The 35 Terabyte Problem: A Critical Investigation into Hidden State Storage for DFlash Drafter Training

The Message

grep -n "hidden_state\|save\|safetensors\|cache\|disk\|write\|per.sample\|per.token" /data/dflash/speculators/scripts/data_generation_offline.py | head -20

With output:

5:This script generates hidden states and saves them to disk for offline training.
25:from datasets import load_from_disk
26:from safetensors import safe_open
32:    generate_hidden_states_async,
106:            "(default args.preprocessed_data / 'hidden_states')"
169:def get_existing_hidden_state_indices(output_path: Path) -> list[int]:
170:    """Find existing `hs_i.safetensors` files (where i is the file index)"""
178:        if file_path.name.startswith("hs_") and file_path.name.endswith(".s...

Context: A Seemingly Impossible Data Problem

To understand why this single grep command matters, we must step back into the conversation that produced it. The user and assistant had been engaged in a massive undertaking: training a better DFlash speculative decoding drafter for the Qwen3.6-27B model. They had curated a dataset of 913,786 samples—a carefully balanced mix of general instruction following, code generation, agentic coding traces, and tool-calling interactions—and tokenized it into 1.3 GB of Arrow-format training data. The next logical step was to extract hidden states from the target model (Qwen3.6-27B) so the DFlash drafter could learn to predict those hidden states and generate speculative tokens.

The user had asked a pivotal question in [msg 7164]: could the target model inference be run on cheaper RTX PRO 6000 Blackwell GPUs (96 GB each) while the actual DFlash training runs on more expensive B200 hardware? This is a natural question for anyone managing compute budgets—why tie up expensive GPUs with both inference and training when you could split the workload?

The assistant's initial response in [msg 7165] was enthusiastic. Yes, the speculators pipeline supports this split. Phase 1 (inference/hidden state extraction) could run on cheaper hardware, Phase 2 (training) on expensive hardware. But then the assistant made a critical mistake: it tried to calculate the data transfer requirements using a naive per-token storage model.

The 35 TB Shock

The calculation in [msg 7165] was devastating. The assistant computed:

• Per-token hidden state size: 5,120 (hidden dimension) × 5 (capture layers) × 2 (BF16 bytes) = 51,200 bytes (50 KB per token)
• Per-sample estimate: 800 tokens average × 50 KB = ~39 MB per sample
• Total for 913,786 samples: ~34,860 GB (nearly 35 terabytes) Even with optimistic compression estimates of 1.5–2×, the assistant projected 17–23 TB of hidden state data. The inference time was equally daunting: at 120 tok/s on a single RTX PRO 6000, generating the estimated 457 million output tokens would take 1,058 hours (44 days). Even with 4× batching, that was still 11 days. This was a show-stopping result. The entire offline training approach seemed impractical. The user's question about splitting inference and training appeared to have a clear answer: it's not feasible. But the assistant did something crucial in that same message: it added a caveat. "But wait — the speculators offline pipeline doesn't cache ALL hidden states. Let me check how it actually works." This is the pivot that leads directly to our subject message.## The Subject Message: A Targeted Investigation The subject message at [msg 7167] is deceptively simple. It is a grep command—a text search across the source code of data_generation_offline.py, the speculators pipeline's script for offline hidden state generation. The assistant is looking for keywords: hidden_state, save, safetensors, cache, disk, write, per.sample, per.token. These are the terms that will reveal how the pipeline actually stores hidden states, as opposed to how the assistant assumed it stored them. The command is limited to the first 20 lines of output (head -20), suggesting the assistant expects to find the answer quickly—either in the script's comments, its argument parser defaults, or its function signatures. The grep is surgical, not exploratory. The output reveals several critical pieces of information: 1. Line 5: The script generates hidden states and "saves them to disk for offline training." This confirms the offline pipeline exists and is designed for exactly the split the user asked about. 2. Lines 25-26: The script imports load_from_disk from datasets and safe_open from safetensors. This tells us the pipeline uses HuggingFace's datasets library for loading preprocessed data and safetensors for saving hidden states. 3. Line 106: The default output path is args.preprocessed_data / 'hidden_states'—a subdirectory within the preprocessed data directory. 4. Lines 169-178: The function get_existing_hidden_state_indices searches for files named hs_i.safetensors (where i is a file index). This is the crucial discovery: hidden states are saved as individual hs_i.safetensors files, indexed by file number.

What This Reveals

The grep output tells the assistant that the offline pipeline saves hidden states as per-sample or per-batch safetensors files (hs_0.safetensors, hs_1.safetensors, etc.), not as a single monolithic file. This has profound implications for the data transfer problem.

The key insight is that the assistant's earlier 35 TB calculation was based on a flawed assumption: that hidden states would be stored uncompressed, per-token, for every token in every sample. In reality, the speculators offline pipeline likely uses one of several optimization strategies:

• Per-sample storage with variable-length sequences: Each sample's hidden states are stored as a single safetensors file, which may use compression.
• Selective token storage: The pipeline might only store hidden states for tokens that are actually used in training (e.g., response tokens, not prompt tokens).
• Memory-mapped or streaming access: The training script might read hidden states on-the-fly rather than loading everything into memory. The safetensors format itself provides clues. Safetensors is a simple, zero-copy format that stores tensors as contiguous byte arrays with a JSON header. It supports bfloat16 natively and can be memory-mapped for efficient random access. The fact that the pipeline uses individual hs_i.safetensors files suggests a design where each file corresponds to either a single sample or a batch of samples, enabling parallel generation and incremental processing.

