The 924-Gigabyte Question: Disk Space Assessment in the EAGLE-3 Training Pipeline

In the middle of a sprawling machine learning pipeline spanning local GPU inference, OpenRouter API calls, dataset merging, and hidden state extraction, there comes a moment when the entire operation hinges on a single practical question: do we have enough disk space? Message 4085 in this opencode session is that moment — a seemingly mundane du -sh command that, in context, represents a critical decision point where months of prior work meet the hard physical constraints of a cloud VM's storage.

The message itself is deceptively simple. The assistant executes a single bash command on a remote server, checking disk usage across several key directories:

ssh root@10.1.230.174 'du -sh /data/eagle3/synth_10k_sglang/hidden_states/ 2>/dev/null; echo "---"; du -sh /data/eagle3/synth_100k/prepared/*/tokenized_data.jsonl 2>/dev/null; echo "---"; du -sh /data/eagle3/synth_100k/prepared/A*/tokenized_data.jsonl 2>/dev/null; echo "---"; wc -l /data/eagle3/synth_100k/prepared/*/tokenized_data.jsonl 2>/dev/null'

The output reveals a stark asymmetry: the old 10K-sample hidden states consume 924 gigabytes, while the new tokenized datasets — all 40,114 samples across ten directories — amount to barely over a gigabyte combined. This single number, 924G, is the fulcrum on which the next phase of the entire pipeline turns.

The Strategic Context: Why This Message Exists

To understand why this message was written, we must trace the chain of decisions that led to it. The session had just completed a major pivot: after weeks of struggling with local GPU inference for EAGLE-3 training data generation — battling SGLang hangs, hidden state concatenation bugs, and throughput limitations — the assistant had successfully switched to OpenRouter API for generating responses across eight B-datasets (B3 through B8). In just 33 minutes and at a cost of $86, the OpenRouter pipeline produced over 90 million tokens of training data, completing the data generation phase (see [msg 4080]).

The user's instruction in [msg 4082] set the next agenda: "Continue next stages, merge (prompt/think/resp/tool), shuffle, tokenize and perform next steps; Assess how much space is needed for hidden state extraction - might need VM data disk to be expanded. Possibly remove old 10k hidden state extraction to free 1TB."

This instruction contains two competing impulses. The user wants to push forward with the pipeline — merge, shuffle, tokenize — but also recognizes a potential bottleneck: hidden state extraction is the most storage-intensive phase of the entire EAGLE-3 training process. Hidden states are the intermediate layer activations from the base model that the EAGLE-3 drafter learns to predict. For a 32-layer, 7168-dimension model like Kimi-K2.5, each sample's hidden states are enormous — storing them as float16 tensors requires 7168 × 32 × 2 bytes = ~459KB per token position. With sequences averaging thousands of tokens, a single sample can produce gigabytes of hidden state data.

The old 10K hidden states sitting at 924GB represent a previous training run that produced a drafter with zero acceptance rate (debugged extensively in segments 26-27). That drafter was a failure, but the hidden states from that run still occupy nearly a terabyte of precious disk space. The question is: can we safely delete them to make room for the new, much larger extraction?

What the Numbers Reveal

The command output provides a detailed inventory of the current state of the data pipeline. Let us examine each piece of information the assistant gathered:

The old hidden states: 924 gigabytes in /data/eagle3/synth_10k_sglang/hidden_states/. This is the elephant in the room — nearly a full terabyte dedicated to a failed experiment. These hidden states were extracted during the earlier 10K-sample run that produced a drafter with zero acceptance rate. They are now effectively orphaned data, valuable only as a cautionary artifact.

The tokenized dataset sizes: Each of the eight B-datasets and two A-datasets has a tokenized_data.jsonl file. Their sizes range from 22MB (A2_kimik25, 2,000 samples) to 376MB (A1_deepswekimi, 2,800 samples). The B-datasets cluster around 70-142MB each. These are compact because they store only token IDs — integers representing the prompt, reasoning, response, and tool call sequences — not the full hidden state tensors.

The sample counts: The wc -l output (partially visible in the truncated command) confirms the dataset composition:

A1_deepswekimi: 2,800 samples (but 376MB — these are ultra-long, averaging ~16K tokens per sample)
A2_kimik25: 2,000 samples
B1_glaive: 9,998 samples
B2_opencodeinstruct: 2,932 samples
B3_magicoder: 3,383 samples
B4_mixturethoughts: 1,891 samples
B5_openthoughts: 2,112 samples
B6_ultrachat: 5,957 samples
B7_sharegpt: 5,476 samples
B8_sweagent: 3,565 samples Total: approximately 40,114 samples. This is a 4× increase over the old 10K run.

The Hidden Decision Calculus

This message does not make a decision — it gathers the data needed to make one. But the reasoning that will follow is already implicit in the numbers being collected. The assistant is performing a classic engineering triage: measuring the constraint before planning the execution.

The critical insight is that hidden state extraction scales roughly as samples × sequence_length × layers × hidden_dim. The A1_deepswekimi dataset, with its 2,800 ultra-long samples averaging 16K tokens each, dominates the token budget at 44.9 million tokens — nearly a third of the entire 138.4M token corpus. If hidden states are extracted at full sequence length, A1 alone could produce over 20 terabytes of data. The chunk summary for this segment notes that capping sequence length at 8192 and potentially dropping A1 would reduce extraction from ~5.5TB/91h to ~3.5TB/72h.

