The Memory Arithmetic: Diagnosing KV Cache Bottlenecks in SGLang Inference

Introduction

In the high-stakes world of large language model inference, throughput is king. When you have eight NVIDIA RTX PRO 6000 Blackwell GPUs, each with 96 GiB of VRAM, and a state-of-the-art model like Kimi-K2.5 (a 547B-parameter Mixture-of-Experts architecture), the expectation is raw speed. So when the user reports that SGLang is "only doing 500tps" and asks "what seems to be the bottleneck? Should be 4-5x faster," the assistant faces a classic systems debugging challenge: is the bottleneck in compute, memory, scheduling, or the client-server interaction?

Message 3836 is the turning point in this diagnostic journey. It is the moment where the assistant stops looking at throughput logs and request queues, and instead starts doing the hard arithmetic of GPU memory allocation. This message is a window into a specific kind of engineering reasoning — the kind where you must understand not just what a configuration parameter does, but how the framework's memory model maps onto physical hardware constraints. It is a message about trade-offs, about the diminishing returns of squeezing VRAM, and about the fundamental tension between KV cache capacity and model weight footprint.

The Scene: An Inference Pipeline Under Pressure

To understand message 3836, we must first understand the context that produced it. The assistant is running a large-scale inference pipeline to generate synthetic training data for an EAGLE-3 speculative decoding drafter. The pipeline processes two datasets: B1_glaive (shorter, tool-calling responses averaging ~1,500 tokens) and B2_opencodeinstruct (longer, creative responses averaging ~4,100 tokens). The SGLang server is configured with 150 maximum concurrent requests, but the logs tell a different story.

In the messages leading up to 3836, the assistant has already done significant diagnostic work. Message 3832 reveals that the server's generation throughput is actually 780–890 tok/s — not 500 tok/s as the user feared. But message 3833 identifies a deeper problem: only 46–56 requests are running concurrently despite 94–101 sitting in the queue. The KV cache token usage hovers at 0.85–0.96, meaning it is nearly full. The scheduler cannot admit new requests because there is no room to prefill their KV cache entries.

Message 3834 confirms the diagnosis: with --mem-fraction-static 0.85, the server can hold approximately 116,171 tokens total across the KV cache. With 50 requests averaging ~2,000 tokens each (and the B2 dataset averaging over 4,000), the cache fills up quickly. The assistant lists three options: increase mem_fraction_static, reduce max_new_tokens, or use chunked prefill.

Message 3835 then checks nvidia-smi, revealing ~5 GB free per GPU. This is the critical data point that sets up message 3836. The assistant has a number — 5 GB of headroom — and needs to figure out how much of that can be converted into KV cache capacity.

The Subject Message: A Deep Dive into Memory Arithmetic

Here is the subject message in full:

~5GB free per GPU. At 0.85, we're using 83.2GB for model+KV. The model weights take ~68.4GB (547GB / 8 GPUs), leaving ~14.8GB for KV cache per GPU. Let me see how much more we could squeeze:

>

[bash command with inline Python script]

>

mem_fraction_static=0.88: ~120271 max_total_tokens (3.5% more) mem_fraction_static=0.9: ~123004 max_total_tokens (5.9% more) mem_fraction_static=0.92: ~125738 max_total_tokens (8.2% more) mem_fraction_static=0.93: ~127104 max_total_tokens (9.4% more) mem_fraction_static=0.95: ~129838 max_total_tokens (11.8% more)

The message is deceptively simple in its output, but the Python script embedded in the bash command reveals a fascinating internal monologue. The assistant is reasoning through the memory model of SGLang in real time, and the code comments show the evolution of its understanding.

The Thinking Process: Wrestling with Memory Models

The Python script in message 3836 is not clean production code. It is a thinking trace — a sequence of attempts to model how SGLang allocates GPU memory. The assistant starts with one model, realizes it is wrong, tries another, and eventually settles on a pragmatic approximation.

The first attempt assumes a simple linear relationship: "at 0.85, static alloc = 0.85 × 97887 = 83204 MiB." But then the assistant notices a contradiction: "Used = 92137 MiB. So overhead (model + other) = 92137 - 83204 = 8933 MiB... that seems too high." The 8,933 MiB figure for non-KV, non-model overhead is implausible, so the assistant backtracks.

The second attempt considers whether mem_fraction_static is "the fraction of remaining memory after model load." This is a common confusion point. SGLang's mem_fraction_static parameter controls what fraction of the free GPU memory (after the model weights are loaded) should be reserved for the KV cache. The remaining free memory is left for temporary allocations, prefill buffers, and other runtime overhead.

But then the assistant tries yet another interpretation: "Actually in SGLang, mem_fraction_static is fraction of TOTAL GPU memory to use for KV cache + model." This is closer to the truth but still not quite right. The parameter is applied to the memory remaining after the model weights are loaded — it determines what fraction of that remaining memory is allocated to the static KV cache pool.

