command — a developer searching through source code for a variable assignment. But this seemingly mundane command sits at a critical inflection point in a much larger debugging saga. The message represents the moment when the assistant, having exhausted the obvious paths of investigation, broadens its search to understand why a production deployment of the Kimi-K2.5 INT4 model on eight RTX PRO 6000 Blackwell GPUs is leaving nearly 8 GB of GPU memory per device completely unused.

The context is essential. The user had noticed something peculiar in the server logs: the KV cache was allocated at only 159,277 tokens per GPU, consuming just 10.42 GB out of the 19.10 GB that the --mem-fraction-static 0.88 setting should have reserved. After the draft model weights, draft KV cache, and CUDA graph captures were accounted for, a full 7.43 GB remained free on each GPU. This was not a trivial inefficiency — across eight GPUs, that represented nearly 60 GB of wasted HBM capacity that could have been used to support longer context windows or higher concurrency. The user's original concern about being "low on max parallel request context" (see [msg 5720]) had led the assistant down a path of investigating KV cache offloading options, but the logs revealed a more fundamental puzzle: the GPU memory was there, but SGLang wasn't using it.

The Investigation Trail

The assistant's reasoning process in the preceding messages ([msg 5725] through [msg 5730]) reveals a systematic forensic analysis. It began by tracing the memory math: total GPU memory was 96 GB, the target model weights consumed 72.33 GB (leaving 21.71 GB available), and mem_fraction_static 0.88 should have reserved 19.10 GB for the KV cache. But the actual allocation was only 10.42 GB — roughly half the expected amount. The assistant then searched model_runner.py for where max_total_num_tokens was computed, but found only references to the variable, not its assignment. This was the dead end that prompted the current message.

The assistant's decision to use a broad recursive grep (grep -rn) across the entire sglang/srt/ directory, filtering out .pyc files and test directories, was a deliberate methodological choice. Rather than continuing to trace through individual files manually — which would have required deep knowledge of SGLang's internal architecture — the assistant opted for a comprehensive search to locate the exact assignment site. This is a classic debugging technique: when you can't find where a variable is set by reading the code linearly, search for it exhaustively.

Assumptions and Their Implications

The assistant operated under several implicit assumptions. First, it assumed that max_total_num_tokens was the key variable controlling KV cache capacity — that the token count was the independent variable and memory consumption was the dependent variable. This is a reasonable assumption given SGLang's architecture, where the KV cache is allocated as a fixed-size pool of token slots at startup. Second, the assistant assumed that the discrepancy between expected and actual memory usage was caused by a software configuration issue rather than a hardware limitation or a deliberate design choice. Third, it assumed that the assignment of max_total_num_tokens would be found in a straightforward location within the model runner or scheduler code — an assumption that was proving incorrect as the search widened.

There was also a subtle assumption about the grep pattern itself. The assistant searched for max_total_num_tokens\s*= — a regex that would match assignments like max_total_num_tokens = 159277 but would miss Python-style type-annotated assignments like max_total_num_tokens: int = 159277 or augmented assignments like self.max_total_num_tokens = min(...). This is a minor but real risk in code search: the pattern might be too narrow.

Input Knowledge Required

To fully understand this message, one needs several layers of context. The reader must know that the deployment uses eight NVIDIA RTX PRO 6000 Blackwell GPUs with 96 GB HBM each, running CUDA 13.0. They must understand SGLang's memory architecture: the KV cache is a pre-allocated token pool sized by max_total_num_tokens, which is computed during initialization based on available GPU memory after model weights are loaded, multiplied by mem_fraction_static. They must also know that the deployment uses EAGLE-3 speculative decoding, which adds a draft model and its own KV cache on top of the target model. The reader needs to understand that the assistant is working remotely via SSH, reading server logs via journalctl, and searching the SGLang source tree at /root/sglang/ on the remote machine.

Output Knowledge Created

This message produced a small but significant piece of information: it confirmed that max_total_num_tokens is referenced in scheduler_runtime_checker_mixin.py at line 160 and beyond. The truncated output (ending with ...) suggests there are more matches that were cut off by head -20. This output narrowed the search space: the variable is used in the scheduler's runtime checker mixin, which is responsible for monitoring token pool utilization during operation. But critically, the grep did not find the assignment of max_total_num_tokens — the matches found are all references to self.max_total_num_tokens in f-string debug messages, not the initialization code that sets its value.

The Thinking Process

The assistant's thinking process is visible in the chain of messages leading to this one. In [msg 5725], the assistant performed the memory math and noticed the discrepancy. In [msg 5727], it traced through the numbers in detail, computing: 0.88 × 21.71 GB = 19.10 GB reserved, but only 10.42 GB actually used for KV cache. It hypothesized that max_running_requests=48 might be the limiting factor rather than memory. In [msg 5728] and [msg 5729], it searched model_runner.py for the max_total_num_tokens assignment but found only references. Message [msg 5730] confirmed that model_runner.py does not contain the assignment — it only uses the variable after it has been set elsewhere.

This is where the current message becomes pivotal. The assistant is now saying, in effect: "The assignment isn't where I expected it. Let me search the entire SGLang source tree." This broadening of scope is a natural progression in debugging — from targeted file inspection to exhaustive search. The assistant is also implicitly acknowledging that the memory accounting logic might be more distributed across the codebase than initially assumed.

The Broader Significance

This message, for all its apparent simplicity, captures a fundamental tension in deploying large language models: the gap between theoretical memory capacity and actual utilization. The Kimi-K2.5 model, at 547 GB in INT4 across eight GPUs, is pushing the limits of what can fit on the hardware. Every gigabyte of wasted memory translates directly into reduced throughput or shorter context windows. The assistant's investigation — of which this grep is just one step — represents the kind of deep systems debugging that separates a working deployment from an optimized one.

The message also illustrates a key principle of the opencode session architecture: the assistant works in synchronous rounds, dispatching multiple tool calls in parallel and waiting for all results before proceeding. Here, a single bash command was issued, and the assistant would need to wait for the next round to act on the results. This creates a natural rhythm of investigation: gather data, analyze, form hypothesis, test, repeat.

Conclusion

Message [msg 5731] is a debugging breadcrumb — a single step in a longer journey to understand why SGLang was underutilizing GPU memory. It represents the moment when the assistant, having exhausted targeted searches, cast a wider net across the entire codebase. The truncated grep output hints at more matches to be found, and the absence of the assignment site in the results suggests that max_total_num_tokens might be set through a more complex mechanism — perhaps in a parent class, through a configuration object, or via a dynamic computation that doesn't use a simple assignment statement. The investigation would continue, but this message marks a shift from assumption-driven searching to exhaustive exploration, a necessary pivot when the obvious paths lead nowhere.