GoKawiilHey HN, we’re Ismaeel, Eren, Yafet and Nikodem. We built Expanse ( https://expanse.sh/ ) to increase the effective capacity of your HPC/GPU clusters running schedulers/orchestrators like Kubernetes and SLURM. We read the source code, job submission script, and the hardware a workload is about to run on to predict what the job actually needs before the cluster sees it. We also flag failures we think are about to happen and surface line-level optimisations the researcher can apply themselves.
The problem: Datacenters run at roughly 30% to 40% effective utilisation. Users request more resources than what they actually need, because of asymmetric risk: while over-requesting is bad because it’s expensive and wastes capacity that someone else could have used, under-requesting kills your job mid-run and you lose days of work. So everyone over-requests by two to three times.
We measured one national-scale HPC cluster for a month and from 122k jobs, 59% of the compute was wasted. At on-demand cloud rates for the same hardware, that’s roughly $8.5M of compute wasted in one month on one cluster. The pattern is similar in large scale compute industries as well, such as quant funds, AI labs, and manufacturing.
The four of us ran HPC and GPU training workloads at the largest quant funds and HPC facilities. Ismaeel did research at EPCC (Edinburgh’s Parallel Computing Centre, the UK’s national HPC site) under Adrian Jackson, where he built the first multimodal HPC resource predictor: a model that ingests job source code, submission scripts, hardware telemetry and cluster metadata in order to figure out how much compute will actually be needed. On a dataset of real workloads on EPCC’s own clusters it scored 34% better than any other baseline, and outperformed frontier general-purpose LLMs prompted on the same prediction task by roughly 8x. These results convinced us the problem was solvable with software.
Expanse installs on every node and hooks into SLURM (or the K8s scheduler). It ingests live hardware telemetry (DCGM, CUPTI, Cgroups, Network/IO monitoring) of your cluster creating a custom embedding of how your hardware performs. We scan any workloads about to be submitted through SLURM/K8s (plugging into the life cycles of the job so you don't have to change how you submit things) and we feed this into our deep learning models to give researchers accurate resource recommendations, failure detections, and optimisation suggestions at submission time. We fine tune cluster-specific models that get sharper over time as you run more workloads. Our models are trained to over-provision rather than under-provision due to the asymmetric outcomes of a job crashing. We also provide uncertainty estimates and p90 values to allow users to choose their risk tolerance.
We surface three capabilities to users of the cluster:
(1) Resource prediction at submit time. We predict the GPU VRAM, Utilisation, memory, CPUs and walltime the job actually needs, with a confidence interval. From these predictions we also surface failure predictions for OOMs and other memory related issues, and code line level optimisations to increase the utilisation of the job on the hardware.
(2) Live Observability. While the job runs we showcase the telemetry we are collecting through a dashboard that gives an intuitive view of what's going on in the hardware and where your workload is at in terms of code stack profiling. We dynamically profile workloads to achieve a low single digit overhead while being informative.
(3) Failure diagnosis. If a workload fails, we take all the data we collected and perform correlations on the stack profiling and the hardware telemetry we collect to surface solution oriented logs. These are one, two line logs telling you not only what happened when the job failed, but why and how to fix it with code line level suggestions.
What’s different about our approach: The state of the art for most clusters is to either have per-user historical averages from sacct (SLURM accounting DB); hand-written rules/heuristics; or frontier LLM coding agents. For per-user historical averages from sacct, once a new type of workload is submitted onto the cluster or code level changes are made the model becomes wildly inaccurate. For the LLM baseline we provided them with the submission script and source code of the workload being ran, and gave it the full capabilities of its coding harness in the cluster and it performed quite poorly. We benchmarked Expanse against the state of the art at the time (Gemini 3.5 pro, Claude Opus 4.8, GPT 5.5, Codex 5.3) and outperformed them by 8x.
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