The 32 t/s Puzzle: Diagnosing a Throughput Regression in Speculative Decoding

Introduction

In any high-stakes machine learning deployment, the moment a user reports "throughput seems off" is a moment of reckoning. The numbers are concrete, measurable, and unforgiving: 32 tokens per second at 5k context versus a baseline of 138. A 4.3× gap demands explanation. In message [msg 12129] of this opencode session, the assistant confronts exactly this discrepancy—and what unfolds is a masterclass in diagnostic reasoning under uncertainty.

The message sits at a critical juncture in a long-running session to deploy and optimize the Kimi K2.6 model with DFlash speculative decoding (DDTree) on 8× RTX PRO 6000 Blackwell GPUs. The user had just reported a severe throughput regression after the assistant enabled reasoning and tool-call parsers on the live SGLang service. The assistant's initial hypothesis was that the parser change might be responsible, but quick measurements ruled that out. Now, in this message, the assistant digs deeper—and discovers that the real problem is far more subtle than a simple configuration error.

The Puzzle: Contradictory Measurements

The message opens with the assistant wrestling with a genuine contradiction in its own data. The two-point decode measurement—a technique that isolates pure decode throughput by measuring the time difference between two requests with different max_tokens values—had shown 113 tok/s at context 3072 and 67 tok/s at context 5120. These numbers were healthy and followed the expected attention-bound scaling curve for C=1 (single-verify) speculative decoding on Blackwell hardware.

Yet when the assistant reproduced the user's exact scenario—a 2701-token prompt generating 1500 tokens—the result was 32.7 tok/s, with a time-to-first-token of only 0.06 seconds confirming that prefill was not the bottleneck. The contradiction is stark: the two-point method says 113 tok/s at similar context, but the full generation says 32.7 tok/s. Both measurements were taken on the same service, same hardware, same model, within minutes of each other.

The assistant's reasoning captures the cognitive dissonance perfectly:

"The puzzle deepens: generating more tokens appears slower per token than generating fewer tokens, and the full generation at lower context (2701→4201) is slower than the two-point measurements at higher context, which is backwards."

This is the hallmark of a genuine systems-level mystery: the data contradicts naive expectations, and the only way forward is to form hypotheses and test them.

Hypothesis Formation: What Could Cause a 3.5× Slowdown?

The assistant works through several candidate explanations with clear quantitative reasoning:

Hypothesis 1: Acceptance collapse. If the drafter's acceptance rate drops during long generations—say from 5 tokens per step to 1—that would directly explain the throughput loss. The assistant calculates: "a step time of 44ms for the short generation versus 156ms for the long one — a 3.5× slowdown despite similar context sizes."

Hypothesis 2: KV cache fragmentation. The service uses page_size=1 for its KV cache, and DDTree's non-contiguous node acceptance pattern could scatter cache entries across memory pages, degrading attention performance over extended sequences. This is a plausible systems-level explanation that would compound over hundreds of steps.

Hypothesis 3: Output-length-driven degradation. Perhaps the act of generating many tokens itself degrades performance, independent of context length—a kind of "generation fatigue" in the inference engine.

The assistant's leading hypothesis is KV cache fragmentation, but it wisely decides to gather data before committing to any explanation. The key insight is that the two-point method's short generation (~200 tokens) doesn't expose whatever degradation mechanism affects the 1500-token generation.

The Diagnostic: Gathering Acceptance Metrics from the Live Service

The assistant executes a targeted bash command to extract DDTree debug metrics from the systemd journal during the long generation:

timeout 30 ssh -o StrictHostKeyChecking=no root@10.1.230.171 'echo "=== DDTREE metrics during the long gen (TP0 only, last 40) ==="; journalctl -u sglang-k26-ddtree --no-pager --since "-60s" 2>/dev/null | sed "s/[[:cntrl:]]//g" | grep "TP0\].*DDTREE metrics" | grep -oE "avg_accepted_drafts=[0-9.]+ avg_commit_len=[0-9.]+" | sort | uniq -c | sort -rn | head'

This command is carefully constructed:

