The Moment of Truth: Launching the AQ-MedAI K2 Drafter Probe

Introduction

In any engineering project, there comes a moment when preparation ends and experimentation begins — when all the checks, fixes, and analyses give way to the simple act of running the system and seeing what happens. Message [msg 4951] captures exactly such a moment in a complex speculative decoding optimization session. After multiple rounds of meticulous preparation — killing stale processes, examining safetensors, comparing vocab mappings, fixing configuration files, and tracing SGLang's weight-loading code — the assistant finally launches the SGLang server with the AQ-MedAI K2 EAGLE-3 drafter paired against the Kimi-K2.5 target model. This single message, comprising just two parallel bash commands and a brief declarative sentence, represents the culmination of Phase 0 of a structured game plan and the beginning of an empirical investigation that would ultimately reshape the entire optimization strategy.

The Context: A Structured Game Plan

To understand why this message was written, one must trace back through the preceding conversation. The assistant and user had been engaged in a prolonged effort to improve inference throughput for the Kimi-K2.5 language model using speculative decoding with EAGLE-3. After training a from-scratch EAGLE-3 drafter on 100K samples that achieved 74.7% validation accuracy (<msg id=4940 context>), the team was exploring whether a pre-existing drafter — the AQ-MedAI K2 EAGLE-3 model trained on 1.4M samples with an impressive accept_len of 3.2–3.5 — could be adapted to work with K2.5 more efficiently than training from scratch.

The game plan, documented in eagle-k2finetune-game-plan.md and referenced in [msg 4941], outlined a multi-phase approach. Phase 0 was a "Quick Probe": simply try the AQ-MedAI K2 drafter directly with the K2.5 base model to measure how similar the hidden state representations were between the two model versions. If the drafter worked well out of the box, it would save weeks of training. If not, the results would inform the fine-tuning strategy.

Messages [msg 4942] through [msg 4950] executed the preparatory steps of Phase 0: killing existing server processes, examining the AQ-MedAI drafter directory structure, comparing the d2t (draft-to-target) vocab mappings between the AQ-MedAI and the team's own drafter, fixing the max_position_embeddings config value from 131072 to 262144, and verifying that SGLang could load the vocab mappings directly from the safetensors file. By the end of [msg 4950], the todo list showed Phase 0.1 (kill processes) and Phase 0.2 (prepare drafter directory) as completed, with Phase 0.3 (launch SGLang) still pending.

The Message: What Actually Happened

Message [msg 4951] contains exactly two tool calls dispatched in parallel. The first is a lengthy bash command that launches the SGLang inference server with a carefully tuned set of parameters:

EAGLE3_PROFILE=1 SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 nohup /root/ml-env/bin/python3 -m sglang.launch_server \
  --model-path /shared/kimi-k2.5-int4 \
  --trust-remote-code \
  --tp-size 8 \
  --mem-fraction-static 0.88 \
  --host 0.0.0.0 --port 8000 \
  --num-continuous-decode-steps 4 \
  --disable-custom-all-reduce \
  --speculative-algorithm EAGLE3 \
  --speculative-draft-model-path /data/eagle3/aq_medai_k2 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 3 \
  --speculative-num-steps 2

The second command copies a benchmark script to the remote machine:

scp /home/theuser/glm-kimi-sm120-rtx6000bw/eagle3-train/benchmark_eagle3.py root@10.1.230.174:/tmp/benchmark_eagle3.py

The server launch is executed with nohup and backgrounded, meaning it runs asynchronously. The assistant does not wait for the server to be ready before proceeding — it simply initiates the launch and moves on. The echo &#34;Server launch initiated&#34; confirms that the SSH command was dispatched, not that the server is actually running. The benchmark script copy ensures that once the server is up, performance measurements can begin immediately.

Decisions Embedded in the Launch Command

Every flag in that server launch command represents a prior decision, many of which were hard-won through earlier debugging sessions:

--speculative-algorithm EAGLE3: This selects the EAGLE-3 speculative decoding algorithm, which uses a lightweight draft model to propose multiple token candidates that the target model then verifies in parallel. This is the core technique being optimized throughout the entire session.

--speculative-draft-model-path /data/eagle3/aq_medai_k2: This points to the AQ-MedAI K2 drafter, the subject of the probe. The path was verified in [msg 4943] to contain config.json and model.safetensors (2.38 GB).

--speculative-eagle-topk 1: This limits the draft model to sampling only the single most likely token at each position, rather than considering multiple candidates. This is a conservative setting that reduces the tree size and simplifies verification.

--speculative-num-draft-tokens 3 and --speculative-num-steps 2: These configure the draft generation to produce 3 tokens per step over 2 steps, for a total of 6 draft tokens per speculation cycle. This mirrors the configuration used with the from-scratch drafter.

--tp-size 8: Tensor parallelism across all 8 GPUs. The machine has 8 RTX PRO 6000 Blackwell GPUs, and this setting distributes the model across all of them.

--disable-custom-all-reduce: This flag disables SGLang's custom all-reduce implementation. Earlier in the session (see <msg id=4930s context>), the team had identified that NCCL all-reduce was a major bottleneck in the verify step, consuming ~25ms of the ~30ms cycle time. Disabling custom all-reduce was part of an ongoing investigation into communication overhead.

--num-continuous-decode-steps 4: This configures the number of continuous decoding steps, a performance optimization that batches multiple decode operations.

