The 828-Gigabyte Trade-Off: Storage Calculus in the EAGLE-3 Training Pipeline

In the sprawling, multi-day effort to deploy speculative decoding for the Kimi-K2.5 language model, few decisions are as starkly practical as the one captured in message [msg 3389]. The assistant, deep into building an EAGLE-3 drafter network, pauses to confront a mundane but critical constraint: disk space. The message reads in full:

1.9 TB available, we need ~1.2 TB for 10K samples. That's tight but doable. Let me delete the old vLLM-extracted hidden states to make room:

>

[bash] ssh root@10.1.230.174 "du -sh /data/eagle3/synth_10k/hidden_states/" 828G /data/eagle3/synth_10k/hidden_states/

This is a message about resource management, about the physical limits of a machine running at the edge of its capacity. It is also a message that reveals the assistant's reasoning process in real time: a quick mental calculation, a judgment call, and an immediate action to verify the premise before committing to a destructive operation. The assistant does not yet delete anything in this message—it first checks the size of the old data, a prudent step before running rm -rf on 828 gigabytes of irreplaceable training artifacts.

The Storage Dilemma

To understand why this message exists, one must trace the arc of the preceding conversation. The assistant has been iterating on an EAGLE-3 training pipeline for Kimi-K2.5 across multiple sessions (segments 20–25 of the conversation). The core idea of EAGLE-3 is to train a lightweight "drafter" network that predicts hidden states at future token positions, enabling speculative decoding—generating multiple tokens per forward pass rather than one at a time.

The drafter must be trained on actual hidden states extracted from the base model during inference. Earlier in the session, the assistant had already extracted 828 GB of hidden states using vLLM (the previous serving framework). But those extractions were tied to an older pipeline, and the assistant had since pivoted to SGLang for multiple reasons: SGLang loaded the model in 22 seconds versus vLLM's much longer startup, and the previous EAGLE-3 drafter trained on vLLM extractions had achieved only a ~15% acceptance rate—far too low to be useful.

A new extraction run was underway using SGLang, and the assistant had just calculated the expected storage requirements. In [msg 3387], the assistant ran a quick analysis of the 10,000 tokenized training samples:

Total samples: 10000
Avg len: 2103
Min/Max: 99/10648
Median: 1267
Total tokens: 21,033,536
Estimated HS size: 1206 GB (4 layers, bf16)

The calculation is straightforward: 21 million tokens × 7168 hidden dimension × 2 bytes (bf16) × 4 layers (three auxiliary layers at positions [3, 31, 59] plus the final layer) equals approximately 1.2 terabytes. This is the raw material needed to train the new drafter from scratch.

Then in [msg 3388], the assistant checked available disk space:

Filesystem      Size  Used Avail Use% Mounted on
/dev/rbd0       2.9T  875G  1.9T  32% /data

The numbers do not lie: 1.9 TB available, 1.2 TB needed. The math works—barely. But the 828 GB of old vLLM-extracted states sitting in /data/eagle3/synth_10k/hidden_states/ represents both a safety net and a liability. Keeping it means living with 700 GB of headroom (1.9 − 1.2 = 0.7 TB) on a machine that also needs space for model weights, temporary files, and the training scripts themselves. Deleting it frees up 828 GB, yielding a comfortable 1.5 TB of free space after extraction.

The Decision-Making Process

The assistant's reasoning, visible in the compressed language of the message, follows a clear chain:

  1. Assess the requirement: "we need ~1.2 TB for 10K samples" — this is the non-negotiable demand of the new extraction run.
  2. Evaluate the buffer: "That's tight but doable" — 1.9 TB minus 1.2 TB leaves 700 GB, which is enough to operate but leaves little margin for error, temporary files, or unexpected growth.
  3. Identify the reclaimable resource: "Let me delete the old vLLM-extracted hidden states" — the 828 GB of old data is the obvious candidate for deletion.
  4. Verify before acting: The assistant runs du -sh to confirm the size before issuing any delete command. This last step is crucial. The assistant does not assume the size—it measures it. The output confirms 828 GB, matching the earlier estimate from [msg 3388] where df -h showed 875 GB used total (828 GB of hidden states plus ~47 GB of other data). The verification gives the assistant confidence to proceed with deletion in the next message.

Assumptions and Implicit Knowledge

Several assumptions underpin this decision, and it is worth examining them critically:

The old hidden states are truly obsolete. The assistant assumes that the vLLM-extracted hidden states will never be needed again. This is a reasonable assumption given the pivot to SGLang: the new extraction uses a different serving framework, different capture methodology (server-side patching of the model forward pass vs. client-side extraction), and is part of a retraining effort "from scratch" (not finetuning from the old AQ-MedAI checkpoint). However, it is an irreversible decision—once deleted, those 828 GB of data are gone. If the SGLang extraction proves flawed (e.g., incorrect layer alignment, missing positions due to radix cache interactions), the old data would be a fallback.

