Powering Pharma AI with NVIDIA H100 and Blackwell GPUs
[Revised January 15, 2026]
Powering Pharma AI with NVIDIA H100 and Blackwell GPUs
The pharmaceutical and biotech industry is embracing cutting-edge AI hardware to accelerate drug discovery and biomedical research. In particular, many companies are investing in NVIDIA's latest GPUs – the H100 Tensor Core GPUs (based on the Hopper architecture), the next-generation Blackwell series (including the B200 and Blackwell Ultra), and soon the Vera Rubin architecture – to power advanced machine learning workloads. These GPUs, often deployed in clusters or supercomputers, provide the massive compute needed for training large models (e.g. generative models, protein-folding algorithms) and analyzing enormous datasets in drug R&D. In a landmark announcement at the January 2026 J.P. Morgan Healthcare Conference, Eli Lilly unveiled the world's largest pharmaceutical AI factory – a system powered by 1,016 NVIDIA Blackwell Ultra GPUs delivering over 9 exaflops of AI performance ([1]). This article explores which pharma and biotech companies around the world have publicly announced the use or purchase of NVIDIA H100/Blackwell GPUs, backed by evidence such as press releases and official reports. We also highlight how these companies apply GPU-accelerated AI for tasks like drug design, protein structure prediction, molecular simulation, and more.
Why Pharma is Investing in AI Supercomputers
Recent breakthroughs in generative AI and large language models (LLMs) have shown that scaling up models with more data and compute can yield dramatic performance gains. Pharma companies are keen to apply similar approaches to biological and chemical data. Training AI models on millions of compounds or genetic sequences demands extensive parallel computing power, which modern GPUs excel at. NVIDIA's H100 GPU, for example, is currently one of the most powerful chips for AI, delivering exaflop-scale performance when many are used in tandem ([2]) ([3]). As Recursion's CTO Ben Mabey put it, "we see AI models in the biology domain improve performance substantially as we scale our training with more data and compute horsepower" – ultimately leading to better outcomes for patients ([4]). This need for scale has driven the industry to adopt GPU-based supercomputers and cloud GPU clusters.
Key AI use cases driving GPU adoption in pharma/biotech include:
- Generative AI for drug design: e.g. models that generate novel molecular structures with desired properties (akin to how LLMs generate text). Companies use generative models to propose new drug candidates (small molecules or even biologics) and optimize them ([5]).
- Protein structure and interaction prediction: GPUs enable protein folding simulations (like AlphaFold-style models) and prediction of how drugs bind to targets (docking). For instance, NVIDIA's BioNeMo service offers models like DiffDock for predicting ligand-protein binding ([6]).
- Molecular dynamics (MD) and simulations: High-end GPUs can perform MD simulations of molecular systems at high speed, helping researchers observe how proteins and compounds behave over time ([7]).
- Analyzing 'omics' and imaging data: AI models for genomics or microscopy images (e.g. high-content cell imaging) require GPUs to train on terabytes of data. Vision transformers applied to cell images or models analyzing genomic sequences are used to discover disease biomarkers and new targets ([8]) ([9]).
To support these efforts, pharma and biotech firms are either procuring on-premises GPU supercomputers or leveraging cloud HPC with GPUs. Below, we profile several leading companies that have publicly confirmed deployments of NVIDIA H100 (and plans for Blackwell) in their AI infrastructure, along with the specific use cases enabled by this hardware.
Recursion (USA) – BioHive-2 Supercomputer with 504× H100 GPUs
One standout example in biotech is Recursion Pharmaceuticals, a tech-driven drug discovery company. Recursion completed BioHive-2 in May 2024, described as "the largest system in the pharmaceutical industry", which debuted on the TOP500 supercomputer list at #35 ([10]). This AI supercomputer is powered by 63 DGX H100 systems containing 504 NVIDIA H100 Tensor Core GPUs connected via NVIDIA's Quantum-2 InfiniBand network, configured as an NVIDIA DGX SuperPOD ([11]). The result is about 2 exaflops of AI performance – nearly 5× faster than Recursion's previous cluster (BioHive-1).
