Pharma's AI Infrastructure Imperative
How pharmaceutical and other life sciences organizations can build GPU infrastructure that matches the scale of their AI ambition.
The opportunity is real. The gap is bigger.
Artificial intelligence (AI) is rewriting the rules of pharmaceutical research and development (R&D). Drug candidates identified by AI are clearing Phase I clinical trials at rates of 80–90%, and AI-enabled workflows can reduce the time it takes to reach a preclinical candidate by 30-50%.
The economic case is equally compelling. Pharma companies that fully industrialize AI could gain an additional $254 billion in annual operating profits worldwide by 2030.
But despite the immense opportunity AI presents for pharmaceutical and life sciences organizations, execution is stalling. Only 22% of life sciences leaders report having scaled AI beyond a single use case and just 9% report significant return on investment.
The gap between opportunity and execution exists not for a lack of effort but because AI infrastructure isn't matched to pharmaceutical realities. Most infrastructure is built for generic workloads, not the regulatory, latency and compute demands pharma AI actually carries.
To close the gap and build GPU infrastructure that matches the scale of their AI ambition, organizations must separate myth from reality, make investment decisions early and adopt a framework that allows for multiple infrastructure paths.
The press release versus the reality
Every major pharmaceutical company has announced an AI strategy. Many have announced AI factories, AI labs and cloud-first AI commitments. Those announcements are real but they don't tell the full story, leaving the industry to make assumptions.
Here is where some of those assumptions diverge from reality. Getting this distinction right is the difference between copying a headline and making investments that close the AI infrastructure gap.
How pharma peers are making the call
Most pharma AI programs share a common decision pattern. AI starts in the cloud, which is chosen for its flexibility and speed. As AI ambition grows, so does cost and commitment: use cases move from experimentation to production, compute and data costs spike, latency problems emerge, and data residency requirements surface.
Teams are forced to make a decision: stay on cloud and absorb escalating unit costs or build dedicated infrastructure that makes production AI economically viable at scale. Common factors that trigger this decision include:
- Scientists hitting compute constraints that limit which experiments they pursue
- High utilization of existing on-prem GPU footprint
- IP and regulatory requirements around proprietary compound and genomic data
- Latency sensitivity for research workflows tied to physical lab equipment
Life sciences organizations that make the transition to building dedicated infrastructure early are the ones generating the greatest competitive advantage from AI. Cost optimization is often a welcomed byproduct of this transition, but guaranteed availability is of the greatest importance for long-term strategic advantage.
By contrast, organizations that delay making this transition end up spending more per experiment, running fewer experiments, falling behind in the discovery pipeline and limiting the likelihood of using AI to springboard to the next therapy that helps patients.
Four paths. One framework.
There's no single right way for life sciences organizations to build out AI infrastructure. Rather, there are four paths with distinct profiles of control, cost, compliance and flexibility. The right investments for any organization are a mix of these paths determined by the nature of the organization's AI workloads.
Path 1: Private Data Center
A private data center gives organizations full ownership of GPU compute, network and storage within a dedicated facility. It represents the highest upfront investment but yields the lowest per-token cost at scale, full regulatory control and no data sovereignty exposure. This path is the right choice for production inference workloads that run continuously at high volume and require HIPAA or GxP compliance by design.
Path 2: Colocation
A colocation model allows organizations to operate their own GPU servers in a shared facility. The colocation provider supplies power, cooling, physical security and connectivity. This path delivers a level of control similar to a private data center but at a lower capital commitment and with the ability to scale rack space as an AI program grows. It's an ideal choice for organizations that need compliance-grade infrastructure but aren't ready to commit to owned real estate.
Path 3: Neocloud
Purpose-built GPU cloud providers offer dedicated GPU clusters on a consumption basis. Organizations benefit from no capital expenditures, faster time to first GPU and competitive pricing for GPU-dense workloads. This is the right path for burst training runs, research teams that need GPU access without queuing, and programs that aren't yet large enough to justify private data center economics.
Path 4: Public Cloud
Amazon Web Services (AWS), Microsoft Azure and Google Cloud offer GPU instances with unmatched flexibility, developer ecosystem depth, and pay-as-you-go economics. This is the right path for development and test, experimentation, and workloads that genuinely need elastic scale across regions. At production inference volumes with compliance requirements, public cloud unit economics typically don't hold, but public cloud remains essential to a multi-path strategy.
Three questions to determine workload placement
Placing AI workloads across four paths requires a consistent decision framework. The framework isn't complicated, but it does need to be applied rigorously. Placing a workload on the wrong path carries financial consequences, regulatory risk, data exposure and competitive slowdown. Organizations should assess every workload through three lenses: regulatory, operational and commercial.
Question 1: Regulatory. What data does this workload touch?
A workload that touches patient data, IP or clinical trial results carries obligations that follow the data itself, regardless of which infrastructure choice seems most convenient at the time. Answering this question means working through three specific checks:
- Does this workload touch data that's subject to residency requirements, including HIPAA, GxP, GDPR or IP protection?
