The Benchmark That Defines the Goal: Why 90 tok/s Is the Number to Beat

In the middle of an intense EAGLE-3 training pipeline spanning days of data generation, hidden state extraction, and iterative debugging, a simple user question cuts to the heart of the entire endeavor: "Remind me, what inference numbers were we getting in kimi k2.5 inference w/o eagle3?" The assistant's response in message 4292 is deceptively brief — a three-row table and two paragraphs — but it crystallizes the strategic rationale for the entire multi-day training effort. This message is the thesis statement of the project, the benchmark that defines success, and the cold mathematical reality that speculative decoding must overcome.

The Message in Full

The assistant responds with a compact benchmark summary:

From the benchmarks we ran:

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| Config | Single-stream tok/s | Notes | |--------|:------------------:|-------| | vLLM baseline | 82.5 | vLLM 0.16 nightly | | SGLang baseline | 90.0 | NCCL tuned + --num-continuous-decode-steps 4 | | SGLang + EAGLE3 (10K drafter) | 82.3 | accept_len ~2.1, 5 draft tokens — slower than baseline |

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The 90.0 tok/s SGLang baseline is the number to beat. The 10K EAGLE-3 drafter actually made things worse because with only accept_len ~2.1 (accepting ~1 of 5 draft tokens), the overhead of running the drafter + verification exceeded the savings from speculation.

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The goal with this 37K-sample, TTT=5 trained drafter is to get accept_len significantly higher (target ~3.0+), at which point speculation becomes net positive. With 10-16 speculative tokens per round on a PCIe system, even moderate acceptance rates should yield big wins since each accepted token saves a full 8-GPU allreduce round.

Why This Message Was Written: The Reasoning and Motivation

The immediate trigger is the user's request for a reminder. But the deeper motivation is far more significant. At this point in the conversation (message 4292), the assistant has just launched a major training run — a new EAGLE-3 draft model trained on 37,312 samples (87.8 million tokens) with TTT=5 (temporal training trick depth of 5), running across 4 GPUs with an estimated 10.8-hour duration. The training is proceeding well: 4,430 batches per epoch, 22,150 total steps, loss converging, GPU utilization at 100% with 350-400W power draw.

The user's question interrupts this long-running training process. It's a natural moment of reflection: Why are we doing this? What are we trying to achieve? The assistant's response serves multiple purposes simultaneously:

  1. It provides a factual answer to the user's direct question about baseline inference performance.
  2. It reframes the training effort in terms of a concrete, measurable goal: beating 90.0 tok/s.
  3. It diagnoses the failure of the previous attempt (the 10K drafter) with precise numbers.
  4. It articulates the theory of improvement — that higher accept_len from better training will flip the speculative decoding equation from net-negative to net-positive.
  5. It implicitly justifies the enormous effort of data generation, extraction, and training that has consumed the preceding segments. The message is, in essence, a strategic brief. It answers not just "what were the numbers" but "why does this training run matter and how will we know if it succeeded."

How Decisions Were Made

While this message doesn't contain any decisions per se, it reflects and communicates decisions made earlier in the session. The most important decision embedded here is the target setting: 90.0 tok/s is established as the bar. This wasn't an arbitrary choice — it emerged from systematic benchmarking that compared two inference engines (vLLM and SGLang) and found SGLang significantly faster with NCCL tuning and continuous decoding steps.

The decision to use SGLang as the inference engine for deployment is implicitly validated here. The 90.0 tok/s SGLang baseline outperforms vLLM's 82.5 tok/s by nearly 10%, making it the clear choice for the production system. This benchmark result informed the entire architecture of the deployment pipeline.

The decision to pursue EAGLE-3 speculation at all is also being re-evaluated through this lens. The 10K drafter's 82.3 tok/s result — worse than both baselines — is a sobering data point. It could have led to abandoning speculative decoding entirely. Instead, the assistant identifies the root cause (accept_len too low) and sets a concrete target (3.0+ accept_len) that would make speculation worthwhile. This is a rational, data-driven decision framework: don't abandon the approach, improve the underlying model until the math works.

Assumptions Made by the User and Agent

Several assumptions underpin this message, some explicit and some implicit:

Explicit assumption: The 90.0 tok/s SGLang baseline is reproducible. The assistant assumes that the benchmark conditions (NCCL tuning, --num-continuous-decode-steps 4) can be maintained in production. If server configuration drifts or load patterns change, the baseline might shift.

Explicit assumption: Higher accept_len will make speculation net positive. The assistant states that with accept_len ~3.0+, speculation becomes beneficial. This assumes that the overhead of running the drafter and verification scales sub-linearly with accept_len, and that the 10-16 speculative tokens per round is the right configuration. Both are reasonable but unverified for the new model.

Implicit assumption: The 37K-sample, TTT=5 training will produce a meaningfully better drafter than the 10K-sample, TTT=1 training. The entire training run is predicated on the belief that more data (3.7× more) and deeper temporal training (5 steps vs. 1) will significantly improve acceptance rates. This is a strong assumption — it's possible that the model architecture or data quality, not quantity, is the bottleneck.

Implicit assumption: PCIe communication cost is the dominant overhead. The message notes that "each accepted token saves a full 8-GPU allreduce round." This assumes that allreduce latency is the primary bottleneck in single-stream decoding. On a PCIe-connected multi-GPU system this is likely true, but the assumption deserves scrutiny — if compute (MoE expert evaluation) dominates, speculation might not help as much.

