FEATURE: STORAGE
David Hutchful, CEO of Yemaachi Biotech
African genomes, representing the most genetically diverse populations in the world, offer a unique window into global health innovation. But fully harnessing that potential demands immense computational resources.
A single sequenced genome can generate hundreds of gigabytes of data; Yemaachi’ s research spans thousands of samples across multiple disease areas. Turning these datasets into intelligence requires powerful storage capable of high-speed ingest, parallel processing and rapid access for Machine Learning pipelines.
Before the BlueNODE deployment, the scale and speed necessary for modern genomic science were difficult to achieve without relying on external cloud platforms – an approach that introduced concerns around cost, latency and most critically, data sovereignty. For genomic information tied to African populations, local control is essential. Yemaachi required a solution that would bring worldclass storage and compute capabilities on-premise in Ghana, without compromising security or performance.
BlueSPACE BlueNODE: Infrastructureas-a-Service for Genomic AI
BlueSPACE, a pioneer in Africa’ s AI and financial infrastructure ecosystem, answered this need by deploying its largest BlueNODE platform to date for Yemaachi. The BlueNODE service provides fully managed, AI-ready compute and storage capacity in a sovereign framework, eliminating the need for research institutions to build or maintain their own HPC environments. resources without the financial and operational burden of owning physical hardware – while still keeping all data within a sovereign, locally controlled environment.
The role of the Nexsan Unity NV10000 in AI-enabled genomics
The Unity NV10000 plays a pivotal role within the BlueNODE deployment, delivering the performance characteristics essential for Yemaachi’ s research. As a unified storage platform supporting file, block and S3 object simultaneously, it aligns perfectly with the diverse demands of genomic science.
Raw sequencing data enters the system as large files, HPC workloads interact through block protocols, and AI model training often relies on object storage repositories. Having all of these modalities within a single platform dramatically simplifies pipeline management and reduces operational friction.
Performance is equally critical. The Unity NV10000 incorporates NVMe acceleration to ensure high throughput and low latency across concurrent workloads. For Machine Learning applications – such as variant calling, biomarker discovery, or disease prediction models – consistently fast access to massive datasets determines the speed at which models can be trained and validated.
By eliminating I / O bottlenecks that traditionally slow GPU-driven training environments, the Unity NV10000 supports Yemaachi’ s ability to innovate rapidly and iterate on new scientific approaches.
Reliability and scale are also central advantages. The NV10000’ s architecture supports multi-petabyte growth without requiring disruptive infrastructure redesigns, allowing Yemaachi to continuously expand its genomic library as new cohorts and studies come online. This long-term scalability ensures that the platform remains sustainable as Yemaachi transitions from foundational research to advanced AI-driven discovery programs and global pharmaceutical collaborations.
Accelerating research through high-speed data access
Within this deployment, the Nexsan Unity NV10000 serves as the storage backbone. The BlueNODE architecture integrates HPC compute clusters with the NV10000’ s multi-petabyte unified storage layer, creating a cohesive platform optimised for genomics and Machine Learning.
One of the most significant outcomes of the BlueNODE / Nexsan deployment is the acceleration of Yemaachi’ s research workflows. High-throughput sequencing instruments now stream data directly into the Unity NV10000, enabling downstream analysis to begin almost immediately.
By consuming the infrastructure-as-a-service, Yemaachi gains access to enterprise-grade AI / HPC
Bioinformaticians and AI researchers can execute multiple processes simultaneously on the same
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