How Recursion uses this GPU powerhouse: Their mission is to accelerate drug discovery by combining biological datasets at massive scale with AI models. Recursion has generated over 50 petabytes of biological images and data from automated experiments. With BioHive-2's compute muscle, they train foundation models on these data – for example, the "Phenom" family of vision-transformer models that turn high-resolution cellular images into useful biological representations. One such model, Phenom-1, was trained on 3.5 billion microscopy images to learn patterns of disease and treatment effects. These models can predict how cells react to new compounds, helping identify promising drug candidates faster.
Recursion's H100 cluster also enables enormous virtual screening tasks. In a collaboration with NVIDIA, Recursion demonstrated that combining BioHive-1 (earlier system) with cloud GPUs could screen ~36 billion chemical compounds in under 30 days, predicting potential protein targets for each. With BioHive-2's expanded capacity, such analyses are now even faster. The company has integrated this AI supercomputer into an end-to-end workflow (called "LOWE") with natural language interfaces for scientists.
Recent advances (2025): Recursion partnered with MIT's Jameel Clinic and CSAIL to develop Boltz-2, a cutting-edge model trained on BioHive-2. Boltz-2 approaches the chemical accuracy of physics-based free-energy perturbation (FEP) simulations while returning results in about 20 seconds on a single A100 GPU – a dramatic speed improvement. According to Recursion's CTO, "With AI in the loop today, we can get 80% of the value with 40% of the wet lab work, and that ratio will improve going forward." This efficiency has helped Recursion and partners like Amgen reduce discovery timelines from years to months, demonstrating concrete ROI on GPU infrastructure investments.
Amgen (USA) – "Freyja" DGX SuperPOD (248× H100) for Generative AI in Drug Discovery
Global biotech leader Amgen has made a bold move to integrate AI at scale into its R&D. In January 2024, Amgen announced it will deploy an NVIDIA DGX SuperPOD AI supercomputer – nicknamed "Freyja" – at its deCODE Genetics subsidiary in Iceland ([12]) ([9]). Freyja will consist of 31 NVIDIA DGX H100 nodes (248 H100 GPUs total) and will be used to train state-of-the-art AI models in a fraction of the time previously required ([9]). This essentially gives Amgen a dedicated, full-stack AI data center platform on-premises ([12]).
What Amgen aims to do with Freyja: A key goal is to leverage deCODE's unique human genomic data for drug discovery. deCODE (acquired by Amgen) has amassed over 200 petabytes of de-identified human genetic and health data from ~3 million individuals, including a large portion of Iceland's population ([13]). Amgen plans to use Freyja to build a "human diversity atlas" – AI models that can find drug targets and disease biomarkers by analyzing genetic variation across this vast dataset ([14]) ([9]). For example, they will train generative AI models on genomic and clinical data to uncover patterns that predict disease progression or drug response ([8]). This could enable more precise identification of therapeutic targets and even aid in developing personalized medicines (by finding patient subgroups with particular biomarker signatures).
Freyja's 248× H100 GPUs drastically speed up model training, allowing Amgen's researchers to iterate faster ([15]). Instead of taking months to train large models on CPU clusters or small GPU setups, they can now train in days. This accelerated cycle is crucial given the complexity of Amgen's AI tasks (which range from LLMs for DNA/RNA to deep neural nets for predicting protein functions). Amgen's embrace of a top-tier NVIDIA SuperPOD – and the public announcement of this collaboration – signals that big biotech sees AI and GPUs as integral to its future. David Reese, Amgen's CTO, noted that this union of technology and biotech is a "hinge moment" for the industry.
Strategic positioning (2025): Amgen strategically located the DGX SuperPOD directly at deCODE genetics' headquarters in Iceland to minimize data transfer risks and create a tightly integrated environment for protecting the intellectual property of its AI models. This approach maximizes synergy between deCODE's crown-jewel genomic data asset and the computational engine. Amgen continues to expand its collaboration with Recursion and other partners, demonstrating measurable ROI by reducing discovery timelines from years to months.
