Tracing the Memory Gap: Diagnosing KV Cache Underutilization in an 8-GPU SGLang Deployment

The Message

[assistant] Now I see the full picture. Let me trace the math:

- **Total GPU**: 96 GB (97,887 MiB)
- **Target model weights**: 72.33 GB → avail = 21.71 GB
- **`mem_fraction_static 0.88`**: 0.88 × 21.71 = 19.10 GB reserved for target KV cache
- **Target KV cache**: 10.42 GB (159,277 tokens) — wait, that's only ~half of 19.10 GB
- **Draft model weights**: 0.95 GB → avail drops to ~8.51 GB
- **Draft KV cache**: 0.30 + 0.30 = 0.60 GB (same 159,277 tokens, but draft model is tiny)
- **After memory pools**: avail = ~7.90 GB
- **After cuda graphs**: avail = ~7.43 GB

So there's **7.43 GB still free per GPU** after everything. The `mem_fraction_static 0.88` of 21.71 GB = 19.1 GB, but target KV + draft KV + draft weights + cuda graphs only uses ~12.3 GB. Something in the memory accounting is leaving headroom.

Let me check how the KV cache size is actually computed — maybe it's limited by `max_running_requests=48` rather than memory:
[bash] ssh root@10.1.230.174 'grep -n "max_total_num_tokens\|init_memory_pool\|rest_memory\|available_size\|mem_fraction" /root/sglang/python/sglang/srt/managers/tp_worker.py | head -30'
80:            self.model_runner.full_max_total_num_tokens,
81:            self.model_runner.swa_max_total_num_tokens,
279:        self.max_total_num_tokens = self.model_runner.max_total_num_tokens
332:            mem_fraction_static=self.server_args.mem_fraction_static,
357:                    mem_fraction_static=self.server_args.mem_fraction_static,
396:            self.max_total_num_tokens,

Context and Motivation

This message arrives at a pivotal moment in a long-running deployment optimization session. The user has been deploying a massive 547 GB Kimi-K2.5 INT4 model across 8 NVIDIA RTX PRO 6000 Blackwell GPUs using SGLang with EAGLE-3 speculative decoding. After successfully getting the model online with tool call parsing, reasoning extraction, and a systemd service, the user raised a critical operational concern: they were "low on max parallel request context." In production serving, the number of concurrent requests a system can handle is directly proportional to the KV cache size — each request consumes a portion of the cache, and when it fills up, new requests must wait. The user was feeling the pinch of limited throughput capacity.

The assistant's response in message 5727 is a direct diagnostic reaction to this concern. Rather than immediately jumping to solutions like enabling hierarchical KV cache offloading to RAM (which was researched in the preceding subagent task), the assistant first needed to understand why the KV cache was so small. This is a classic engineering discipline: diagnose before prescribing.

The Reasoning Process

The message reveals a meticulous step-by-step accounting of GPU memory. The assistant starts from first principles: total GPU memory is 96 GB per card. After loading the target model weights (72.33 GB), 21.71 GB remains available. The --mem-fraction-static 0.88 flag reserves 88% of that remaining memory for the KV cache pool, which should yield 19.10 GB. But the actual KV cache allocation is only 10.42 GB — barely half of what was reserved.

This discrepancy is the core puzzle. The assistant traces the full chain: target KV cache (10.42 GB), draft model weights (0.95 GB), draft KV cache (0.60 GB), memory pool overhead, and CUDA graph capture. After all allocations, 7.43 GB per GPU remains completely unused. The total used for KV-related purposes is ~12.3 GB, far short of the 19.1 GB that mem_fraction_static reserved.

The "wait" in the message — "Target KV cache: 10.42 GB (159,277 tokens) — wait, that's only ~half of 19.10 GB" — captures the moment of cognitive dissonance. The numbers don't add up. Something is constraining the KV cache allocation independently of the memory reservation.

The Diagnostic Leap

The assistant formulates a hypothesis: "maybe it's limited by max_running_requests=48 rather than memory." This is a sophisticated inference. In SGLang's architecture, the KV cache is organized as a token pool. The total number of tokens that can be cached is determined by two factors: the available memory budget (controlled by mem_fraction_static) and the maximum number of concurrent requests (which determines how the cache is partitioned). If max_running_requests is set too low, it can cap the token pool size even when ample memory exists, because the cache management system needs to reserve per-request overhead and ensure fair allocation.

The assistant then reaches for the source code, grepping through /root/sglang/python/sglang/srt/managers/tp_worker.py to find how max_total_num_tokens is computed. The grep targets key variables: init_memory_pool, rest_memory, available_size, mem_fraction. This is a developer-level debugging approach — rather than consulting documentation or configuration guides, the assistant goes straight to the implementation to understand the actual computation.

Assumptions and Their Implications

Several assumptions underpin this diagnostic message:

Assumption 1: Memory accounting is correct. The assistant trusts the reported values from the server logs — 72.33 GB for weights, 10.42 GB for KV cache, 0.95 GB for draft model. If any of these numbers are inaccurate (e.g., if memory fragmentation or CUDA driver overhead is not being reported), the entire chain of reasoning would be built on a faulty foundation.

