The Fourth Attempt: Finding the Memory Limit in EAGLE-3 Training

A Message in the Iteration Loop

On the surface, message [msg 4274] appears to be one of the most mundane actions in any machine learning engineer's workflow: relaunch a training job with a different hyperparameter. The assistant runs a single bash command over SSH, clearing the output directory and starting a torchrun invocation with --max-seq-len 16384. But this message is far from ordinary — it is the fourth attempt in a rapid-fire iteration loop to find the maximum sequence length that fits within GPU memory, a process that reveals deep assumptions about memory scaling, attention complexity, and the practical realities of training large draft models for speculative decoding.

The message, quoted exactly, reads:

[assistant] [bash] ssh -o ConnectTimeout=10 root@10.1.230.174 'rm -rf /data/eagle3/output_100k_sglang/*; nohup ~/ml-env/bin/torchrun --nproc_per_node=4 /tmp/04_train.py --verifier-path /shared/kimi-k2.5-int4 --data-dir /data/eagle3/synth_100k/hidden_states --vocab-mapping-dir /data/eagle3/synth_10k_sglang/vocab_mapping --output-dir /data/eagle3/output_100k_sglang --epochs 5 --lr 3e-5 --max-seq-len 16384 --num-workers 4 --ttt-steps 5 --noise-std 0.05 --scheduler cosine --warmup-ratio 0.01 --val-ratio 0.05 > /data/eagle3/synth_100k/logs/train_4gpu_ttt5.log 2>&1 &'
echo "Training launched with max-seq-len=16384"
Training launched with max-seq-len=16384

To understand why this message exists, one must trace the frantic sequence of events that preceded it.

The Context: A Chain of Kill-and-Restart Cycles

The story begins with the user's observation at [msg 4251] that the GPUs were drawing only 250W out of a 600W power budget — a clear sign of underutilization despite showing 100% GPU-Util. The assistant investigated and discovered that the EAGLE-3 model's forward method expects a batch dimension of exactly 1 (shape [1, total_seq_len, ...]), meaning the conventional approach of increasing batch_size was impossible. The only lever available was max_seq_len, which controls how many tokens are packed into each batch via the collate function's sequence packing mechanism.

The assistant's first attempt at [msg 4261] raised max_seq_len from 4096 to 8192, which improved power draw to 412-476W but still left 35,446 batches per epoch — each batch packing roughly one sample since many training sequences were themselves close to 8192 tokens. The user correctly pointed this out at [msg 4263]: "Max seq 8k sounds like too small for many of train seqs btw."

The assistant then made an aggressive leap. At [msg 4266], it launched with max_seq_len=32768, expecting to pack 4-8 samples per batch and dramatically reduce steps per epoch. The user reported back at [msg 4268]: "OOMed, 24k?" — the model had run out of memory, and the user suggested trying 24576 as the next candidate.

The assistant complied at [msg 4270], launching with max_seq_len=24576. Again, the user reported OOM at [msg 4272]: "OOM again, try 16." The assistant killed the processes at [msg 4273] and, at [msg 4274], launched the fourth attempt with max_seq_len=16384.

The Reasoning: Why This Binary Search Was Necessary

The assistant's thinking process reveals a fundamental tension in training large models: the desire to maximize GPU utilization versus the hard constraint of VRAM. The assistant had observed that at max_seq_len=8192, the GPUs used 39.8 GB of 96 GB available — only 41% utilization. With 59 GB of headroom, the assistant reasoned that a larger sequence length would pack more samples per batch, increasing compute per step and thus GPU utilization.

The reasoning was sound in principle but flawed in its memory scaling model. The assistant assumed roughly linear VRAM scaling with sequence length: 40 GB at 8K tokens, so 32K tokens might use ~160 GB — too much, but 24K might use ~120 GB, still too much, and 16K might use ~80 GB, which would fit. However, this linear approximation ignored the quadratic nature of attention memory. The attention mechanism's memory footprint scales as O(n²) with sequence length, meaning the jump from 8K to 16K is a 4× increase in attention memory, not 2×. Combined with the TTT=5 training which requires storing intermediate activations for 5 autoregressive steps, the actual memory scaling was far steeper than the assistant anticipated.

