The Steady-State Analysis: Measuring SGLang Throughput After FP8 KV Cache Optimization

Introduction

In the high-stakes world of large language model serving, optimization is never a single event — it is a continuous loop of hypothesis, experiment, measurement, and refinement. Message <msg id=3907> captures one of the most critical moments in that loop: the verification step. After deploying a major configuration change — switching the KV cache from bf16 to FP8 quantization — the assistant must answer the fundamental question: did it actually work? This message is the quantitative analysis that follows a server restart, providing a real-time assessment of whether the optimization achieved its goals and revealing the new steady-state behavior of the system.

The broader context is a massive data generation pipeline for EAGLE-3 speculative decoding training. The assistant is running inference on a Kimi-K2.5 model served by SGLang across 8 NVIDIA RTX PRO 6000 Blackwell GPUs, generating responses for tens of thousands of prompts to create training data. The throughput of this server directly determines how quickly the training data can be produced — and with a target of ~88K prompts, every tok/s matters.

The Optimization Journey Preceding This Message

To understand message <msg id=3907>, one must first understand the bottleneck it is evaluating. Throughout segment 28, the assistant has been wrestling with a fundamental constraint: GPU KV cache capacity. Each concurrent request consumes GPU memory for its key-value cache entries, and with long sequences averaging ~4100 tokens, the number of simultaneous requests the server can handle is sharply limited.

The initial configuration used bf16 KV cache with --mem-fraction-static 0.85, yielding approximately 116K maximum tokens of KV cache capacity. This allowed only ~28 concurrent 4K-token requests, and the server throughput hovered around 600 tok/s with a growing queue of pending requests. The assistant tried several levers: increasing mem-fraction-static to 0.88 (which OOM'd at 0.93), enabling hierarchical cache (--enable-hierarchical-cache with --hicache-size 48), and finally switching to FP8 KV cache (--kv-cache-dtype fp8_e4m3) with mem-fraction-static 0.90.

The FP8 change was the breakthrough. As shown in <msg id=3903>, the maximum token count jumped to 376,029 — a 3.2× improvement over the original 116K and more than the ~2× the assistant initially expected. The server came up with 3.52 GB headroom per GPU, and initial measurements in <msg id=3905> showed 1444 tok/s generation throughput with all 150 concurrent requests running and zero in queue — a dramatic improvement over the 600 tok/s seen just minutes earlier.

What the Message Actually Says

The subject message begins with a precise calculation:

33 completions in 120s = 0.275 req/s. At ~4100 avg tokens, that's ~1128 tok/s effective. The server is generating 1351 tok/s but token usage is now at 0.93 — it's filling up again as the long sequences grow. Still much better than before though.

This short paragraph packs in several layers of analysis. The assistant has taken raw data — 33 completions over 2 minutes from the B2_opencodeinstruct dataset — and derived meaningful metrics. The completion count comes from comparing line counts in the raw_responses.jsonl file (1485 at the start of the measurement window, 1520 at the end, as shown in <msg id=3906>). By dividing 33 by 120 seconds, the assistant computes a request throughput of 0.275 requests per second. Multiplying by the assumed average output length of ~4100 tokens yields an effective throughput of approximately 1128 tok/s.

The assistant then immediately compares this derived metric against the server's reported generation throughput of 1351 tok/s (from the SGLang log line showing gen throughput (token/s): 1351.40). The discrepancy — 1128 tok/s effective vs 1351 tok/s server-reported — is notable but not alarming. It likely reflects the difference between raw token generation and the overhead of request handling, prompt processing, and response formatting that the server's internal counter may not capture.

The most telling observation is the token usage metric: 0.93. This means the KV cache is 93% full. After the initial burst of 150 concurrent requests, the system is approaching saturation again. The FP8 optimization bought significant headroom, but with long sequences steadily consuming KV cache entries, the system is once again approaching its memory limit. The assistant correctly interprets this: "it's filling up again as the long sequences grow."

The Thinking Process Revealed

What makes this message particularly interesting is the real-time analytical reasoning on display. The assistant is not simply reporting numbers — it is actively interpreting them, building a mental model of the system's behavior, and identifying the next question to investigate.

The flow of reasoning proceeds in distinct steps:

Compute derived metrics from raw observations. The assistant takes the completion count delta (33) and time interval (120s) and computes request throughput (0.275 req/s). This is not a metric the server reports directly — it must be calculated from file-level data.
Cross-reference multiple data sources. The assistant compares the derived effective throughput (1128 tok/s) against the server's reported generation throughput (1351 tok/s). This cross-validation is crucial for building confidence in the measurements and identifying potential discrepancies.
Identify the limiting factor. By noting the token usage at 0.93, the assistant recognizes that KV cache capacity remains the binding constraint, even with the FP8 improvement. The system is not CPU-bound or network-bound — it is memory-bound.
Contextualize the results. The phrase "Still much better than before though" shows the assistant maintaining perspective. Despite approaching saturation, the current performance represents a significant improvement over the ~600 tok/s baseline.
Formulate the next investigation. The assistant then issues a bash command to examine the throughput logs more carefully: "Let me check what the steady-state looks like — the initial burst of 150 requests is still working through." This reveals an awareness that the system may not yet have reached steady state — the initial batch of requests is still propagating through, and the true equilibrium behavior may differ from the current measurements.