BioNTech (Germany) – InstaDeep's "Kyber" Cluster (224× H100) for AI-Driven Drug Design
German biotech BioNTech (famous for its mRNA vaccine) has also placed AI at the center of its R&D strategy. In 2023, BioNTech acquired InstaDeep, an AI startup, to bolster its machine learning expertise. As part of this effort, BioNTech/InstaDeep built a new in-house AI supercomputing cluster called "Kyber", unveiled in late 2024 ([16]). Kyber is equipped with 224 NVIDIA H100 GPUs, 86,000 CPU cores, and high-speed networking, delivering on the order of 0.5 exaFLOPs of AI performance ([17]). This makes it one of the world's top 100 most powerful computer clusters and among the top 20 H100 GPU clusters globally ([18]).
Applications of Kyber at BioNTech: The cluster is intended to accelerate BioNTech's pipeline in areas like immunotherapy and vaccine development by enabling large-scale AI research. At the BioNTech AI Day 2024, InstaDeep showcased how Kyber powers new AI innovations in biology ([19]). For example, they introduced Bayesian Flow Networks (BFN) – a novel class of generative AI models for biotech applications. BFNs can generate biological sequences (such as protein or antibody sequences) in a continuous, controllable manner, which could be useful in designing new proteins or optimizing vaccine immunogens. Unlike standard diffusion models, these BFNs allow researchers to more precisely guide the generation process, potentially speeding up the discovery of drug candidates or vaccines by exploring sequence space more efficiently.
2025 developments: At AI Day 2025, InstaDeep demonstrated significant progress with their AI stack. Kyber enabled training of generative AI models with more than 15 billion parameters with hardware efficiency on par with Meta's Llama 3.1 foundational model. The InstaNovo model, trained on 63 million labeled spectra, delivers a 10–15% increase in accuracy, up to twice as many peptide identifications, and 50-fold faster inference. InstaNovo is being applied at BioNTech to uncover novel targets and biomarkers within the "Dark Proteome."
On top of Kyber sits AIchor, InstaDeep's orchestration platform that makes the supercomputer accessible to scientists through a streamlined GitOps workflow. In 2025, AIchor facilitated an average of 15,000 experiments per month across their research teams. As InstaDeep CEO Karim Beguir highlighted: "AI is not a single exponential, but a triple exponential comprising data, compute, and models." This investment clearly signals BioNTech's commitment to being at the forefront of AI-enabled drug development. Uğur Şahin, BioNTech's CEO, has indicated that integrating such AI capabilities is crucial for the company's vision of developing "personalized medicines at scale."
Astellas & Tokyo-1 (Japan) – Consortium Supercomputer (DGX H100) for Pharma
In Japan, several pharma companies are banding together to access world-class AI infrastructure. A prominent example is Astellas Pharma, one of Japan's top pharmaceutical firms, which is participating in the "Tokyo-1" initiative ([20]). Tokyo-1, announced in 2023 by Mitsui & Co. in collaboration with NVIDIA, is Japan's first generative AI supercomputer for the pharmaceutical industry ([21]). In its first iteration, Tokyo-1 includes 16 NVIDIA DGX H100 systems, each with eight NVIDIA H100 Tensor Core GPUs (128 GPUs total), hosted as a shared resource for pharma companies and startups in Japan. The effort is poised to accelerate Japan's $100 billion pharma industry, the world's third largest following the U.S. and China.
Astellas is using BioNeMo (NVIDIA's drug discovery AI platform) on Tokyo-1 to accelerate its research ([22]). Concretely, Astellas plans to leverage this H100-powered supercomputer for tasks like molecular simulations and large language models applied to drug discovery ([22]). By tapping into Tokyo-1, Astellas scientists can run high-resolution molecular dynamics to study how drug molecules behave, or train LLMs on chemical and biomedical text data to aid in drug design. The Tokyo-1 infrastructure supports generative chemistry models, allowing users to create novel molecular structures in silico, and also enables running quantum chemistry calculations faster than before ([7]). In short, it provides Japanese pharma companies a competitive AI-as-a-service platform with H100-scale performance.