Assumptions Made and Their Consequences

The assistant made several critical assumptions in this message and the preceding ones. First, it assumed that VRAM was the only constraint — that if the model fit in memory, it would run correctly. This was reasonable given that the OOM errors were indeed VRAM-related, but it overlooked the possibility of other resource limits like CPU memory for data loading or NCCL buffer allocations.

Second, the assistant assumed that clearing the output directory (rm -rf /data/eagle3/output_100k_sglang/*) was necessary between restarts. This reflects an assumption that the training script cannot resume from a partially-written checkpoint, or that stale checkpoints might cause issues. In practice, this assumption was conservative but safe — it ensured each run started from a clean state.

Third, the assistant assumed that the user's guidance ("try 16") was authoritative and should be followed without further analysis. This is a reasonable assumption in a collaborative coding session where the user has demonstrated domain expertise, but it meant the assistant did not independently verify whether 16384 would fit before launching.

Fourth, and most significantly, the assistant assumed that the memory scaling from sequence packing was well-understood. The assistant's earlier analysis at [msg 4261] stated: "The attention is O(n²) but with flex_attention and block-diagonal masking it's actually O(sum of individual seq lengths²), so it scales well with packing." This analysis was correct about the attention mechanism's asymptotic behavior, but it failed to account for the total memory footprint, which includes not just attention but also the hidden state projections, the MLP layers, and — critically — the gradient memory during backward pass, which effectively doubles or triples the memory requirement for trainable parameters.

Input and Output Knowledge

To understand this message, the reader needs significant background knowledge. One must understand the EAGLE-3 architecture — a speculative decoding framework where a small "draft" model predicts multiple tokens ahead that a larger "verifier" model then validates. The --ttt-steps 5 parameter controls how many autoregressive steps the drafter is trained to predict, with each step adding a full forward and backward pass through the model. One must understand sequence packing — the technique of concatenating multiple training samples into a single sequence with block-diagonal attention masks, allowing efficient GPU utilization without increasing batch size. One must understand the constraints of the speculators library, which hardcodes batch dimension to 1 in its model forward method. And one must understand the practical workflow of remote GPU training: SSH into a head node, use pct exec to access the container, kill stale processes, clear GPU memory with fuser, and launch training with nohup so it survives shell exit.

The output knowledge created by this message is significant. It establishes that max_seq_len=16384 is the highest value that fits in 96 GB of VRAM across 4 RTX PRO 6000 Blackwell GPUs for this particular training configuration. This becomes the final working configuration for the 100K-sample EAGLE-3 training run, as confirmed by the chunk summary which reports successful completion with this setting. The message also implicitly documents the memory ceiling through its failure modes: 32768 and 24576 both OOM, establishing a practical upper bound that future training runs can reference.

The Thinking Process Visible in the Reasoning

What is most striking about this message is what it does not contain. There is no analysis, no explanation, no reasoning — just a command and an echo. The thinking has already happened in the preceding messages, and by this point the assistant is operating in pure execution mode. The user said "try 16" and the assistant did exactly that, without questioning, without recalculating, without proposing alternatives.

This reveals a shift in the collaboration dynamic. Earlier in the iteration, the assistant was proactive — it analyzed the model architecture, read the source code, calculated memory budgets, and proposed specific values. But after two consecutive OOM failures (32768 and 24576), the initiative shifted to the user, who now had a better intuitive sense of the memory boundary. The assistant's role contracted from analyst to executor, faithfully translating the user's instruction into a precise command invocation.

The message also reveals the assistant's understanding of operational hygiene. Each relaunch begins with rm -rf /data/eagle3/output_100k_sglang/* to clear stale output, ensuring no confusion between runs. The command uses nohup and backgrounding (&) to survive shell exit, redirects stdout and stderr to a log file, and specifies ConnectTimeout=10 for robustness. These are the marks of an operator who has learned from experience that training jobs on remote infrastructure need to be resilient to network interruptions.

Conclusion

Message [msg 4274] is, on its surface, a simple command to launch a training job. But it is better understood as the fourth data point in an empirical search for a memory boundary — a search that began with optimistic assumptions about linear scaling, crashed against the quadratic reality of attention mechanisms, and converged through rapid user-guided iteration to a working configuration. The message captures the moment when the assistant stops analyzing and starts executing, trusting the user's intuition over its own calculations. It is a testament to the messy, iterative, and deeply empirical nature of training large models at the frontier of GPU memory constraints.