The assistant eventually gives up on deriving the exact formula and falls back to a pragmatic approach: "Simpler: we know at 0.85 we get 116171 tokens. Let me compute at 0.90 and 0.93. The KV cache bytes = 116171 × bytes_per_token. At 0.90, bytes grow by 0.90/0.85 = 1.059x."

This is a proportional scaling approach. If mem_fraction_static = 0.85 yields 116,171 tokens of KV cache capacity, then mem_fraction_static = 0.90 should yield 116,171 × (0.90 / 0.85) = 123,005 tokens. This is a reasonable approximation if the relationship between the parameter and the resulting capacity is linear — which it approximately is, as long as the model weight footprint remains constant and the total GPU memory is fixed.

The output table tells a sobering story. Even pushing to mem_fraction_static = 0.95 — which leaves almost no headroom for runtime allocations — yields only 129,838 tokens, an 11.8% increase over the baseline. This is nowhere near the 4–5× improvement the user expected.

Assumptions and Their Implications

The assistant makes several assumptions in this analysis, some explicit and some implicit.

Assumption 1: Linear scaling. The proportional calculation assumes that KV cache capacity scales linearly with mem_fraction_static. This is approximately true, but it ignores second-order effects. At higher fractions, the model weights might not fit as cleanly, or the runtime overhead might increase due to larger prefill buffers or activation memory. The linear model is a useful first approximation, but it could overestimate the gains at the high end.

Assumption 2: Model weight size is fixed at ~68.4 GB. The assistant computes this as 547 GB / 8 GPUs = 68.4 GB per GPU. This assumes perfect even distribution of model parameters across the 8 GPUs in the tensor parallelism (TP=8) configuration. In practice, some layers or parameters might be distributed unevenly, and the memory-mapping overhead from the model loading process could vary.

Assumption 3: The KV cache is the only bottleneck. The assistant focuses entirely on KV cache capacity, implicitly assuming that compute (GPU cores, memory bandwidth) is not the limiting factor. The gen throughput of ~850 tok/s suggests the compute is being utilized, but the question is whether increasing KV cache capacity would actually translate to higher throughput, or whether the scheduler would still be limited by prefill bandwidth or other factors.

Assumption 4: The 5 GB of free memory is usable for KV cache. The nvidia-smi output shows ~5 GB free per GPU. The assistant treats this as headroom that could be allocated to the KV cache. However, some of this free memory might be needed for runtime allocations, temporary tensors, or the prefill buffer. Pushing mem_fraction_static too high could cause out-of-memory (OOM) errors during operation.

Potential Mistakes and Incorrect Assumptions

The most significant potential mistake in this message is the oversimplification of the memory model. The assistant's Python script contains a revealing comment: "Wait, that is not how it works. mem_fraction_static is fraction of FREE memory after loading." This self-correction shows that the assistant is aware its initial model was wrong, but the final proportional calculation still glosses over the exact semantics.

In SGLang, mem_fraction_static is applied to the memory remaining after the model weights are loaded and other fixed allocations are made. The exact formula is:

kv_cache_pool_size = mem_fraction_static × (total_gpu_memory - model_weights - fixed_overhead)

The proportional scaling new_tokens = 116171 × new_frac / 0.85 implicitly assumes that (total_gpu_memory - model_weights - fixed_overhead) is constant, which it is. But it also assumes that the KV cache token capacity is exactly proportional to the pool size, which is true if the bytes-per-token is constant. So the calculation is actually sound given the assumption that 116,171 tokens at 0.85 is a reliable measurement.

A more subtle issue: the assistant does not verify that the 116,171 token figure is actually the maximum achievable at 0.85, or whether it is the current usage. The max_total_num_tokens field from the server info is the configured maximum, not the achieved maximum. If the server is configured with mem_fraction_static=0.85 but the actual KV cache usage never reaches the theoretical maximum due to fragmentation or scheduling inefficiencies, then the proportional calculation would overestimate the gains.

Another potential blind spot: the assistant does not consider fragmentation in the KV cache. SGLang uses a paged KV cache (similar to vLLM's PagedAttention), where memory is allocated in pages. If requests have varying sequence lengths, the cache can become fragmented, reducing the effective capacity below the theoretical maximum. The 0.85–0.96 token usage figures suggest the cache is nearly full, but fragmentation could mean that even with more memory, the effective capacity gain would be less than linear.

Input Knowledge Required

To fully understand message 3836, the reader needs knowledge spanning several domains:

SGLang architecture. The mem_fraction_static parameter controls what fraction of available GPU memory is allocated to the static KV cache pool. Understanding this requires familiarity with how SGLang separates static allocations (KV cache, model weights) from dynamic allocations (temporary tensors, activation memory). The parameter is a trade-off: higher values give more KV cache capacity but leave less headroom for prefill and computation.

