Biotech GPU Computing Requirements: Genomics and Drug Discovery

Biotech GPU computing is the capacity problem where sequencer output, model memory, and simulation runtimes replace generic cloud sizing. Genomics pipelines, protein structure models, molecular dynamics, and screening runs each impose different GPU, storage, and scheduling requirements, and a requirements list that names the workload family names the infrastructure. This page maps the four dominant families to observable requirements, provides sizing math the reader reruns with their own sample volumes, and lists the evidence a provider must produce before a biotech team commits.

Four Workload Families and What Each Demands

Biotech compute separates into four families with different requirements. Genomics pipelines (alignment and variant calling) run short GPU jobs in the tens-of-minutes range per whole genome with GPU-accelerated toolkits, so they scale on job throughput and scheduler overhead more than single-job speed. Protein structure and sequence models follow the AI pattern: transformer-class training and inference where GPU memory in gigabytes and interconnect bandwidth set the ceiling. Molecular dynamics simulations run long resident jobs that value sustained floating-point throughput and large memory per node. Screening and docking workloads scale horizontally across thousands of independent jobs, favoring many cost-efficient GPUs over few flagship ones. A requirements list that names the family names the GPU class, the interconnect, and the scheduler behavior the provider must demonstrate.

The families rarely arrive alone. A discovery program that sequences cohorts, trains models on the results, and screens candidate compounds touches all four rows, which is why the one-provider question later in this page asks for evidence across the mix rather than a benchmark of a single pipeline. A single accountable operator such as OneSource Cloud can serve the full mix on one dedicated environment instead of four disconnected capacity sources.

Sizing from Sequencer Output and Evidence to Request

Size from your own throughput targets with three formulas. Genomics: sequencer output per week times documented per-sample processing time on the chosen GPU class, divided by target turnaround window, yields the GPU count; a 30x whole-genome sample also carries roughly 100 GB of raw and derived storage, so weekly sequencing volume times that figure sizes the capacity tier. Simulation and AI families: node count follows per-node performance benchmarks the provider must run on your actual workload, not on a generic LINPACK score. The evidence to demand before contract: a benchmark of your pipeline on the offered GPU class with measured wall-clock time, a storage design that keeps multi-hundred-gigabyte sample sets near compute, written data-handling scope covering genomic data residency, and validation support commitments. Providers that decline workload-specific benchmarks are quoting capacity, not capability.

1. Compute the genomics GPU count: weekly samples times documented GPU-hours per sample, divided by the turnaround window in hours.
2. Compute the storage tier: weekly sequencer output times roughly 100 GB per 30x whole-genome sample, plus retention policy.
3. Request a workload-specific benchmark on the offered GPU class with measured wall-clock time per sample or per simulation step.
4. Request written data-handling scope: where genomic data resides, who can access it, and what commitments the provider signs.
5. Request validation support: who executes, who signs off, and what happens when results diverge from the benchmark.

Each step converts a marketing claim into a number or a signature. Sequencing cores that operate under clinical-adjacent agreements should also confirm whether their data triggers healthcare-grade handling, including BAA-eligible environments, before the pilot rather than after it.

Fit, Not Fit, and What to Verify Further

Public cloud fits burst genomics analysis and early-stage discovery where teams value elastic capacity and accept egress economics on terabyte-scale sample movement. Dedicated environments fit sustained pipelines, controlled genomic data, and simulation workloads where data gravity and utilization make metered capacity the expensive option. Not fit conditions are nameable: public cloud fails when data-use agreements restrict processing to controlled environments, and single-node setups fail when discovery models exceed single-GPU memory. Verify further in every model: whether the provider runs your pipeline benchmark before commitment, where sample data physically resides, who signs data-handling commitments, and how scheduling isolates a screening burst from a time-critical clinical-adjacent run. Teams with mixed clinical and discovery data should also verify whether the environment supports the BAA-eligible boundary their clinical side requires.

OneSource Cloud delivers the dedicated row as single-tenant bare-metal clusters in secure U.S. data centers with tiered NVMe-oF storage for sample-scale datasets and flat-rate monthly pricing with zero egress fees, so a sequencing week does not become an egress invoice.

FAQ

How much GPU capacity does a genomics pipeline need?

Compute it directly: weekly sample volume times the documented GPU-hours per sample on your chosen GPU class, divided by the turnaround window, gives the GPU count. For example, 200 samples per week at 1 GPU-hour each inside a 40-hour window needs 5 GPUs dedicated to the pipeline, before headroom. The estimate becomes a committed number only after the provider runs your pipeline as a benchmark.

What does OneSource Cloud provide for biotech and life-sciences compute?

OneSource Cloud delivers dedicated single-tenant bare-metal GPU clusters in secure U.S. data centers with BAA-eligible environments, tiered NVMe-oF storage for sample-scale datasets, and flat-rate monthly pricing with zero egress fees. Genomic and discovery data stays inside one controlled boundary, and capacity scales by commitment rather than by metered rate resets.

Summary: Requirements Before Capacity

The discipline that protects biotech compute budgets is naming the workload family before naming the provider: the family determines GPU class, interconnect, storage tier, and the four evidence requests that separate capability from capacity claims. Sizing from sequencer output keeps the numbers honest, and the benchmark gate keeps the provider accountable to them. For teams whose genomic and discovery data must stay inside a controlled U.S. boundary, OneSource Cloud dedicated environments are built for exactly that requirement.

To map your workload families onto dedicated infrastructure, explore OneSource Cloud healthcare and life-sciences solutions.