The new extraction will succeed. The assistant has tested the SGLang hidden state dump mechanism in [msg 3380] and confirmed it works: 13 input tokens produced 13 hidden state vectors at each of the 4 capture layers. But that was a single test. Scaling to 10,000 samples with 21 million tokens introduces failure modes that a single test cannot cover: server crashes mid-extraction, disk filling up, corrupted output files, or subtle bugs in the dump counter synchronization.

Disk space is the binding constraint. The assistant treats storage as the primary resource to manage, but there are other constraints: the SGLang server must remain running for the duration of the extraction (hours), the CPU must keep up with the request rate, and the network filesystem (RBD—RADOS Block Device) must handle the write throughput of ~1.2 TB of data. These constraints are not discussed in this message, though they may surface later.

The extraction format is compatible with the training pipeline. The assistant assumes that the new SGLang-extracted hidden states, saved as binary .pt files in the "speculators v1 format," will work with the training script 04_train.py that was rewritten in segment 22. This is a reasonable assumption since the format was designed for this purpose, but any mismatch would require re-extraction.

Input Knowledge Required

To fully understand this message, a reader needs:

  1. The storage arithmetic: 1.9 TB available − 1.2 TB needed = 700 TB buffer, plus 828 GB reclaimable = 1.5 TB comfortable margin. The assistant performs this mental calculation implicitly.
  2. The pipeline context: That the old hidden states were extracted using vLLM as part of a previous EAGLE-3 training attempt (segments 22–23), and that the new extraction uses SGLang with a server-side patch (Approach C developed earlier in segment 25). The old data is from a different framework and a different drafter training run.
  3. The data format: That each hidden state is a bf16 tensor of shape [num_tokens, 7168] for each of 4 layers (3 auxiliary + 1 final), and that the extraction saves these as individual .pt files per request.
  4. The hardware topology: That the machine has 8 RTX PRO 6000 Blackwell GPUs with ~96 GB of VRAM each, and that /data is a 2.9 TB RBD volume (a Ceph RADOS block device, indicating a distributed storage backend).
  5. The risk calculus: That deleting 828 GB of data is irreversible but necessary to make room for the new extraction, and that the assistant has already validated the new extraction mechanism on a small test.

Output Knowledge Created

This message produces several tangible and intangible outputs:

A verified size measurement: The du -sh command confirms that the old hidden states occupy exactly 828 GB. This is not an estimate—it is a precise measurement of disk usage, including any filesystem overhead.

A decision recorded in the conversation log: The assistant's reasoning is captured for future reference. If something goes wrong, a human reviewing the conversation can see why the old data was deleted and what assumptions were made.

A precondition for the next action: The message sets up the deletion that follows in subsequent messages. By verifying the size first, the assistant ensures that the delete command targets the correct directory and that the freed space will be sufficient.

A demonstration of resource-conscious engineering: The message shows the assistant thinking about physical constraints—disk space, data sizes, and trade-offs—rather than just code correctness. This is a hallmark of production-grade ML engineering.

The Thinking Process

The assistant's thinking is compressed but visible. The phrase "That's tight but doable" reveals a rapid mental simulation: 1.9 TB minus 1.2 TB equals 0.7 TB, which is enough for operating system overhead, temporary files, and the training scripts, but not by a wide margin. The word "tight" acknowledges the risk.

The immediate pivot to "Let me delete the old vLLM-extracted hidden states" shows that the assistant has already identified the solution before typing the message. The size check is not exploratory—it is confirmatory. The assistant already knows the old data is large; it just needs the exact number to be sure.

The choice to run du -sh rather than rm -rf directly is significant. It reflects a design principle: verify before destroying. In the context of an automated assistant that has already made mistakes (the 400 errors with prompt_token_ids in [msg 3373], the radix cache issue in [msg 3380]), this caution is well-justified.

Conclusion

Message [msg 3389] is a small but revealing moment in a complex engineering effort. It is the point where the assistant transitions from planning to execution, from "what if" to "let's do it." The storage calculus—1.2 TB needed, 1.9 TB available, 828 GB reclaimable—is simple arithmetic, but the decision to delete the old data carries weight. It represents a commitment to the new SGLang-based pipeline and an acknowledgment that the old vLLM pipeline is definitively behind them.

In the broader narrative of the conversation, this message marks the clearing of the decks. The old data is clutter; the new extraction is the future. By freeing up space, the assistant ensures that the 10,000-sample extraction can proceed without storage errors, and that the subsequent training of the new EAGLE-3 drafter has room to breathe. It is not the most technically complex message in the conversation—no code is written, no algorithms are designed—but it is one of the most consequential, because without enough disk space, none of the rest matters.