- Does training or inference involve proprietary compound data, genomic data or patient data?
- Are there sector-specific AI regulations on the horizon that could constrain cloud deployment?
Question 2: Operational. What does this workload demand at runtime?
Response time, availability requirements and throughput needs determine workload placement. It's important to assess the workload against three runtime pressures:
- Are there latency requirements that cloud variability cannot reliably meet?
- Is this workload embedded in a time-critical research or manufacturing process?
- Does it require continuous, guaranteed access, not subject to GPU queue times?
Question 3: Commercial. What are the volume and unit economics?
The cost to run a workload is directly shaped by how many scientists use it, how many tokens it processes per day and what cost per experiment the business can accept. Keep these cost considerations in mind:
- Will this workload run at significant volume for a known period?
- What does the total cost of ownership look like over three to five years on dedicated versus alternative infrastructure?
- Are inference costs already material and growing, or still small and speculative?
The goal is not to pick one infrastructure path for the whole organization. Most pharma organizations will find that they need all four paths simultaneously, governed by this three-question framework applied to each workload class. The goal is to place each workload on the path where it runs best, costs least and meets compliance requirements.
Case studies: AI infrastructure in action
The following examples from our work with life sciences organizations illustrate the four-path infrastructure model in action.
Building an AI Factory at scale, from design to operation
A global top-20 pharmaceutical company partnered with WWT to design and implement a full AI Factory architecture. This included coordinating a multi-vendor build involving NVIDIA, VAST Data and Digital Realty.
WWT led the infrastructure design, managed vendor integration and validated the complete stack in its AI Proving Ground before production deployment.
The result was a purpose-built, private GPU environment capable of running large-scale inference for drug discovery workloads, with GxP-compliant data architecture and the compute density to support hundreds of researchers simultaneously.
Establishing an AI Foundation for discovery visibility
A leading pharmaceutical company partnered with WWT to build the AI infrastructure foundation for a portfolio-wide discovery visibility program, spanning small molecule discovery, large molecule programs, genomics analysis and hypothesis-generation LLMs.
The engagement covered infrastructure assessment, multi-path architecture design and implementation of an integrated compute environment capable of running parallel workloads across four distinct AI model classes.
How WWT bridges every path
WWT helps pharmaceutical organizations evaluate, build and operate AI infrastructure across all four deployment paths. From workload placement and architecture design to infrastructure implementation and ongoing operations, WWT brings together the technical expertise, ecosystem partnerships and real-world experience required to support AI at pharmaceutical scale.
NVIDIA AI Factory partnership
WWT is a named implementation partner for NVIDIA's AI Factory program, the NVIDIA-validated architecture for enterprise AI infrastructure at scale. This partnership gives our pharma clients access to NVIDIA engineering expertise, preferential hardware allocation and validated reference architectures that compress build time from months to weeks.
Advanced Technology Center and AI Proving Ground
Backed by more than $1 billion in technology investments, WWT's Advanced Technology Center (ATC) allows pharmaceutical organizations to validate AI infrastructure before deployment. Teams can use the ATC's AI Proving Ground to evaluate GPU cluster performance, software stack compatibility and workload-specific benchmarks in a production-scale environment, reducing risk and accelerating time to value.
Partner ecosystem
WWT works with NVIDIA, VAST Data, Digital Realty, CoreWeave, Dell, HPE and the major public cloud providers. Our robust partner ecosystem allows us to design multi-path architectures that draw on best-in-class components across every layer without being tied to a single vendor.
Regulatory expertise
WWT's life sciences team understands GxP validation, HIPAA compliance architecture, GDPR data residency and EU AI Act high-risk system requirements. Infrastructure design is compliance-aware from the first conversation.
The conversation starts here
AI infrastructure decisions made in the next 12–18 months will shape the competitive position of pharma organizations for the rest of the decade. The organizations that build the right infrastructure now will run more experiments, at lower cost, with better regulatory standing.
The decision isn't binary. Life sciences organizations don't have to choose between public cloud and private data center. They need a framework for placing each workload on the path where it performs best and costs least — and a partner with the capabilities to build, validate and operate infrastructure across all four paths.
Four conversations to have with WWT
- Workload placement assessment: Map your current and planned AI workloads to the four-path model. Identify regulatory exposure. Quantify cost at scale.
- Tokenomics modeling: Build the unit economics case for private GPU infrastructure at your production volume. Drive the CFO conversation.
- AI Proving Ground: Validate your AI infrastructure architecture before you build it. Run your actual workloads on reference hardware, eliminating uncertainty.
- AI Factory design and build: Receive end-to-end AI Factory services, from architecture and validation through deployment and ongoing operations.
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This report is compiled from surveys WWT Research conducts with clients and internal experts; conversations and engagements with current and prospective clients, partners and original equipment manufacturers (OEMs); and knowledge acquired through lab work in the Advanced Technology Center and real-world client project experience. WWT provides this report "AS-IS" and disclaims all warranties as to the accuracy, completeness or adequacy of the information.