Reading the Source: How a Single Bash Command Uncovered SGLang's KV Cache Allocation Formula

In the middle of a high-stakes production deployment of the Kimi-K2.5 INT4 model across eight NVIDIA RTX PRO 6000 Blackwell GPUs, a seemingly simple user observation triggered a deep forensic dive into SGLang's memory management. The user had noticed something peculiar in the server logs: after KV cache allocation, each GPU still reported 11.18 GB of free memory, prompting the question: "seems like much more space unused or what?" ([msg 5724]). What followed was a meticulous investigation that culminated in message [msg 5735]—a single bash command that read the source code of SGLang's profile_max_num_token function, revealing the exact formula governing KV cache capacity.

The Context: A Production System Under Scrutiny

The conversation leading up to this moment was anything but casual. The assistant had just spent dozens of messages hardening the Kimi-K2.5 INT4 deployment: creating a systemd service, configuring EAGLE-3 speculative decoding with spec_v2 overlap, fixing tool call and reasoning parsers, and enabling hierarchical KV cache offloading. The system was running with --mem-fraction-static 0.88, a parameter that tells SGLang what fraction of available GPU memory (after model weights) should be reserved for the KV cache. At 0.88, one would expect roughly 88% of the 21.71 GB free per GPU to go toward KV cache—about 19.1 GB. Yet the logs showed only 10.42 GB allocated for 159,277 tokens.

This discrepancy was not just an academic curiosity. In a production system serving a 547 GB model across 8 GPUs, every gigabyte of underutilized memory translates directly to reduced context capacity, lower batch sizes, and degraded throughput. Understanding why the KV cache was only half the expected size was essential before making any configuration changes.

The Investigation: Tracing the Memory Math

The assistant's response in [msg 5725] acknowledged the user's observation and began tracing the memory math. It checked journalctl logs for KV cache allocation details, then in [msg 5727] performed a careful line-by-line accounting:

The Subject Message: Reading the Source

Message [msg 5735] is the culmination of this search. It contains a single bash command executed on the remote server:

ssh root@10.1.230.174 'sed -n "116,200p" /root/sglang/python/sglang/srt/model_executor/model_runner_kv_cache_mixin.py'

This command reads lines 116 through 200 of the model_runner_kv_cache_mixin.py file, which contains the profile_max_num_token method. The output shown in the message begins with the function signature:

def profile_max_num_token(self: ModelRunner, total_gpu_memory: int):
    available_gpu_memory = get_available_gpu_memory(
        self.device,
        self.gpu_id,
        distributed=get_world_group().world_size > 1,
        cpu_group=get_world_group().cpu_group,
    )
    # Get the number of layers used for KV cache calculation
    if self.is_draft_worker:
        num_layers = getattr(
            self.model_config.hf_config,
            "num_ne...

The output is truncated at this point—the bash command captured only the first 85 lines of the function. But the critical insight was already visible: the function first calls get_available_gpu_memory to determine how much memory is free after model weights are loaded, then uses total_gpu_memory (the full GPU memory capacity) in a formula that the assistant would decode in the very next message.

The Discovery: The Hidden Formula

In the immediate follow-up message ([msg 5736]), the assistant synthesized what it had read:

rest_memory = available_gpu_memory - total_gpu_memory * (1 - mem_fraction_static)

This was the key insight. The formula does not simply take 88% of available memory. Instead, it reserves (1 - mem_fraction_static) of the total GPU memory as headroom, then subtracts that from the available memory. With mem_fraction_static = 0.88:

Why This Matters: Assumptions and Implications

The assistant's investigation revealed several important things:

The assumption that mem_fraction_static was the sole governor of KV cache size was wrong. The parameter is more nuanced: it controls the fraction of total GPU memory reserved as a static buffer, and the KV cache gets whatever remains of the available memory after that buffer is subtracted. This design choice prioritizes stability—the 12% reserve ensures there's always headroom for CUDA graphs, activation memory, and draft model overhead, preventing out-of-memory crashes under load.

The user's observation of "wasted" memory was partially incorrect. While 7.43 GB per GPU appears unused at startup, much of it is consumed during operation by CUDA graph captures and activation tensors. The reserve is a safety margin, not pure waste. However, the assistant's analysis did confirm that some headroom could be reclaimed—options included pushing mem_fraction_static to 0.92 (risky), enabling hierarchical KV cache offload to CPU RAM, or switching to FP8 KV cache dtype.

The investigation methodology itself is noteworthy. Rather than guessing or consulting documentation, the assistant went directly to the source code, tracing the assignment chain through multiple files until it found the exact formula. This approach is characteristic of deep systems debugging: when observed behavior doesn't match mental models, verify against the actual implementation.

Input and Output Knowledge

To understand this message, one needs: familiarity with SGLang's server architecture (tensor parallelism, KV cache management, draft workers), knowledge of GPU memory accounting (model weights vs. KV cache vs. activation memory), understanding of the mem_fraction_static parameter's intended purpose, and basic proficiency with grep, sed, and journalctl for source code navigation.

The message produced several forms of knowledge: a precise understanding of the KV cache allocation formula, confirmation that the 12% reserve is computed against total (not available) GPU memory, a clear accounting of where every gigabyte goes (weights: 72.3 GB, target KV: 10.4 GB, draft: 1.55 GB, reserve/headroom: 11.47 GB), and a roadmap of actionable options for increasing KV cache capacity.

The Thinking Process

The assistant's reasoning is visible across the message sequence. It starts with a hypothesis ("maybe it's limited by max_running_requests"), tests it by searching for the max_total_num_tokens assignment chain, narrows the search from tp_worker.py to model_runner.py to model_runner_kv_cache_mixin.py, and finally reads the exact function. The decision to use sed -n "116,200p" rather than cat or head shows precision—the assistant knew exactly which lines to read based on the earlier grep -n results. The truncation at line 200 is also strategic: the function likely continues beyond that, but the assistant got enough to understand the formula and could read more if needed.

This message is a masterclass in evidence-based debugging. When faced with a discrepancy between expected and observed behavior, the assistant didn't tweak parameters blindly—it traced the logic to its source, read the code, and derived the exact formula. The result was not just an answer to the user's question, but a deeper understanding that informed every subsequent decision about KV cache configuration.