EAGLE3_PROFILE=1: This environment variable enables profiling instrumentation in the EAGLE-3 worker code, which would be essential for diagnosing performance bottlenecks later.

SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1: This allows the server to override the context length, which was necessary because the AQ-MedAI config originally had max_position_embeddings set to 131072 (K2's value) and had to be updated to 262144 (K2.5's value) in [msg 4949].

Assumptions Underlying the Launch

The message rests on several key assumptions, some of which would prove incorrect:

Architectural compatibility: The assistant assumes that because both K2 and K2.5 share the same DeepSeek V3 / MLA architecture with identical hidden_size (7168), intermediate_size (18432), and layer structure, the AQ-MedAI drafter's weights would be structurally compatible with the K2.5 base model. This assumption was validated by the config comparison in [msg 4944], which showed identical architectural parameters.

Vocab mapping correctness: The assistant correctly determined that AQ-MedAI's own d2t mapping should be used for the probe, since the drafter's lm_head was trained to predict token positions within that specific mapping. However, the deeper assumption — that the K2 vocab distribution (which determined which 32,000 tokens were selected for the draft vocabulary) would overlap sufficiently with K2.5's distribution — was untested. The comparison in [msg 4948] showed that 31,748 out of 32,000 d2t values differed between the two drafters, which should have been a stronger warning sign.

Hidden state alignment: The most critical assumption was that the hidden states produced by the K2.5 base model at layers 2, 30, and 58 (the auxiliary hidden state layers specified in eagle_config) would be similar enough to K2's hidden states that the drafter's fc projection layer (which maps 3×7168 = 21504 hidden dimensions down to 7168) would produce meaningful inputs. This assumption would be tested empirically by the probe results.

Server stability: The assistant assumed the server would start successfully with the given parameters. Given the complexity of the SGLang stack and the many compatibility issues encountered earlier (flash-attn rebuilds, CUDA version mismatches, NCCL tuning), this was not guaranteed.

Input Knowledge Required

To fully understand this message, one needs:

  1. EAGLE-3 speculative decoding architecture: Understanding that a lightweight draft model proposes tokens that a target model verifies, and that the draft model takes hidden states from intermediate layers of the target model as input.
  2. SGLang server configuration: Knowledge of the launch parameters, including tensor parallelism (--tp-size), memory management (--mem-fraction-static), and speculative decoding flags.
  3. d2t/t2d vocab mappings: Understanding that EAGLE-3 uses a reduced vocabulary of 32,000 tokens (vs. the full 163,584+ token vocabulary of the base model), and that d2t maps draft token IDs back to target token IDs. The mapping is stored as deltas (offsets) in the safetensors file.
  4. The hardware topology: 8 RTX PRO 6000 Blackwell GPUs connected via PCIe, which makes all-reduce communication a significant bottleneck.
  5. The project history: The earlier struggles with flash-attn compilation, CUDA toolkit versions, NCCL tuning, and the from-scratch EAGLE-3 training that established the baseline performance metrics.

Output Knowledge Created

This message produces several forms of knowledge:

Immediate outputs: A running SGLang server process on the remote machine, a log file at /data/eagle3/synth_100k/logs/sglang_eagle3_aqmedai_probe.log, and a benchmark script at /tmp/benchmark_eagle3.py on the container.

Empirical data (to be generated): Once the server is running and the benchmark script is executed, the team would learn the actual accept_len and tokens-per-second achieved by the K2 drafter with K2.5. According to the chunk summary, this would yield an accept_len of approximately 1.5 and 52 tok/s — significantly worse than both the baseline (82 tok/s without speculation) and the from-scratch EAGLE-3 drafter (60 tok/s).

Diagnostic signal: The poor performance would confirm that the K2 drafter's hidden state representations are misaligned with K2.5, justifying the decision to attempt fine-tuning (Phase 1) and ultimately leading to the discovery of the vocab mapping mismatch that made fine-tuning ineffective.

The Thinking Process

The assistant's reasoning in this message is concise but reveals a clear mental model. The phrase "Config is fixed" references the completed work of [msg 4949], where max_position_embeddings was updated. The description "K2.5 base + AQ-MedAI K2 drafter (2-step EAGLE-3)" shows the assistant thinking in terms of the system architecture — two components being combined.

The decision to copy the benchmark script in parallel with the server launch reveals strategic thinking: the assistant anticipates that the server will take some time to start (loading an 8-GPU model is not instantaneous), and having the benchmark script ready means measurements can begin as soon as the server is available. This is a small optimization, but it reflects an awareness of the overall workflow.

The choice to background the server process with nohup and redirect output to a log file shows an understanding that this is a long-running process that should persist independently of the SSH session. The log file location (/data/eagle3/synth_100k/logs/sglang_eagle3_aqmedai_probe.log) follows the convention established earlier in the project.

Conclusion

Message [msg 4951] is deceptively simple — two bash commands and a short sentence — but it represents a critical transition point in a complex optimization effort. It is the moment when theory meets practice, when all the preparatory analysis is put to the test. The decisions encoded in the launch parameters reflect weeks of accumulated knowledge about the hardware, the software stack, and the EAGLE-3 algorithm. The assumptions embedded in those decisions would soon be tested by empirical results, leading to a fundamental shift in strategy from data-centric improvements (more training data, fine-tuning) to system-level communication optimization. In the story of this engineering session, this message is the turning point where the team commits to a path and prepares to learn from the outcome.