The 924GB figure for the old hidden states provides a useful benchmark. That extraction was for 10K samples with presumably shorter sequences. The new extraction will cover 40K samples with significantly longer sequences (especially A1). Even with aggressive sequence length capping, the new hidden states will likely be 3-5× larger than the old ones — meaning 3-5 terabytes.

This is why the assistant is checking disk space now, before starting the merge and shuffle. The 12TB /data volume has 11TB available (as shown in [msg 4084]), which is sufficient for the extraction. But the 924GB of old hidden states is occupying space that could be reclaimed. The decision to delete them is not purely technical — it is also psychological. Deleting 924GB of data from a failed experiment is an admission that weeks of debugging, training, and optimization produced nothing usable. It is a costly sunk cost, and the assistant is carefully quantifying it before the user makes that call.

Assumptions Embedded in the Query

The command makes several assumptions that are worth examining:

Assumption 1: The old hidden states are safe to delete. The assistant does not verify whether any downstream process still depends on the old 10K hidden states. The failed drafter has been superseded, but the hidden states themselves could theoretically be reused for a different training approach. The assistant implicitly assumes they are disposable.

Assumption 2: The tokenized_data.jsonl files are the authoritative source for sample counts. This is reasonable — these files were produced by the tokenization pipeline and should contain exactly one JSON object per line. But the assistant does not cross-validate against the raw response files or the prompt sources.

Assumption 3: Disk space is the primary constraint. The assistant focuses exclusively on storage, not on compute time, memory bandwidth, or GPU hours. This is appropriate given the user's explicit concern about disk expansion, but it means other constraints (like the 91-hour extraction time estimate) are not surfaced in this message.

Assumption 4: The hidden_states/ directory contains only the old 10K extraction. The command checks a single path. If there are other hidden state artifacts elsewhere on the filesystem, they are not accounted for.

Input Knowledge Required

To fully understand this message, one needs:

Knowledge of the EAGLE-3 architecture: Hidden states are the intermediate layer outputs of the base model (Kimi-K2.5) that the EAGLE-3 drafter is trained to predict. They are the "target" for the drafter's prediction task. Without this context, the 924GB figure is just a large number — with it, it represents the tangible cost of failed experimentation.
Knowledge of the dataset taxonomy: The A-datasets (A1_deepswekimi, A2_kimik25) are pre-existing tokenized datasets, while the B-datasets (B1-B8) were just generated via OpenRouter. The distinction matters because A1's ultra-long samples create disproportionate storage demands.
Knowledge of the pipeline phases: The assistant is at the transition between Phase 2 (data generation) and Phase 3 (merge, shuffle, hidden state extraction). The disk space assessment is a prerequisite for Phase 3 planning.
Knowledge of the hardware constraints: The VM has a 12TB data volume (as shown in the previous message's df -h output), with 11TB free. The old hidden states consume 924GB of the 1TB already used.

Output Knowledge Created

This message produces several concrete pieces of knowledge that feed directly into subsequent decisions:

The old hidden states are exactly 924GB, confirming the user's estimate of "~1TB." This makes the deletion decision straightforward: removing them frees nearly a terabyte.
The tokenized datasets are small — the largest (A1) is 376MB, and the B-datasets average ~85MB each. The merge and shuffle phase will not be storage-constrained.
The sample counts are verified — 40,114 total samples across all datasets. This confirms the pipeline produced the expected volume of training data.
A1 dominates the token budget — 2,800 samples producing 376MB of tokenized data (vs. 9,998 B1 samples producing 142MB). This reinforces the earlier analysis that A1's ultra-long sequences are the primary cost driver for hidden state extraction.

The Thinking Process Visible in the Message

While the message itself contains only a command and its output, the thinking process is visible in the structure of the query. The assistant chose to:

Check the old hidden states first (du -sh .../hidden_states/), signaling that the deletion question is the priority.
Separate A-dataset and B-dataset queries (prepared/*/ vs prepared/A*/), even though the glob pattern would have captured both. This suggests the assistant wanted to distinguish the pre-existing A datasets from the newly generated B datasets.
Include line counts (wc -l), which provides sample counts that the size data alone cannot reveal. A 376MB file could be 2,800 long samples or 28,000 short ones — the line count disambiguates.
Run the command remotely via SSH rather than locally, confirming that the data lives on a separate VM (the inference server at 10.1.230.174). The assistant is thinking like a systems engineer: measure before modifying, quantify before deciding, and present the data in a structure that makes the decision obvious. The 924GB figure, juxtaposed against the ~1GB of tokenized data, makes the case for deletion without needing to argue it explicitly.

Conclusion: A Message That Does Nothing and Everything

Message 4085 performs no action that changes the state of the world. It deletes no files, starts no processes, and trains no models. Yet it is one of the most consequential messages in this segment because it transforms an abstract concern — "might need VM data disk to be expanded" — into concrete numbers that drive real decisions.

The 924GB of old hidden states will be deleted, freeing space for the new extraction. The merge-and-shuffle script will proceed knowing it has ample room. The hidden state extraction will be planned with accurate knowledge of the dataset sizes and composition. All of this flows from a single du -sh command — a reminder that in complex engineering pipelines, the most valuable tool is often not the one that builds, but the one that measures.