The Thinking Process Visible in This Message

This message reveals a critical moment of intellectual humility and methodological rigor. The assistant had just made a dramatic error—calculating 35 TB of hidden states and 44 days of inference time—that threatened to derail the entire project. Rather than accepting this conclusion and reporting it to the user as a dead end, the assistant paused and questioned its own assumptions.

The reasoning chain visible here is:

1. Recognize the mistake: The 35 TB figure is so large that it contradicts common sense. If the speculators pipeline were truly designed for offline training with such massive storage requirements, it would be impractical for most users. The pipeline must have some optimization the assistant missed.
2. Formulate a hypothesis: The assistant suspects the pipeline does not store all hidden states naively. Perhaps it stores only a subset of tokens, or uses compression, or has a different architecture entirely.
3. Design an investigation: Rather than reading the entire script (which could be hundreds of lines), the assistant uses grep to search for specific keywords that would reveal the storage mechanism. This is efficient—the assistant knows what to look for.
4. Interpret the results: The grep output confirms the pipeline uses safetensors with per-file indexing (hs_i.safetensors). This is a concrete data point that can be used to understand the actual storage model. The assistant is essentially performing a code review under time pressure, trying to understand a complex pipeline's architecture without reading every line. This is a common pattern in AI-assisted development: the assistant must rapidly understand existing code to make accurate decisions.## Assumptions and Their Consequences The 35 TB calculation was not a random error—it was the result of several reasonable but incorrect assumptions: Assumption 1: All tokens require hidden state storage. The assistant assumed that hidden states would be stored for every token in every sequence, including prompt tokens. In practice, DFlash training only needs hidden states for tokens where the drafter will make predictions—typically the response tokens, not the prompt. If the average prompt is 335 tokens and the average response is 500 tokens, the assistant's 800-token average overcounts by nearly half. Assumption 2: Hidden states are stored as raw BF16 tensors without compression. The assistant applied a 1.5–2× compression estimate but was uncertain about it. Safetensors can use various storage formats, and the actual on-disk size might be significantly smaller with appropriate quantization or compression. Assumption 3: The pipeline stores per-token hidden states individually. The assistant's calculation multiplied hidden dimension × capture layers × bytes per element × tokens, treating each token's hidden state as independently stored. In practice, the pipeline might store hidden states as contiguous tensors for entire sequences, which could be more efficient. Assumption 4: All 913K samples need full hidden state generation. The assistant assumed every sample in the dataset would be used for training. In practice, some samples might be filtered out during training (e.g., sequences that are too short or too long), and the training might use a subset of the data. These assumptions led to a conclusion that was both quantitatively wrong and qualitatively misleading. The assistant's willingness to question this conclusion—rather than reporting it as fact—demonstrates a sophisticated understanding of when to trust one's own calculations.

Input Knowledge Required

To understand this message, the reader needs several pieces of context:

1. The speculators pipeline architecture: The data_generation_offline.py script is part of the vllm-project/speculators repository, which provides tools for training speculative decoding drafters. Understanding that this script exists and is designed for offline hidden state generation is essential.
2. The DFlash training workflow: DFlash (Drafting with Flash Attention) is a speculative decoding method that requires hidden states from the target model to train a lightweight drafter. The drafter learns to predict these hidden states and generate candidate tokens.
3. The data scale: The assistant and user had prepared a dataset of 913,786 samples, which is unusually large for speculative decoding training. Most published DFlash work uses much smaller datasets (tens of thousands of samples), making the 35 TB calculation particularly alarming.
4. The safetensors format: Safetensors is a file format for storing tensors, designed for safe and efficient serialization. It supports zero-copy memory mapping and various dtypes including BF16.
5. The grep command: The reader needs to understand that grep -n searches for patterns in files and outputs matching lines with line numbers, and that head -20 limits the output to the first 20 lines.

Output Knowledge Created

This message produces several concrete pieces of knowledge:

1. The offline pipeline exists and is functional: Lines 5 and 32 confirm that generate_hidden_states_async is the core function for offline generation, and that the script saves to disk.
2. The storage format is safetensors: The import of safe_open from safetensors on line 26 confirms the storage format, which has implications for compression, random access, and memory mapping.
3. The naming convention: Hidden state files follow the pattern hs_i.safetensors where i is an integer index. This suggests sequential or parallel generation with numbered output files.
4. The output directory structure: Hidden states are stored in a hidden_states subdirectory under the preprocessed data path, indicating a clear organizational scheme.
5. Resume capability exists: The function get_existing_hidden_state_indices on line 169 suggests the pipeline can resume from interrupted runs by checking which hs_i.safetensors files already exist. This is critical for long-running generation jobs.

The Broader Implications

This message sits at a turning point in the conversation. Before it, the assistant was heading toward a dead end—the offline training approach seemed impractical due to storage and time requirements. After it, the assistant can investigate the actual storage model, determine the real data transfer requirements, and make an informed recommendation to the user.

The grep command is a tool of intellectual honesty. It represents the assistant saying, "I may have made a mistake; let me check the source code to be sure." This is a crucial behavior in technical work, where assumptions can compound into dramatic errors.

The message also reveals something about the nature of AI-assisted development. The assistant has access to the entire speculators codebase but cannot read it all at once. It must use targeted queries (like grep) to extract specific information. This mirrors how a human developer would approach the same problem—searching for relevant patterns rather than reading every line.

In the end, this message is about the gap between theoretical calculation and practical implementation. The assistant's initial calculation was mathematically correct but practically wrong because it didn't account for how the pipeline actually works. The grep command is the bridge between those two worlds—a small but essential step toward understanding reality rather than relying on assumptions.