The Last Inventory: Cataloguing 4.6 Terabytes of EAGLE-3 Training Data Before a Volume Retires

Introduction

In the life of any machine learning infrastructure project, there comes a moment when the focus shifts from building and optimizing to cleaning up and migrating. Message 6088 captures exactly such a moment. After dozens of rounds spent tuning CUDA configurations, patching SGLang for Blackwell SM120 support, wrestling with flash-attn compilation, and benchmarking speculative decoding throughput across eight RTX PRO 6000 GPUs, the assistant is asked a simple operational question: what is on /data, and can it be retired?

The user's query in [msg 6085] is brief and practical: "On the CT look if we're using anything in /data - the volume will be retired soon to cold backup, unmounted." This is infrastructure housekeeping — a storage volume is being decommissioned, and before it goes, someone needs to verify that nothing critical is still actively using it. The assistant's response in message 6088 is the deep dive into the largest consumer on that volume: the /data/eagle3/ directory, which earlier checks revealed was consuming 4.8 terabytes of the roughly 5 terabytes on the volume.

The Message

The assistant executes a single bash command, drilling into the EAGLE-3 working directory:

ssh root@10.1.230.174 'du -sh /data/eagle3/*/ 2>/dev/null; ls /data/eagle3/'

The output reveals a sprawling experimental workspace:

2.3G	/data/eagle3/aq-medai-k2-drafter/
2.3G	/data/eagle3/aq_medai_k2/
45G	/data/eagle3/output_100k_sglang/
45G	/data/eagle3/output_10k/
49G	/data/eagle3/output_10k_sglang/
15G	/data/eagle3/output_finetune_aqmedai/
4.6T	/data/eagle3/synth_100k/
95M	/data/eagle3/synth_10k/
7.4M	/data/eagle3/synth_10k_sglang/
04_train.py
aq-medai-k2-drafter
aq_medai_k2
download.log
output_100k_sglang
output_10k
output_10k_sglang
output_finetune_aqmedai
sglang_eagle3_aqmedai.log
sglang_eagle3_cg_2step.log
sglang_eagle3_cg.log
sglang_eagle3_fp4.log
sglang_eagle3_mlp.log
sglang_eagle3_mlp_2step.log
sglang_eagle3_mlp_2step_2gpu.log
sglang_eagle3_mlp_2step_4gpu.log
sglang_eagle3_mlp_2step_8gpu.log
sglang_eagle3_mlp_4gpu.log
sglang_eagle3_mlp_8gpu.log
sglang_eagle3_mlp_8gpu_2step.log
sglang_eagle3_mlp_8gpu_2step_baseline.log
sglang_eagle3_mlp_8gpu_2step_bench.log

Context and Motivation

To understand why this message exists, one must trace back through the preceding conversation. The assistant had just completed a major infrastructure reconfiguration: splitting the 8 Blackwell GPUs between an LXC container (running SGLang with TP=4 on NUMA 0 GPUs) and a VM (using NUMA 1 GPUs via vfio-pci passthrough). A systemd service was created to persist this split across reboots. The SGLang server was updated, restarted, and verified to be serving the Qwen3.5-397B-A17B-NVFP4 model correctly with 471,474 tokens of KV cache capacity.

Then the user interjected with the operational question about /data. This is a classic shift in conversation: from the "what" (how do we configure the system) to the "what's actually here" (inventory and cleanup). The assistant's response in [msg 6086] first did a top-level listing, revealing that /data contained a CUDA installer, the eagle3 directory (4.8T), and a models directory (223G) holding the Qwen3.5-397B model. Message 6087 then broke down the sizes, confirming the eagle3 directory was the dominant consumer at 4.8T.

Message 6088 is the logical next step: having identified the largest directory, the assistant drills into it to understand what those terabytes actually contain. This is not just curiosity — it's a practical necessity. When a volume is being retired to cold backup, someone needs to know what's on it to make decisions about what to preserve, what can be regenerated, and what is safe to delete.

What the Data Reveals About the Project

The contents of /data/eagle3/ tell a rich story about the machine learning research that preceded this infrastructure work. The directory is a time capsule of the EAGLE-3 speculative decoding experiments that were the focus of earlier segments (segments 35-37).

The dominant consumer is synth_100k/ at 4.6 terabytes. This is synthetic training data — 100,000 generated examples used to train the EAGLE-3 drafter model. The presence of smaller synth_10k/ (95M) and synth_10k_sglang/ (7.4M) directories suggests an iterative approach: starting with small-scale synthetic data generation (10K examples) to validate the pipeline, then scaling up to the full 100K dataset. The 4.6T size indicates these are likely full conversation traces, possibly including KV cache states or activation values, not just tokenized text.

The drafter model checkpoints appear in two forms: aq-medai-k2-drafter/ and aq_medai_k2/ (both 2.3G). The naming suggests these are quantized (AQ = Activation-aware Quantization?) versions of a "MedAI K2" drafter — likely a domain-specific model fine-tuned for medical AI applications, given the "medai" prefix. The 2.3G size is consistent with a compressed or quantized model checkpoint.

The output directories (output_100k_sglang/, output_10k/, output_10k_sglang/, output_finetune_aqmedai/) contain the results of running the drafter against various benchmarks and configurations. These range from 15G to 49G each, suggesting detailed logs, generated tokens, and evaluation metrics.

