The Batch Size Revelation: Unlocking GPU Utilization Through DataLoader Packing

In the high-stakes world of training speculative decoding draft models, every percentage point of GPU utilization matters. When training the EAGLE-3 drafter for Kimi-K2.5 on a cluster of RTX PRO 6000 Blackwell GPUs, the assistant found itself in a puzzling situation: despite steadily increasing the maximum sequence length from 4096 to 12288 tokens, the number of training batches per epoch remained stubbornly fixed at 35,446. The GPUs were drawing only 320–400W out of a possible 600W, and VRAM usage sat at a modest 50 GB out of 96 GB available. Something was fundamentally wrong with how the training pipeline was packing data.

The message at [msg 4284] captures the moment of resolution: a single edit to the training script that would transform GPU utilization by fixing a subtle but critical misunderstanding about how PyTorch's DataLoader and collate function interact.

The Context: A Long Trail of Optimization Attempts

The journey to this edit began several messages earlier. The assistant had launched EAGLE-3 training on 100K synthetic samples with batch_size=1 and max_seq_len=4096, observing only 250W GPU power draw — barely 40% utilization. The user flagged this immediately: "Also was seeing only 250/600W GPU power use, can we bump batch or sth to get better util?" ([msg 4251]).

The assistant's initial response was to increase max_seq_len, reasoning that longer packed sequences would mean more compute per training step. The model used a packing collate function that concatenated multiple samples into a single sequence of length max_seq_len, with block-diagonal attention masking keeping individual samples independent. At 4096 tokens, each batch contained roughly one sample. At 8192, maybe two. At 32768, perhaps eight. The logic seemed sound.

But reality intervened. At max_seq_len=32768, the training OOM'd. At 24576, it OOM'd again. At 16384, it crashed with a Triton shared memory error — the compiled RMSNorm kernel required 163,912 bytes of shared memory, but the SM120 architecture only provides 101,376 bytes per block. This was a Triton autotuning limitation on Blackwell GPUs, not a VRAM issue. The assistant backed off to 12288, which ran successfully — but the batch count per epoch was identical to the 8192 run: 35,446.

The Moment of Insight

This is where the assistant's debugging took a crucial turn. In [msg 4282], the assistant re-read the collate function and had a realization:

"Wait — the batch count is the same (35,446) because the DataLoader has batch_size=1 and yields one file at a time. The collate function packs multiple files into one sequence, but with batch_size=1 in the DataLoader, it only gets one file per call. The packing only kicks in with batch_size > 1 in the DataLoader."

This was the key insight. The collate function — create_collate_fn(max_len) — receives a list of samples from the DataLoader. It concatenates all of them along the sequence dimension and then slices/pads to max_len. But with batch_size=1, that list always contains exactly one sample. The max_seq_len parameter was irrelevant for packing because there was never more than one sample to pack.

The assistant confirmed this by reading the collate function source code ([msg 4282]):

def collate_fn(batch: list[BatchType]) -> BatchType:
    collated_data = {}
    for key in batch[0]:
        # Concatenate the tensors along the seq (0th) dimension
        collated_data[key] = torch.cat([b[key] for b in batch], dim=0)

The function iterates over batch (the list of samples) and concatenates them. With batch_size=1, len(batch) == 1, so torch.cat is a no-op. The packing mechanism was structurally disabled.

The Edit: Adding a Proper Batch Size

In [msg 4283], the assistant killed the running training processes and announced: "Let me update the script to pass a proper batch_size." Then in [msg 4284], the subject message, the edit was applied.

The message itself is deceptively brief:

[assistant] [edit] /home/theuser/glm-kimi-sm120-rtx6000bw/eagle3-train/04_train.py
Edit applied successfully.

LSP errors detected in this file, please fix:
<diagnostics file="/home/theuser/glm-kimi-sm120-rtx6000bw/eagle3-train/04_train.py">
ERROR [33:8] Import "torch" could not be resolved
ERROR [34:6] Import "transformers" could not be resolved
ERROR [48:6] Import "speculators.models.eagle3.config" could not be resolved
ERROR [49:6] Import "speculators.models.eagle3.core" could not be resolved
ERROR [50:6] Import "speculators.config" could not be resolved
ERROR [51:6] Import...

The edit added a --batch-size argument to the training script's argument parser and passed it to the DataLoader constructor. Previously, the DataLoader was hardcoded with batch_size=1. Now it would accept a configurable value, allowing the collate function to receive multiple samples per call and pack them into the max_seq_len window.

The LSP errors shown are entirely expected — they're import resolution failures from the language server running on the remote machine, where torch, transformers, and speculators are installed in a virtual environment that the LSP cannot resolve. These are not runtime errors and would not affect training.

Why This Matters: The Packing Economics

The implications of this fix are substantial. With batch_size=8 and max_seq_len=8192, the collate function would receive up to 8 samples and concatenate them into a single 8192-token sequence. If each sample averages ~2000 tokens, that's 4 samples per batch — a 4× reduction in the number of training steps per epoch. The GPU spends more time on actual computation and less time on framework overhead, data loading, and gradient synchronization.

More importantly, the GPU utilization would increase. At 50 GB VRAM and 320-400W, the GPUs were loafing. With proper packing, the assistant could push VRAM usage toward 80-90 GB and power draw toward 500-600W, extracting the full computational capacity of the RTX PRO 6000 Blackwell GPUs.

The fix also resolved a subtle architectural tension. Earlier, the assistant had determined that the model's forward method expected batch dimension = 1 (shapes like [1, total_seq_len, hidden_size]). Increasing the DataLoader's batch_size would normally produce tensors with shape [batch_size, ...], which would break the model. But the collate function concatenates along the sequence dimension (dim=0), not the batch dimension — it produces [total_seq_len, ...] tensors regardless of how many samples were in the input list. The packing and the model's shape expectations were compatible all along.

The Broader Lesson

This episode illustrates a classic pitfall in ML engineering: the assumption that a parameter controls what you think it controls. The assistant had been tuning max_seq_len as a proxy for "how much work per step," but the actual lever was the DataLoader's batch_size, which controlled how many samples the collate function could pack. The max_seq_len parameter only constrained the output of packing, not the input — it was a ceiling, not a multiplier.

The debugging process also demonstrates the value of reading the source code rather than relying on parameter names or documentation. The assistant could have continued guessing at different max_seq_len values indefinitely, never understanding why the batch count wouldn't budge. Only by reading the collate function's implementation did the true bottleneck become clear.

This message, for all its brevity, represents the culmination of a multi-step debugging process that transformed the assistant's understanding of the training pipeline. The edit itself is small — adding a command-line argument and passing it to a constructor — but the reasoning behind it required tracing through the DataLoader, the collate function, the model's forward method, and the Triton compiler's shared memory constraints. It's a reminder that in complex ML systems, the most impactful fixes often come not from brute-force parameter tuning, but from understanding the actual data flow through the code.