Case Studies
Overview
Context
Challenge
Testimonial
Contribution
Impact
Why It Matters?
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Biotech

GPU Reliability & Virtualisation Optimisation

Overview
Context
Challenge
Testimonial
Contribution
Impact
Why It Matters?
Overflow
Overview & Metadata

GPU Reliability & Virtualisation Optimisation for a Biotech Organisation

Improved GPU node reliability, observability, and virtualisation performance for the organisation’s AI and computing platform.
Client Context
Client Context

A Biotech organisation runs large-scale AIand scientific computing workloads across a GPU-enabled infrastructure built onHashiCorp tooling and HPC-grade virtualisation.

Hybrid Mind Supported →

Hybrid Mind delivered engineering expertise to strengthen GPU durability,improve observability, and accelerate performance across the compute platform, powering advanced modelling and experimentation.

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Challenge

Key Challenges We Faced

1. Limited visibility into GPU utilisation, thermal behaviour, and node-level stability.

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2. Obtainer (HPC container engine) exhibited slow startup times and inconsistent performance.

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3. Highly complex infrastructure landscape spanning Terraform, Nomad, Consul, Vault,GitHub Actions, and Python-based automation.
Our Contribution

Hybrid Mind delivered:

1. GPU Stability Improvements  

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  • GPU Stability Improvements  

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2. Enterprise-Grade Observability

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  • Built a Grafana dashboard visualising GPU performance and platform stability.
  • Supported definition of SLIs and SLOs for operational governance.

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3. HPC Virtualisation Performance Enhancement

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  • Diagnosed and resolved Obtainer performance issues.
  • Diagnosed and resolved Obtainer performance issues.

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4. Platform Engineering Uplift

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  • Improved automation, orchestration, and operational workflows across Terraform, Nomad,Consul, Vault, GitHub Actions, and Python tooling.
Impact

Hybrid Mind works with enterprises to unlock real value from AI, from readiness to measurable impact

the impact on the GPU performance was substantial.
  • More reliable GPU platform supporting AI and scientific workloads.  

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  • 10×improvement in container startup times, accelerating model training and experimentation.

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  • Stronger operational maturity through improved observability and governance.  

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  • Positive client feedback and expanding internal visibility of Hybrid Mind's engineering capability.
Why It Matters?

Why It Matters?

AI and scientific workloads depend on reliable, observable GPU infrastructure.

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Hybrid Mind's work improved platform performance, stability, andgovernance—strengthening the foundation for future AI-driven research and computation.

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