Assumptions Embedded in the Analysis

The assistant's calculations rest on several assumptions that deserve scrutiny:

Average token length of ~4100. This is an estimate, not a precise measurement. The actual distribution of output lengths may vary significantly across prompts, and the average could shift as different categories of prompts are processed. If the true average is higher, the effective throughput would be higher; if lower, it would be lower. The assistant does not explain how this figure was derived — it may come from prior analysis of the dataset or from the --short-max-tokens 10240 and --long-max-tokens 16384 configuration parameters.

The relationship between server throughput and effective throughput. The assistant implicitly assumes that the server's reported gen throughput should approximately equal the product of request throughput and average tokens per request. The gap between 1351 tok/s and 1128 tok/s suggests either that the average token length is actually higher than 4100 (which would increase the effective throughput estimate) or that there is overhead not captured in the simple calculation.

That the system is approaching steady state. The assistant acknowledges this assumption explicitly by checking whether the initial burst is still working through. If the system has not yet reached equilibrium, the current measurements may overstate or understate the long-term throughput.

That token usage of 0.93 is the primary constraint. While KV cache capacity is clearly the dominant factor, other constraints — such as CPU-side request processing, network bandwidth, or disk I/O for logging — could become binding at different load levels.

Input Knowledge Required

To fully understand this message, one needs familiarity with several concepts:

KV cache mechanics in transformer inference. The key-value cache stores intermediate attention computations to avoid redundant calculation during autoregressive generation. Its size scales with sequence length, batch size, and the number of layers, making it the dominant memory consumer during decoding.
SGLang server architecture. SGLang uses a radix attention-based prefix caching system and reports metrics like gen throughput, token usage, running-req, and queue-req in its periodic log messages. Understanding these metrics is essential for interpreting the assistant's analysis.
FP8 quantization for KV cache. The --kv-cache-dtype fp8_e4m3 flag reduces each KV cache entry from 16 bits (bf16) to 8 bits, roughly doubling capacity. For MLA (Multi-head Latent Attention) models like Kimi-K2.5, the KV cache is already a compressed latent representation, so further quantization to FP8 carries some quality risk but is generally safe.
The relationship between token throughput and request throughput. For a given average output length L, a request throughput of R req/s corresponds to a token throughput of R × L tok/s. This simple relationship underlies the assistant's calculation.

Output Knowledge Created

This message produces several valuable pieces of knowledge:

Quantified confirmation that the FP8 KV cache optimization is effective. The system is achieving approximately 1128-1351 tok/s, roughly 2× the ~600 tok/s baseline from earlier configurations.
Identification of the new bottleneck. Even with 376K token capacity, the KV cache fills to 93% utilization during steady operation with 150 concurrent long-sequence requests. This establishes a ceiling on further throughput improvements without additional memory capacity.
A refined understanding of system behavior. The assistant now knows that the initial burst of requests creates a transient period before steady state, and that token usage grows over time as sequences lengthen.
A benchmark for future comparisons. The measured 0.275 req/s and ~1128 tok/s effective throughput provide a baseline against which future optimizations (such as shorter timeouts, different concurrency settings, or alternative KV cache strategies) can be evaluated.

The Broader Significance

Message <msg id=3907> exemplifies a pattern that recurs throughout the entire opencode session: the tight coupling of measurement and action. The assistant does not simply apply a configuration change and hope for the best — it measures the result, interprets the data, and uses that interpretation to guide the next step. This message is the analytical pivot point between two actions: the server restart with FP8 KV cache and the subsequent decision about whether the current configuration is sufficient for the data generation pipeline.

The message also reveals the inherent tension in LLM serving optimization. Each improvement — FP8 KV cache, hierarchical cache, higher memory fraction — shifts the bottleneck rather than eliminating it. The system went from being limited by ~28 concurrent requests (bf16 KV cache) to being limited by ~94 concurrent requests (FP8 KV cache), but it is still limited. The 0.93 token usage is a reminder that in serving systems, there is always a bottleneck somewhere, and the optimizer's job is to push it to the most acceptable location.

For the data generation pipeline, this analysis has practical implications. At 0.275 req/s and ~88K prompts remaining, the estimated completion time is approximately 89 hours — still a significant duration. The assistant's next steps will likely involve either accepting this throughput, finding further optimizations (such as reducing average sequence length through earlier stopping), or scaling horizontally with additional server instances.

Conclusion

Message <msg id=3907> is a masterclass in real-time performance analysis. In just a few sentences, the assistant computes derived metrics, cross-references multiple data sources, identifies the binding constraint, contextualizes the results against prior performance, and formulates the next investigative question. It is a small but dense piece of analytical work that demonstrates the continuous measurement-and-iteration cycle at the heart of effective ML infrastructure engineering. The FP8 KV cache optimization worked — throughput roughly doubled — but the system remains memory-bound, and the assistant knows exactly where to look next.