01
Jobs die halfway through
A laptop runs out of memory, and a failed long job starts over from zero.
Solutions / Research computing
Research jobs that run to completion
Tell us the software and data size before we talk hardware. We size environments around memory footprint, runtime, and delivery deadlines, so jobs don’t die halfway through on missing resources.
Bioinformatics, simulation, and batch jobsCPU / GPU / high-memory, matched to the jobRepresentative test run first
The blockers
Memory, storage, dependencies, or data handoffs can slow an analysis just as easily.
02
Nobody else can reproduce it
Software and script versions are scattered across machines, so results are hard to verify.
03
Overspending on specs
Buying headroom “just in case,” when the real bottleneck may be one single step.
04
Handing off results takes forever
Inputs, outputs, and intermediate files all mixed together — finding the right version is its own project.
How we solve them
Judge a new environment by total cost per valid result.
Find the real bottleneck
Pin down the program, data size, peak memory, and runtime before looking at CPU, GPU, and storage.
Budget goes where it matters.
Freeze the software and steps
OS, tool versions, and run commands recorded for the whole team to reuse.
Handoffs stop starting with fresh installs.
Test first, scale later
Run a representative dataset to check the configuration and output.
No guessing performance from spec sheets.
Agree where files live
Set conventions for inputs, outputs, access, and backups.
Finding and verifying results gets straightforward.
Deployment options
There is no single right answer. Where your data lives, how long jobs run, and what hardware you already have all shape the choice.
01
On-demand cloud
Jobs cluster around project milestones — no machine to keep alive between them.
Best for clearly spiky workloads.
02
Team-shared
Multiple people reusing the same software and data.
Best for steady lab pipelines.
03
On-premise
Data location or on-site management has hard requirements.
Confirm storage, backup, and ops conditions first.
How it comes together
Same data, same program versions, same accuracy checks — only then are speed and cost comparable.
- 01
Collect the job details
Software, data volume, runtime, and current errors.
- 02
Draft a configuration
Check CPU, GPU, memory, and storage requirements.
- 03
Run the comparison
Record runtime, cost, and output accuracy.
- 04
Hand over the environment
Versions, scripts, and result locations documented.
Worked example · simulated data
Say the same job takes 10 hours at $6/hour on the current environment, and 4 hours at $10/hour on the new one.
$60
Resource cost per run, before
$40
Resource cost per run, after
20 runs
Break-even, including the setup fee
Calculation:Before: 20 runs × $60 = $1,200. After: 20 runs × $40 + a one-time $400 setup fee = $1,200.
This is an illustrative estimate, not customer results or a quote. Resource cost per run drops about 33% and runtime shrinks 60% — but if the output doesn’t pass the same accuracy checks, the comparison is void.
Tell us about your workload
Send the software name, data size, current runtime, and errors. We find the bottleneck first, then recommend a configuration.
One representative job is enough to start
Existing hardware can be assessed too
FAQ
Do we need a GPU?
Not always. First check whether the software supports GPU at all, and whether memory, CPU, or storage is the actual bottleneck.
Can we use our current scripts and software?
We verify versions and dependencies first — and keep whatever can be kept.
How do we compare two configurations?
Same data and program versions, confirm the outputs match, then compare runtime and cost per valid result.
Does the data need to move?
It depends on where the data lives and how you deploy. Migration time and cost belong in the plan either way.