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Expanse gets you more out of the GPUs you already have.

Expanse delivers compute certainty.

We predict exactly what every job needs before it runs, so your cluster does far more real work with the same hardware.

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Expanse analyses a GROMACS SLURM job, returns evidence-backed GPU, memory, runtime, and completion recommendations, then connects those recommendations to live cluster evidence.
Product demonstration
The problem

Every workload
starts with
a guess.

A training run, an HPC simulation, an inference serving fleet: before any of them, an engineer has to decide how much GPU, memory and time it needs.

Today, that decision is a guess. Expanse replaces it with evidence.

Illustrative comparison values showing the same workload before and after Expanse analysis.

The educated guess

A sensible plan by experienced engineers. Nobody could see it runs out of GPU memory.

GPU allocation
16 × H100
GPU memory
80 GB
Runtime, then died
6.5 h
Completion probability
11%

with Expanse

The same job, analysed and re-planned in seconds.

Recommended GPUs
16 × H100
Expected GPU memory
24.6 GB
Estimated runtime
45 min
Completion probability
97%
How it works

Three steps to
compute certainty.

  1. 1

    Connect your existing cluster

    One command installs Expanse on your existing SLURM, Kubernetes, or Nomad cluster. It automatically collects detailed telemetry alongside your workloads, bringing complete infrastructure visibility without changing your existing workflows.

  2. 2

    Animated diagram showing Expanse model accuracy improving as it observes more workloads on your infrastructure.

    Expanse learns your infrastructure

    Expanse arrives pretrained on real HPC and AI workloads, then continuously adapts to your environment. Every workload improves future predictions, making the platform more accurate over time.

  3. 3

    Animated execution summary revealing predicted GPU allocation, runtime, memory use, and completion probability.

    Receive accurate predictions before every workload

    Run Analyse before execution to predict GPU allocation, memory usage, runtime, and completion probability. Every prediction is presented as a confidence distribution, helping teams make infrastructure decisions with greater certainty.

Workloads

Certainty for
every workload.

Expanse learns from everything that runs on your cluster. The only difference is how predictions reach you: ask in the terminal, or have them applied behind the scenes.

Book a discovery call

You ask

HPC & Simulation

Use Expanse analyse before launching large-scale simulations to estimate resource requirements and reduce failed or overprovisioned jobs.

Animated command-line example running Expanse Analyse on a GROMACS HPC workload before submission.

Behind the scenes

Inference

Predictions happen automatically behind the scenes. Models are continuously right-sized, GPUs are utilized more efficiently, and workloads are optimized without changing developer workflows.

Animated Expanse inference workflow showing continuous workload prediction and GPU right-sizing.

You ask

Training

Run expanse analyse before you submit. Predict GPU allocation, memory usage, runtime, and failure risk before your job enters the queue.

Animated Expanse training workflow predicting compute requirements before the job enters the queue.
Proof

Measured outcomes,
not marketing claims.

$8M
Monthly wastage uncovered
A national supercomputing centre revealed more than $8 million in monthly compute waste
8 ×
Read the benchmark
More accurate than the best frontier LLM
June 2026 benchmark across two national HPC systems. Expanse predicted runtime within 10% and memory within 5% (median error). Frontier models were off by ~85% on runtime and ~60% on memory.
2 to 2.5 ×
Reserved vs actually used
Measured across 122,000 production HPC jobs: teams typically reserve two to two and a half times what they actually use. That gap is the capacity Expanse recovers.
Features

One platform.
Two features.

expanse analyse

Predicts each job’s memory, runtime and failure risk before it runs, so requests are sized on evidence instead of estimation.

  • Memory, runtime and failure risk, before the queue
  • Predictions as distributions, with confidence
  • Same submit script, no workflow change
Learn more
expanse analyse product interface

expanse diagnose

Finds the root cause of failed jobs from code, logs and metrics, so engineers stop losing hours in logs.

  • Root cause named: the failure pattern, the exact file
  • A ready-made prompt for your coding agent, grounded in the evidence
  • Every diagnosis makes the model sharper
Learn more
expanse diagnose product interface
Why Expanse

Built for teams that
run their own clusters.

1 of 7: Runs alongside your scheduler, not instead of it

Runs alongside your scheduler, not instead of it 01

Expanse sits alongside SLURM, Kubernetes or Nomad, not in place of them. Your scheduler keeps scheduling; Expanse sizes the requests it schedules.

Runs alongside your scheduler, not instead of it. Expanse sits alongside SLURM, Kubernetes or Nomad, not in place of them. Your scheduler keeps scheduling; Expanse sizes the requests it schedules.

Animated diagram showing a submitted job passing through Expanse to an existing Slurm, Kubernetes, or Nomad scheduler.

Ready to make
compute certain?

Talk to our team about how Expanse fits your cluster, your scheduler, and your workloads.

Book a discovery call