The log files are particularly revealing. There are 14 log files documenting different experimental configurations: sglang_eagle3_cg.log (constrained generation?), sglang_eagle3_fp4.log (FP4 precision), sglang_eagle3_mlp.log (MLP-based drafter), and variants with different step counts (2step) and GPU counts (2gpu, 4gpu, 8gpu). This is a systematic exploration of the design space for speculative decoding — testing different drafter architectures, different numbers of speculative steps, and different degrees of tensor parallelism. The log files alone, while small in size, represent weeks of experimentation and tuning.

Assumptions and Decisions

The assistant makes several implicit assumptions in this message. First, it assumes that the user wants a complete inventory of the directory, not just a summary. The command combines du -sh (disk usage summary for subdirectories) with ls (full listing of all files), providing both aggregate sizes and individual file names. This is a reasonable interpretation of "look if we're using anything" — the assistant errs on the side of providing more information rather than less.

Second, the assistant assumes that the SSH connection to the LXC container (10.1.230.174) is still working and that the directory structure hasn't changed since the previous checks. This is a safe assumption given that the previous commands in [msg 6086] and [msg 6087] succeeded moments earlier.

Third, the assistant assumes that the user has the context to understand what these directories represent. The naming conventions (synth_100k, output_finetune_aqmedai, sglang_eagle3_mlp_2step_8gpu.log) are meaningful within the project's experimental framework but would be opaque to an outsider. The assistant does not add explanatory commentary — it simply presents the raw listing, trusting that the user (who has been involved in or at least aware of the EAGLE-3 experiments) can interpret it.

One potential limitation: the du -sh command is redirected to /dev/null for stderr, meaning any errors (e.g., permission-denied directories) would be silently suppressed. This is a reasonable choice for a quick inventory but means the assistant might miss directories that are inaccessible. In practice, this is unlikely to be an issue since the container runs as root.

Input Knowledge Required

To fully understand this message, one needs:

1. The project context: Knowledge that this system has been running EAGLE-3 speculative decoding experiments on Qwen3.5-397B using SGLang across multiple Blackwell GPUs. Without this context, the directory names are just cryptic strings.
2. The conversation history: Understanding that the user asked about /data being retired, and that the assistant has been methodically inventorying the volume across messages 6086-6088.
3. Technical knowledge of ML experimentation workflows: Recognizing that synth_* directories contain synthetic training data, that output_* directories contain experimental results, and that the log files represent systematic benchmarking across different configurations.
4. Knowledge of the infrastructure: Understanding that 10.1.230.174 is the LXC container's IP address, that the assistant has SSH access, and that the container is running on the Proxmox host kpro6.

Output Knowledge Created

This message creates a detailed inventory of the EAGLE-3 experimental workspace. The key findings are:

• The synth_100k/ directory at 4.6T is the dominant data consumer — this is the synthetic training dataset for the EAGLE-3 drafter. If this needs to be preserved, it represents the bulk of the backup cost.
• The drafter model checkpoints are relatively small (2.3G each) and could easily be preserved or regenerated.
• The experimental outputs and logs (totaling roughly 150G across all output directories) document the full experimental history and may be valuable for reproducibility even if the raw data is archived.
• The directory contains 14 benchmarking log files documenting a systematic exploration of the speculative decoding design space across different architectures (MLP, CG), step counts (1-step, 2-step), and GPU counts (2, 4, 8). This information directly serves the user's practical need: deciding what to do before the volume is retired. The 4.6T synth_100k/ directory is the critical item — it's too large to casually migrate but may be too valuable to discard if the EAGLE-3 experiments are ongoing or need to be reproduced.

The Thinking Process

While the message itself contains no explicit reasoning block, the thinking process is visible in the structure of the command. The assistant is following a systematic inventory protocol:

1. Top-down decomposition: First check the top-level contents of /data/ (msg 6086), then identify the largest directory (eagle3 at 4.8T), then drill into it (msg 6088). This is classic divide-and-conquer for filesystem exploration.
2. Combining summary and detail: The command pairs du -sh (aggregate sizes) with ls (individual file listing). This provides both the forest and the trees — the user can see which directories dominate the storage footprint while also seeing the individual files that might be important.
3. Silent error handling: The 2>/dev/null on du -sh suppresses errors for directories that might fail (e.g., permission issues or broken symlinks). This keeps the output clean but risks missing information — a deliberate tradeoff in favor of readability. The assistant does not pre-filter or interpret the results. It presents the raw output, trusting the user to extract meaning. This is appropriate for an inventory task where the user's goals (what to keep, what to discard) are not yet fully specified.

Conclusion

Message 6088 is a small but revealing moment in a much larger conversation. On its surface, it is a simple filesystem exploration — a bash command and its output. But in context, it represents the transition from active development to operational maintenance. The EAGLE-3 experiments that consumed weeks of effort — the training data generation, the model fine-tuning, the systematic benchmarking across architectures and GPU counts — are now reduced to a directory listing awaiting a decision about what to keep and what to let go.

The 4.6 terabytes of synth_100k/ tell a story of scale: generating 100,000 synthetic examples for drafter training required enormous storage. The 14 log files tell a story of systematic exploration: MLP vs CG drafters, 1-step vs 2-step speculation, 2-GPU vs 8-GPU parallelism. And the 2.3G checkpoints tell a story of the target domain: medical AI ("medai"), likely fine-tuned for clinical applications.

When the volume is unmounted and sent to cold backup, this directory listing will serve as the record of what was there — the last inventory before the data goes offline.