It filters to the last 60 seconds of logs, capturing the just-completed 1500-token generation
It restricts to TP0 (tensor parallelism rank 0) to avoid duplicate metrics
It extracts only the avg_accepted_drafts and avg_commit_len fields
It counts and sorts unique value pairs to show the distribution The results are revealing:

     67 avg_accepted_drafts=1.00 avg_commit_len=2.00
     63 avg_accepted_drafts=2.00 avg_commit_len=3.00
     40 avg_accepted_drafts=3.00 avg_commit_len=4.00
     34 avg_accepted_drafts=0.00 avg_commit_len=1.00
     20 avg_accepted_drafts=4.00 avg_commit_len=5.00
     10 avg_accepted_drafts=5.00 avg_commit_len=6.00
      2 avg_accepted_drafts=6.00 avg_commit_len=7.00
      1 avg_accepted_drafts=1.00 avg_commit_len=1.00

Interpreting the Distribution: The Acceptance Profile

This histogram of acceptance values across the 1500-token generation tells a nuanced story. The earlier quick tests had shown a consistent avg_accepted_drafts=4.00, but that was an average over a short burst. The full distribution reveals:

Mode acceptance is 1-2 drafts, not 4-5. The most common outcomes are 1 draft accepted (67 steps) and 2 drafts accepted (63 steps).
Zero-accept steps are common: 34 steps where no draft was accepted at all, meaning the target model had to generate from scratch—the worst case for speculative decoding.
High-acceptance steps are rare: only 2 steps with 6 drafts accepted, and 1 with 7.
The weighted average is approximately 2.9 tokens per step, far below the ~5 seen in short tests. This distribution is characteristic of an undertrained drafter that performs well on simple, predictable text but struggles with the kind of complex reasoning and free-form generation that the Kimi K2.6 model produces. The drafter was likely trained on a narrow distribution of text, and when asked to predict tokens in a long, open-ended reasoning generation, its accuracy degrades significantly. The assistant's reasoning correctly identifies this: "the undertrained drafter generalizes poorly to long free-form/reasoning text." But the acceptance drop alone only explains about a 1.7× slowdown (from ~5 to ~2.9 tokens per step). The remaining gap—another ~2×—must come from increased step time.

The Step-Time Mystery

The assistant performs a critical calculation in its reasoning:

"the two-point method's 200 tokens at 113 t/s takes about 1.77 seconds for the decode portion, while the full generation's 1500 tokens at 32 t/s takes roughly 47 seconds. That's a step time of 44ms for the short generation versus 156ms for the long one — a 3.5× slowdown despite similar context sizes."

This is the key insight: the step time itself has increased by 3.5× between the short and long generation, even though the context length is similar or lower in the long generation. This cannot be explained by context scaling alone.

The assistant correctly identifies that this points to a stateful degradation mechanism—something that accumulates over the course of a long generation. The leading candidate is KV cache fragmentation: with page_size=1 and DDTree's non-contiguous node acceptance pattern, each accepted token may be stored in a new page that is not physically adjacent to previous tokens, causing the attention kernel to access scattered memory locations. Over 300+ steps, this fragmentation compounds, degrading memory bandwidth utilization and increasing step time.

Assumptions and Their Validity

The assistant makes several assumptions in this message, some explicit and some implicit:

Assumption 1: The two-point method accurately measures pure decode throughput. This is a reasonable assumption—by subtracting the time of a short max_tokens request from a longer one, the prefill time cancels out. However, the assistant later discovers that the two-point method's short generation (~200 tokens) samples a period when acceptance is artificially high (the "honeymoon phase" of generation), making its throughput estimate optimistic for long generations.

Assumption 2: The parser change did not cause the regression. The assistant had earlier ruled this out by reasoning that parsers are output post-processing and don't affect decode speed. This assumption holds—the data confirms that the service's raw decode performance is consistent with expectations given the context length and acceptance rate.

Assumption 3: KV cache fragmentation is the likely cause of step-time growth. This is a hypothesis, not a confirmed diagnosis. The assistant does not test this directly in this message (it plans to do so in the next message by measuring decode speed from a short prompt with long generation). The assumption is reasonable given the known behavior of page-based KV caches with non-contiguous allocations, but it remains unverified at this point.

