Beirit Harvey

At a recent industry session, Kyle Bell, Head of Product at TensorWave, shared how the company approaches one of the most complex challenges in modern AI: building infrastructure capable of supporting healthcare and life sciences workloads at scale.

Scaling AI Infrastructure for Healthcare and Life Sciences

At a recent industry session, Kyle Bell, Head of Product at TensorWave, shared how the company approaches one of the most complex challenges in modern AI: building infrastructure capable of supporting healthcare and life sciences workloads at scale.

AI is rapidly becoming central to drug discovery, genomics, diagnostics, and medical imaging. These workloads require more than raw compute. They demand high-memory accelerators, efficient distributed systems, and infrastructure capable of supportin

At a recent industry session, Kyle Bell, Head of Product at TensorWave, shared how the company approaches one of the most complex challenges in modern AI: building infrastructure capable of supporting healthcare and life sciences workloads at scale.

AI is rapidly becoming central to drug discovery, genomics, diagnostics, and medical imaging. These workloads require more than raw compute. They demand high-memory accelerators, efficient distributed systems, and infrastructure capable of supporting both AI training and traditional research workflows.

TensorWave’s approach is to build that foundation around AMD Instinct GPUs, creating an open environment designed specifically for AI workloads.


Infrastructure Designed for AI Workloads

TensorWave’s platform combines AMD Instinct GPUs with an optimized ROCm software stack, bare-metal performance, and 800G ultra-low-latency networking. This architecture enables large distributed training jobs while maintaining predictable performance across multi-node clusters.

Native integration with Slurm and Kubernetes allows teams to orchestrate resources dynamically, making it easier to run different types of workloads, from training and fine-tuning to inference, all within the same environment.

Rather than locking organizations into rigid deployment models, the platform is designed to scale alongside evolving AI workloads.


Bringing New Accelerators Into Production

Bell also highlighted TensorWave’s collaboration with AMD across multiple generations of AI accelerators.

The company was an early launch partner for MI300X, delivered one of the first AMD direct liquid-cooled training clusters, and helped bring MI355X Polara AI into production environments. TensorWave is also preparing for upcoming platforms such as MI455X.

Operational experience deploying these accelerators at scale allows the company to integrate networking, storage, and orchestration in ways that keep large clusters stable under real workloads.


A Unified AI Infrastructure Stack

TensorWave’s platform spans the full AI infrastructure stack. At the hardware layer, AMD Instinct GPUs and EPYC CPUs are paired with high-performance networking and storage designed for distributed workloads. The platform supports multiple compute models including bare metal clusters, containerized environments, and managed orchestration.

This architecture allows teams to run training, fine-tuning, and inference on the same infrastructure, reducing operational complexity and improving overall resource utilization.


Why It Matters for Healthcare and Life Sciences

Healthcare and life sciences organizations often work with massive datasets, from genomics sequencing to high-resolution medical imaging. These environments require infrastructure capable of supporting both AI models and computational research pipelines.

The high-memory design of AMD Instinct GPUs helps support these workloads, while ROCm and HIP tooling provide a path for migrating existing CUDA-based workflows to AMD infrastructure.

One example comes from AstraZeneca, which worked with AMD’s Silo AI team to move several life sciences models from NVIDIA hardware to MI300X running on TensorWave infrastructure. According to AMD’s published results, the migration improved performance across multiple models while requiring no code changes.

As AI becomes more deeply integrated into scientific research, infrastructure will play an increasingly important role. Platforms built specifically for large-scale AI workloads can help healthcare and life sciences organizations move faster from experimentation to real-world impact.


TensorWave Cloud: Engineered for AI Velocity

TensorWave is the industry’s premier AMD-exclusive cloud provider, delivering memory optimized AI infrastructure for training, fine-tuning and inference. 

Our platform combines the raw computational power of AMD's latest GPU architectures with innovative liquid cooling technology and ultra-high-speed networking, creating an optimized environment for the most demanding AI workloads. From large language model training to real-time inference, TensorWave provides the performance, scalability, and cost-efficiency that modern AI initiatives demand.

Ready to get started? Connect with a sales engineer. 

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Scaling AI Infrastructure for Healthcare and Life Sciences

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