User's assumption: The baseline numbers are worth remembering. The user's question implies they see value in having these numbers top-of-mind during the training wait. This is a reasonable instinct — it keeps the goal visible during a long automated process.

Mistakes or Incorrect Assumptions

The most notable potential issue in this message is the optimism about the 37K drafter's acceptance rate. The assistant states "target ~3.0+" accept_len as the threshold for net-positive speculation. But the previous 10K drafter achieved only 2.1 accept_len with 5 draft tokens — meaning it accepted roughly 1 out of every 5 drafts. To reach 3.0+ accept_len with 10-16 speculative tokens, the model needs to accept a much higher fraction of drafts, or consistently accept longer runs.

The jump from 2.1 to 3.0+ represents a ~43% improvement in acceptance length. Is 3.7× more training data and TTT=5 sufficient for this? It's plausible but far from guaranteed. The assistant doesn't present any evidence that the new training regime will achieve this — it's a hypothesis to be tested.

Additionally, the message doesn't address diminishing returns. The 10K drafter was trained on 10,000 samples with TTT=1. The new one uses 37,312 samples with TTT=5. But the training data comes from the same source (synthetic responses generated by Kimi-K2.5 itself via OpenRouter). If the data distribution is similar, additional samples may provide diminishing information. The TTT=5 improvement is more promising, as it trains the drafter to handle multi-step interactions, but it also increases training complexity.

Another subtle issue: the message treats accept_len as the sole metric for speculation quality. In reality, the variance of acceptance length matters too. A drafter that sometimes accepts 8 tokens and sometimes 0 might have the same average accept_len as one that consistently accepts 3, but the latter produces much smoother and more predictable throughput. The assistant doesn't discuss this.

Input Knowledge Required to Understand This Message

To fully grasp this message, one needs:

  1. Understanding of speculative decoding: The concept of a "draft model" that proposes tokens and a "verifier" (the full model) that checks them. The key metric is "acceptance length" — how many draft tokens are accepted before a rejection triggers a resync.
  2. Knowledge of the EAGLE-3 architecture: EAGLE-3 is a speculative decoding framework that uses a lightweight transformer to predict hidden states of the base model, enabling draft token generation without running the full model. TTT (temporal training trick) refers to training the drafter on multi-step sequences so it learns to recover from its own errors.
  3. Context about the hardware: The system uses 8 RTX PRO 6000 Blackwell GPUs (SM120 architecture) connected via PCIe. This means allreduce communication between GPUs is a significant bottleneck — each decode step requires synchronizing gradients/activations across all 8 GPUs over PCIe, which is much slower than NVLink-connected systems.
  4. The previous 10K drafter's results: Earlier in the conversation (segment 26-27), the assistant built and tested a smaller EAGLE-3 drafter trained on 10,000 samples. It achieved zero acceptance rate initially due to a bug (wrong speculative algorithm flag), then after fixes reached 82.3 tok/s — still below baseline.
  5. The training pipeline context: The "37K-sample, TTT=5 trained drafter" refers to the training run launched in messages 4287-4290, using 37,312 hidden state samples extracted from the Kimi-K2.5 model, with batch_size=8 packing, max_seq_len=8192, running on 4 GPUs with an estimated 10.8-hour duration.

Output Knowledge Created by This Message

This message creates and communicates several pieces of actionable knowledge:

  1. A clear benchmark hierarchy: SGLang (90.0) > vLLM (82.5) > SGLang+EAGLE3-10K (82.3). This hierarchy guides deployment decisions — use SGLang for baseline serving, and only add speculation if it can beat 90.0.
  2. A quantified failure analysis: The 10K drafter's 82.3 tok/s is explained in terms of accept_len ~2.1 with 5 draft tokens. This is a precise diagnosis that points to a specific remedy (higher accept_len).
  3. A success criterion: The new drafter needs accept_len ≥ 3.0 to make speculation worthwhile. This is a concrete, testable metric that can be evaluated immediately after training completes.
  4. A strategic narrative: The message connects the dots between the training effort (which might otherwise feel like a detached optimization exercise) and the real-world performance goal. It answers the question "why are we spending 10.8 hours on this?"
  5. A communication of confidence: The assistant's tone is measured and analytical, not promotional. It acknowledges that the previous attempt failed and explains why the new approach might succeed. This builds trust through intellectual honesty.

The Thinking Process Visible in the Reasoning

The assistant's reasoning, though compressed into a few sentences, reveals a sophisticated mental model:

First, it recognizes the user's question as an opportunity for strategic alignment. Rather than just reciting numbers, it contextualizes them within the ongoing work.

Second, it performs a cost-benefit analysis of speculative decoding in real time. The calculation is implicit but clear:

Conclusion

Message 4292 is a masterclass in concise technical communication. In a few lines, it recaps past benchmarks, diagnoses a failure, sets a success criterion, justifies ongoing work, and aligns the user and assistant on shared goals. It transforms a simple reminder request into a strategic touchstone for the entire EAGLE-3 project. The 90.0 tok/s SGLang baseline is no longer just a number — it's the mountain to climb, and the 37K-sample drafter is the expedition being launched to summit it.