@xlr8harder Incidentally also matters to run this on HPC for synth…
@Dorialexander Been thinking about this a lot, looking forward to reading your thoughts on it.
Pleias CTO Pierre-Carl Langlais and builder @xlr8harder highlight how conventional Slurm schedulers, built for long-running batch or MPI jobs, create friction with the short, iterative, state-heavy loops typical of synthetic data generation in modern LLM pipelines.
@xlr8harder Incidentally also matters to run this on HPC for synth…
@Dorialexander Been thinking about this a lot, looking forward to reading your thoughts on it.
The mismatch arises because synthetic workloads often require dynamic tool calls, validation cycles, and decentralized agent flows that clash with static queueing and barriers, prompting @xlr8harder to prototype a bypass tool.
The claim rests on direct practitioner experience rather than controlled benchmarks, so broader impact on scaling synthetic pretraining or RL environments stays uncertain without further data.
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@Dorialexander HPC solution for this is much harder. Slurm is not shaped well for this workflow. I might have a useful tool for that environment, will DM you.
@xlr8harder Incidentally also matters to run this on HPC for synth…
@xlr8harder i know. been thinking as well, both on inference and agentic sim side…
@Dorialexander HPC solution for this is much harder. Slurm is not shaped well for this workflow. I might have a useful tool for that environment, will DM you.

@synquid @xlr8harder biggest one is actually tool exec latency/orchestration (though might have a solution, just very unconventional)

@Dorialexander @xlr8harder Interested in your challenges here, too slow inference?

@synquid @xlr8harder wide ep would be a massive unlock for running large frontier-ish moe on budget but don't think anyone ever did that…