NVIDIA Healthcare United States

DELFI Diagnostics

DELFI Diagnostics needed to speed genomic analysis for cancer screening and therapeutic monitoring, but CPU-bound alignment and somatic variant-calling steps created turnaround-time and cost bottlenecks that limited how quickly the company could move assays into clinical use.

10–20 business days
5.4x faster compute time and a 4.1x cost reduction
6.6x faster compute time and an 8.1x reduction in compute cost
approximately 150x depth

Reported impact

Business outcome

DELFI showed that Parabricks can cut compute time 5.4x with a 4.1x cost reduction for FirstLook Lung and 6.6x with an 8.1x cost reduction for DELFI-TF, helping the company shorten turnaround times, make genome-wide somatic calling affordable, and prepare its monitoring workflow for clinical-scale sample growth.

  • Cost Reduction
  • Risk Reduction
  • Speed to Market

Solution

DELFI evaluated NVIDIA Parabricks as a drop-in accelerator for the genomic pipelines behind FirstLook Lung and DELFI-TF. The company already used low-pass and deeper whole-genome sequencing on cell-free DNA to screen for lung cancer and monitor recurrence, so the key opportunity was to replace slow CPU-based secondary-analysis stages without rewriting upstream or downstream workflows. Parabricks FQ2BAM took over alignment and preprocessing tasks such as BWA-MEM, SAM sorting, duplicate marking, and optional BQSR, while GPU-accelerated variant callers including MutectCaller and DeepSomatic made genome-wide somatic analysis practical for DELFI-TF. Because inputs and outputs remain compatible with existing pipelines, DELFI can add GPU acceleration directly into its production flow, cut compute time and cost, and open the door to deeper sequencing coverage and faster clinical turnaround as sample volumes grow.

Data flow

Sequencing reads from DELFI's liquid-biopsy assays enter Parabricks for GPU-accelerated alignment and preprocessing, variant-calling tools such as MutectCaller and DeepSomatic analyze the resulting data, and the outputs feed DELFI's screening and therapeutic-monitoring products without changing the surrounding pipeline contracts.

Solution architecture

4 components · 1 layer
  1. Compute
    • NVIDIA Parabricks Provides GPU-accelerated secondary analysis for DELFI's genomic workflows.
    • Parabricks FQ2BAM Replaces CPU-based alignment and preprocessing inside existing DELFI pipelines.
    • MutectCaller Adds GPU-accelerated somatic variant calling for disease-monitoring workflows.
    • DeepSomatic Expands DELFI's ability to run genome-wide somatic analysis at practical cost.

Architecture clues

  • DELFI-TF currently runs at 4x depth, and DELFI tested approximately 150x depth with Ultima sequencing.
  • MutectCaller and DeepSomatic make genome-wide somatic calling computationally practical.
  • Parabricks FQ2BAM replaces BWA-MEM, SAM sorting, duplicate marking, and optional BQSR.

Evidence from the source

DELFI-TF's current turnaround time is 10–20 business days.
For FirstLook Lung, running Parabricks on a low-pass WGS pipeline yielded 5.4x faster compute time and a 4.1x reduction in cost for sequence alignment.
Parabricks' FQ2BAM is a GPU-accelerated tool that replaces the CPU-based alignment and preprocessing steps.
incorporating this technology can achieve 6.6x faster compute time and an 8.1x reduction in compute cost.
Transparency Evidence & methodology 100/100 reference value · 92% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. NVIDIA

    DELFI Diagnostics customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/delfi-diagnostics/
    Source type
    Vendor-published customer story
    Published
    Sep 22, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. Built In

    Delfi Diagnostics Careers, Perks + Culture | Built In

    Independent secondary
    https://builtin.com/company/delfi-diagnostics

    The Organization JSON-LD lists numberOfEmployees with value 137. An employee count of 137 maps to SMB.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

  • Published on source page Sep 22, 2026 · NVIDIA · First-party
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.