KV cache mechanics in MLA (Multi-head Latent Attention). The Kimi-K2.5 model uses DeepSeek's MLA architecture, which has a compressed KV cache. Instead of storing full key-value pairs for each attention head, MLA stores a low-rank compressed representation (kv_lora_rank=512). This means the bytes-per-token for the KV cache is much smaller than in standard multi-head attention. The assistant's calculation of ~14.8 GB for KV cache per GPU at 116,171 tokens implies roughly 136 bytes per token — a figure that is consistent with MLA's compressed format across 61 layers.

GPU memory accounting. The assistant uses nvidia-smi output (92137 MiB used out of 97887 MiB total) and the mem_fraction_static setting (0.85) to back-calculate the model weight footprint. This requires understanding that the total used memory equals model weights plus KV cache plus runtime overhead. The calculation model_weights = 92137 - (0.85 × 97887) is an attempt to isolate the model weight contribution, though the assistant quickly realizes this is an oversimplification.

Tensor parallelism and model sharding. The 547B-parameter model is split across 8 GPUs using tensor parallelism (TP=8). The assistant computes ~68.4 GB per GPU (547 GB / 8), which assumes the model is evenly distributed. This is a reasonable approximation for MoE architectures, though the actual per-GPU footprint depends on the specific sharding strategy.

Proportional reasoning. The core analytical move in this message is proportional scaling: using a known data point (116,171 tokens at 0.85) to extrapolate to other values. This is a standard engineering technique when the exact system model is unknown or too complex to derive from first principles.

Output Knowledge Created

Message 3836 produces several concrete outputs that shape the subsequent conversation:

A quantified trade-off table. The assistant produces a clear table showing the token capacity at various mem_fraction_static values, from 0.88 to 0.95. This table is immediately actionable — the user can decide whether an 11.8% capacity increase is worth the risk of OOM errors.

A negative result. The most important output is the realization that KV cache expansion alone cannot achieve the 4–5× throughput improvement the user expects. Even at the aggressive 0.95 setting, the gain is only 11.8%. This negative result forces the assistant to look elsewhere for the bottleneck — which it does in the very next message (3837), pivoting to prefill cost analysis and client-side measurement issues.

A validated methodology. The assistant demonstrates a pattern of reasoning that can be applied to other performance debugging scenarios: measure the current state, understand the configuration parameter, compute the theoretical maximum, and compare with the observed reality. The discrepancy between the theoretical maximum and the observed throughput is where the real bottleneck lies.

A corrected mental model. The assistant's Python script shows a learning process — it starts with an incorrect model of how mem_fraction_static works, iterates through several alternatives, and settles on a pragmatic approximation. This thinking trace is itself a valuable artifact, documenting the process of building a mental model of a complex system.

The Broader Significance

Message 3836 is a microcosm of a fundamental challenge in LLM inference optimization: the tension between KV cache capacity and model weight footprint. On modern GPUs with 96 GiB of VRAM, a 547B-parameter model (even quantized to INT4) consumes roughly 70% of available memory just for weights. The remaining 30% must be split between KV cache, prefill buffers, activation memory, and runtime overhead. This leaves relatively little room for the KV cache, which is the primary determinant of how many concurrent requests can be served.

The assistant's analysis reveals a hard truth: when the model is this large relative to the GPU memory, KV cache capacity is fundamentally constrained. The mem_fraction_static parameter offers only marginal gains — moving from 0.85 to 0.95 yields less than 12% improvement. The real bottleneck is not the configuration parameter but the hardware capacity itself.

This insight drives the subsequent conversation. In message 3837, the assistant pivots to analyzing prefill costs and client-side measurement. In message 3839, the user suggests considering RAM offloading. The assistant's memory arithmetic in message 3836 is the foundation that makes these subsequent explorations possible — without it, the team might have wasted time chasing configuration tweaks that could never deliver the 4–5× improvement they wanted.

Conclusion

Message 3836 is a masterclass in applied systems reasoning. It demonstrates how to take a vague performance complaint ("only doing 500tps, should be 4-5x faster") and transform it into a precise, quantified understanding of hardware constraints. The assistant navigates the ambiguity of SGLang's memory model, makes reasonable assumptions where exact knowledge is lacking, and produces actionable output — even when that output is a negative result that rules out the most obvious fix.

The message also illustrates the value of thinking traces. The Python script's comments — with their self-corrections, false starts, and pragmatic compromises — are not just code; they are a record of the assistant building a mental model in real time. For anyone debugging a similar system, this kind of transparent reasoning is more valuable than a polished answer, because it shows how to think about the problem, not just what the answer is.

In the end, the KV cache arithmetic tells a clear story: when your model consumes 70% of GPU memory, the remaining 30% cannot be stretched to 4–5× capacity through configuration alone. The bottleneck is hardware, not software — and knowing that is the first step toward finding a real solution.