The Long Tail of Hidden State Extraction: A Progress Check at 8,080 of 10,000

In the middle of a marathon machine learning pipeline, a single progress-check message can reveal as much about the process as any benchmark. Message [msg 2974] captures one such moment: the assistant checks on a hidden state extraction job that has been running for hours, confirms it is 8,080 of 10,000 samples complete, and extracts a wealth of performance data from the log output. This seemingly mundane status update is actually a rich document of assumptions, measurements, and the quiet tension between what a log reports and what the filesystem shows.

Context: The EAGLE-3 Training Pipeline

To understand this message, one must understand the larger mission. The assistant has been building an EAGLE-3 speculative decoding system for the Kimi-K2.5 model—a 1-trillion-parameter Mixture-of-Experts architecture running on 8× RTX PRO 6000 Blackwell GPUs. Speculative decoding accelerates inference by using a small "draft" model to predict multiple tokens, which the large model then verifies in parallel. EAGLE-3 is a particularly sophisticated form of speculation that extracts hidden states from intermediate layers of the large model and trains a lightweight transformer to predict future hidden states.

The pipeline consists of several steps: generating synthetic training data by having the model answer 10,000 prompts, extracting hidden states from those generations, building vocabulary mappings between the target and draft vocabularies, and finally training the draft model. Message [msg 2974] falls at the tail end of Step 2—hidden state extraction—which is the most computationally expensive phase before training.

What the Message Actually Says

The message is deceptively simple. The assistant writes:

8,080 of 10,000 done! Nearly there. About 10 more minutes:

It then executes a bash command that sleeps 600 seconds (10 minutes) and checks the remote machine:

ssh root@10.1.230.174 'for d in /data/eagle3/synth_10k/hidden_states/rows_*/; do echo "$d: $(ls "$d" | wc -l) files"; done; echo "---"; du -sh /data/eagle3/synth_10k/hidden_states/; echo "---"; tail -20 /data/eagle3/synth_10k/extraction.log | grep -v "Worker_TP" | grep -v "flashinfer" | grep -v "Loading safe"'

The output reveals five shard directories:

Batch 8968-8976: 3.7s, Total: 8976 samples, 15505408 tokens, 1.8 samples/s, 3161 tok/s, ~237.8 GB on disk

The Discrepancy That Tells a Story

The most interesting feature of this message is the discrepancy between two numbers: the log reports "~237.8 GB on disk" at 8,976 samples, but the du -sh command reports 764 GB. These are not measuring the same thing at the same time—and understanding why reveals the hidden complexity of long-running distributed jobs.

The extraction script writes progress lines to the log as each batch completes. The line shown—"Batch 8968-8976"—was written when batch 8968-8976 finished. At that moment, the script's internal counter reported 237.8 GB. But by the time the assistant's du -sh command ran, the actual disk usage had grown to 764 GB. This suggests that the script's disk usage counter is either stale, computed differently, or simply wrong.

Looking at the historical data from earlier checks provides clarity. In [msg 2970], 344 samples consumed 28 GB (~83 MB/sample). In [msg 2971], 1,480 samples consumed 123 GB (~83 MB/sample). In [msg 2972], 3,656 samples consumed 304 GB (~83 MB/sample). In [msg 2973], 5,848 samples consumed 486 GB (~83 MB/sample). The pattern is consistent: approximately 83 MB per sample. At 8,976 samples, the expected disk usage is 8,976 × 83 MB ≈ 745 GB—very close to the 764 GB reported by du -sh.

The log's "237.8 GB" figure is therefore almost certainly stale or erroneous. It may be a counter that was initialized at some point during extraction and never updated, or it may reflect a different measurement (perhaps only counting one of the four hidden state layers). The assistant does not comment on this discrepancy, suggesting either that it did not notice the mismatch or that it implicitly trusts the filesystem measurement over the log counter.## The Performance Data: What 3,161 tok/s Means

The log line reveals a critical performance metric: the extraction is running at 3,161 tokens per second across 8 GPUs with tensor parallelism. This is a prefill-only workload—the model processes each sequence in a single forward pass without autoregressive generation—so it represents the raw throughput of the model's attention and MoE computation on Blackwell GPUs.

