Chunk 54.1

This chunk captures a failed attempt to restart training after the OOM on GPU 6. The assistant diagnosed the memory shortfall (~200 MB) as stemming from the torch cu130 upgrade and extra packages (SGLang, flashinfer, triton) consuming additional GPU memory. To keep the critical `anchors=1024` and `block_size=32` configuration intact, the assistant reduced `token_budget` from 49152 to 45056 and `max_batch_size` from 64 to 48, then launched a run with 5 target GPUs and 3 drafter GPUs. However, GPUs 6 and 7 crashed silently during the first backward pass, leaving only GPU 5 active among the drafters and dropping throughput to ~5.4 Ktok/s. The assistant pivoted to a 6‑target + 2‑drafter configuration, which avoided OOM but only reached ~9.7 Ktok/s—far below the previous 20 Ktok/s achieved with 5 targets + 3 drafters on the older torch 2.11+cu128. The user rejected this outcome, pointing out that the assistant had been instructed to “start from scratch” (i.e., from step 0, not resume from step 690) and that the earlier 5t+3d setup had worked well before the dependency changes. The assistant then killed the session and began reverting torch from cu130 back to cu128 to restore the original memory budget, aiming to recover the stable 20 Ktok/s performance with the expanded dataset. Themes in this chunk include the tension between preserving training signal (anchors/block size) and managing GPU memory, the cascading impact of dependency version upgrades on a tightly tuned pipeline, and the importance of following user instructions precisely (resuming from step 690 vs. starting from scratch). The assistant’s iterative adjustments—from 3 drafters to 2, from 5 targets to 6—demonstrate a systematic but ultimately unsuccessful attempt to work around the memory overhead, leading to a rollback of the torch version as the corrective action.

The 200MB That Broke a Training Pipeline: A Case Study in Dependency Cascades and Memory Budget Crises 2215 words

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