Assumption 2: The mem_fraction_static parameter directly controls KV cache size. The assistant assumes that 0.88 × available_memory_after_weights should equal the KV cache allocation. In reality, the parameter might interact with other constraints in ways that are not purely multiplicative — for instance, it might set an upper bound that other limits then override.

Assumption 3: The draft model's memory usage is correctly isolated. The assistant treats the draft EAGLE-3 model as consuming 0.95 GB independently. But in a tensor-parallel deployment across 8 GPUs, memory sharing and communication buffers could complicate this accounting.

Assumption 4: max_running_requests is the likely culprit. This is the strongest assumption in the message. The assistant has already identified the symptom (7.43 GB free per GPU) and is now searching for a cause. The choice to investigate max_running_requests rather than, say, memory fragmentation, CUDA graph reservation overhead, or a bug in the memory pool initialization, reflects an intuition about how SGLang's scheduler works.

Input Knowledge Required

To fully understand this message, one needs:

  1. SGLang architecture knowledge: Understanding that mem_fraction_static reserves GPU memory for KV cache, that max_total_num_tokens determines the token pool size, and that max_running_requests can constrain concurrent request handling.
  2. Tensor parallelism concepts: The model is split across 8 GPUs with --tp 8, meaning each GPU holds 1/8 of the weights. The 72.33 GB "mem usage" per GPU reflects this sharding.
  3. Speculative decoding memory model: EAGLE-3 requires both a target model and a draft model, each with its own KV cache. The draft model is much smaller (0.95 GB vs 72.33 GB) but still consumes memory.
  4. CUDA graph capture: The message mentions "After cuda graphs: avail = ~7.43 GB." CUDA graphs are pre-optimized execution traces that SGLang uses to reduce launch overhead. They consume GPU memory for the captured graph structures.
  5. The preceding conversation context: The user's complaint about being "low on max parallel request context" provides the motivation. The subagent task researching hierarchical cache options (message 5723) provides the solution space that this diagnostic message is feeding into.

Output Knowledge Created

This message produces several valuable insights:

  1. Quantified memory waste: The assistant has precisely calculated that 7.43 GB per GPU (59.44 GB across all 8 GPUs) is sitting idle. This is a concrete, actionable finding.
  2. Identified the discrepancy: The gap between reserved memory (19.1 GB) and actual KV cache usage (10.42 GB) is approximately 8.7 GB per GPU. This is the "missing" memory that could be repurposed for KV cache expansion.
  3. A testable hypothesis: The suggestion that max_running_requests=48 is the limiting factor provides a clear next step for investigation. If confirmed, increasing this limit would allow the KV cache to grow into the reserved memory.
  4. A debugging methodology: The message demonstrates a template for diagnosing memory utilization issues: start from total capacity, subtract known allocations, compare expected vs actual values, identify discrepancies, and trace the computation in source code.

The Thinking Process in Action

What makes this message particularly interesting is the visible reasoning arc. It begins with a moment of synthesis ("Now I see the full picture"), proceeds through a structured calculation, encounters an anomaly ("wait, that's only ~half"), quantifies the gap, formulates a hypothesis, and immediately moves to verify it by examining source code.

The "wait" is a genuine artifact of real-time reasoning — the assistant is thinking through the math as it writes, and the discrepancy only becomes apparent mid-computation. This is not a polished, post-hoc explanation but a live diagnostic trace.

The shift from arithmetic to code investigation is also telling. The assistant doesn't speculate further or consult documentation; it goes straight to tp_worker.py to find the actual computation. This reflects a debugging philosophy: when runtime behavior contradicts configuration expectations, the implementation is the ground truth.

Broader Significance

In the context of the full session, this message represents a critical turning point. The user's concern about parallel request capacity could have been addressed by immediately enabling hierarchical KV cache offloading (the solution researched in the subagent task). But the assistant chose instead to first understand why the existing GPU memory was underutilized. This is a "measure twice, cut once" approach — optimizing the GPU-side cache before falling back to RAM offloading.

The message also reveals a tension in production ML serving: configuration parameters like mem_fraction_static and max_running_requests interact in non-obvious ways. A user might set a high memory fraction expecting large cache capacity, only to find that request-level limits silently cap the allocation. The assistant's diagnostic work here is essentially reverse-engineering this interaction to understand the actual constraint surface.

Conclusion

Message 5727 is a masterclass in systematic debugging of GPU memory utilization in a complex ML serving stack. It combines quantitative reasoning (tracing the memory budget), anomaly detection (identifying the 8.7 GB gap), hypothesis formation (suspecting max_running_requests), and empirical verification (grepping the source code). The message doesn't solve the problem — it sets up the next investigation step — but it transforms a vague concern about "low parallel request context" into a precise, testable hypothesis about a configuration constraint. This diagnostic clarity is often the most valuable contribution in a debugging session, as it directs subsequent effort toward the actual bottleneck rather than speculative optimization.