To put 3,161 tok/s in perspective: the Kimi-K2.5 model has 1 trillion parameters, though in INT4 quantization with MoE sparsity, only a fraction of parameters are active per token. The extraction script processes batches of 8 sequences with a maximum sequence length of 4,096 tokens. At 3,161 tok/s, each batch of 8 sequences (average ~2,000 tokens each) takes about 5 seconds. The log shows individual batches taking 3.7 seconds, which is consistent with the overall average.

This throughput is important because it directly determines how long the pipeline takes. The total dataset has 21,033,536 tokens across 10,000 samples. At 3,161 tok/s, the extraction phase alone requires about 111 minutes of pure computation, plus 24.5 minutes for model loading. The assistant's earlier estimates were remarkably accurate: it predicted ~3.8 hours total, and the actual time was very close.

The Assumptions Embedded in This Message

Every progress check carries implicit assumptions, and this message is no exception. The assistant assumes that the extraction process will continue running without crashing, that the log output is reliable (even though the disk usage counter is clearly stale), and that the 10-minute wait is sufficient to see completion. It also assumes that the SSH connection will remain stable and that the remote machine's filesystem operations (ls, du) will not interfere with the extraction process.

More subtly, the assistant assumes that the file count in each shard directory is a reliable proxy for progress. This is reasonable—the extraction script writes one file per sample—but it depends on the script writing files atomically and not leaving partial outputs. The assistant also assumes that the rows_8000-10000 directory will fill to exactly 2,000 files, matching the other shards, which implies a consistent batch size throughout the run.

The Reasoning Behind the Timing

The assistant's decision to sleep 600 seconds before checking is itself a reasoning artifact. Earlier checks used shorter intervals (30 seconds, 2 minutes, 5 minutes) during model loading, then longer intervals (10 minutes, 20 minutes) once extraction was underway. The assistant has learned the extraction rate empirically: from [msg 2971] through [msg 2973], it observed that ~2,000 samples take about 20 minutes. With 1,920 samples remaining (10,000 − 8,080), 10 minutes is a reasonable estimate.

This adaptive polling strategy is characteristic of long-running ML workflows. The assistant is not just checking for completion—it is building a mental model of the process's behavior, refining its estimates, and adjusting its monitoring cadence accordingly. The "About 10 more minutes" prediction is not a guess; it is a calculation based on observed throughput.

What This Message Creates

This message produces several kinds of output knowledge. First, it confirms that the extraction pipeline is stable and performant: zero errors, consistent throughput, no GPU memory issues. Second, it establishes the disk usage profile (83 MB/sample, ~828 GB total), which is critical for planning storage for future runs. Third, it validates the assistant's earlier estimates and demonstrates that the pipeline is on track to complete within the expected timeframe.

For anyone following the conversation, this message also serves as a checkpoint. It says, in effect, "the hard part is almost done, and it worked." After hours of debugging, patching, and fixing API incompatibilities (documented in [msg 2950] through [msg 2969]), the extraction is running smoothly. The message is a quiet celebration—not of a breakthrough, but of a plan executed correctly.

Conclusion

Message [msg 2974] is a progress report that contains far more information than its author likely intended. The file counts, disk usage, and log line together tell a story of a pipeline running at 3,161 tok/s, consuming 83 MB per sample, and producing 828 GB of training data for the EAGLE-3 drafter. The discrepancy between the log's disk counter and the actual filesystem usage is a reminder that even well-instrumented distributed systems have blind spots. And the assistant's adaptive polling strategy reveals a thinking process that is constantly measuring, estimating, and recalibrating—the quiet engine behind every successful long-running ML job.