Assumption 4: The draft window size of 2048 is contributing to low acceptance. The assistant reasons that at 3239+ context, the drafter sees only the last 2048 tokens, losing earlier context and making worse predictions. This is a plausible mechanism, though the assistant acknowledges it's hard to separate from the drafter's general poor performance on generated text.

Input Knowledge Required

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

Speculative decoding (DDTree): The core technique where a small, fast "draft" model proposes multiple token sequences, and the large "target" model verifies them in parallel. The avg_accepted_drafts metric measures how many of the proposed tokens are accepted per verification step.

Two-point decode measurement: A benchmarking technique that isolates decode throughput by measuring the time difference between two requests with different max_tokens values but the same prompt. This cancels out prefill time and gives pure decode throughput.

KV cache and page_size: The key-value cache stores attention states for all previous tokens. With page_size=1, each token's KV state occupies a separate memory page. Non-contiguous access patterns (from DDTree's tree-structured acceptance) can cause fragmentation and degrade memory bandwidth.

Attention-bound vs compute-bound: At short context lengths, transformer inference is typically compute-bound (dominated by matrix multiplications). As context grows, attention becomes the bottleneck because each query must attend over all previous tokens, making inference attention-bound.

C=1 speculative decoding: A configuration where the target model performs one verification pass per step (as opposed to verifying multiple draft trees in parallel).

Output Knowledge Created

This message produces several valuable pieces of knowledge:

The acceptance distribution under load: The first detailed characterization of DDTree acceptance rates during a long, realistic generation on Kimi K2.6. The finding that acceptance drops from ~5 to ~2.9 over 1500 tokens is a significant data point for understanding speculative decoding behavior on reasoning-capable models.
The step-time growth phenomenon: The discovery that step time increases by 3.5× during a long generation even at constant context length. This is a previously unmeasured effect that points to stateful degradation in the inference engine.
The two-point method's blind spot: The message reveals that short-burst measurements systematically overestimate throughput because they sample the "easy" early portion of generation where acceptance is highest. This is a methodological insight that applies to any speculative decoding benchmark.
A validated diagnostic methodology: The combination of two-point measurement, full-generation reproduction, and acceptance distribution analysis forms a template for diagnosing throughput regressions in speculative decoding systems.

The Thinking Process: A Case Study in Diagnostic Reasoning

The assistant's reasoning in this message exemplifies several hallmarks of effective systems debugging:

Contradiction-driven investigation. Rather than accepting the user's report at face value or dismissing it as measurement error, the assistant identifies a genuine contradiction in its own data (113 tok/s vs 32 tok/s) and treats it as the primary object of investigation.

Quantitative hypothesis formation. Each hypothesis is accompanied by a concrete calculation: "a step time of 44ms for the short generation versus 156ms for the long one — a 3.5× slowdown." This forces precision and testability.

Separating mechanism from symptom. The assistant correctly distinguishes between the proximate cause (low acceptance) and the deeper cause (undertrained drafter + context scaling + fragmentation). It identifies multiple interacting factors rather than searching for a single root cause.

Acknowledging uncertainty. The reasoning is filled with conditional language: "my leading hypothesis," "I'm wondering if," "the most likely culprits." This intellectual honesty prevents premature commitment to a wrong explanation.

Planning the next experiment. Even as the assistant presents its findings, it's already designing the next diagnostic: measuring decode speed from a short prompt with long generation to separate output-length effects from context-length effects.

Conclusion

Message [msg 12129] captures a pivotal moment in a complex systems debugging effort. The assistant takes a user-reported throughput regression, reproduces it, identifies a contradiction in its own measurements, gathers targeted diagnostic data, and produces a nuanced explanation that implicates multiple interacting factors: an undertrained drafter that generalizes poorly to reasoning text, KV cache fragmentation under page-based allocation, and the inherent attention-bound scaling of C=1 speculative decoding.

The 32 t/s that so alarmed the user turns out not to be a bug or a regression from the parser change, but rather the honest performance of the current stack under sustained long-context generation. The assistant's diagnosis provides a clear roadmap for improvement: improve the drafter (long-term), expand the draft window (short-term), and address KV cache fragmentation (medium-term). This message exemplifies the kind of rigorous, data-driven thinking that separates genuine system understanding from superficial troubleshooting.