For Astellas, which might not have built its own giant supercomputer in-house, participating in Tokyo-1 is a way to still get access to state-of-the-art GPU hardware. According to NVIDIA, "the project will provide customers with access to DGX H100 nodes" for all these advanced applications ([7]). The collaborative model (with Mitsui's Xeureka unit operating the facility ([23])) means multiple pharmas can benefit. We know Astellas is on board; other major Japanese pharmas like Takeda or Daiichi Sankyo could join as well. This reflects a trend in some regions to invest in shared AI infrastructure for pharma R&D, rather than each company building from scratch. Nonetheless, it underscores that even in Japan (historically more focused on wet-lab research), there is now a push to embrace AI and GPU-accelerated computing in drug development ([24]).
Other Notable Efforts and Future Outlook (Blackwell and Vera Rubin GPUs)
Beyond the examples above, numerous other pharma and biotech players have signaled serious investments in AI hardware:
Eli Lilly (USA) – World's Largest Pharma AI Factory with 1,016 Blackwell Ultra GPUs
In October 2025, Eli Lilly unveiled the world's largest pharmaceutical AI factory at NVIDIA GTC Washington, D.C. – a system powered by 1,016 NVIDIA Blackwell Ultra GPUs delivering over 9 exaflops of AI performance ([1]). This represents the first NVIDIA DGX SuperPOD with DGX B300 systems wholly owned and operated by a pharmaceutical company. A single NVIDIA Blackwell Ultra GPU contains the power of approximately 7 million Cray systems, and Lilly's full AI factory can perform over 9 quintillion calculations per second.
At the January 2026 J.P. Morgan Healthcare Conference, NVIDIA and Eli Lilly announced a $1 billion co-innovation AI lab focused on applying AI to tackle some of the most enduring challenges in the pharmaceutical industry ([25]). The lab will be built on the NVIDIA BioNeMo platform and the upcoming NVIDIA Vera Rubin architecture, with select AI models to be made available on Lilly TuneLab – an AI platform providing biotech companies access to drug discovery models built on $1 billion worth of Lilly's proprietary data.
Using NVIDIA NeMo software, Lilly can create AI agents that reason, plan, and act across digital and physical labs, with goals including generating new molecules, designing treatments in silico, and testing them in vitro. With NVIDIA Omniverse and NVIDIA RTX PRO Servers, Lilly can create digital twins of manufacturing lines to model and optimize entire supply chains before making physical changes.
Novo Nordisk (Denmark) – Gefion Supercomputer Partnership
In June 2025, Novo Nordisk announced a multiyear partnership with the Danish Centre for AI Innovation (DCAI) to use the Gefion sovereign AI supercomputer, powered by 1,528 NVIDIA H100 Tensor Core GPUs ([26]). This collaboration provides Novo Nordisk access to unparalleled computational capabilities for processing vast datasets and transforming drug discovery.
Novo Nordisk will use NVIDIA BioNeMo for generative AI-powered drug discovery, NVIDIA NIM and NVIDIA NeMo microservices for building customized agentic workflows, and the NVIDIA Omniverse platform to create physically accurate simulation environments. Researchers will focus on using single-cell models to predict cellular responses to drug candidates and designing models to build molecules with drug-like properties. As Lars Fogh Iversen, Senior Vice President at Novo Nordisk noted: "Gefion will allow us to tackle compute-heavy challenges, like AI-based protein engineering and biological models... the opportunities are immense."
AstraZeneca (UK) – Quantum-Accelerated Drug Discovery
AstraZeneca continues to be a pioneer in pharma AI. They utilized Cambridge-1, NVIDIA's £40M supercomputer launched in 2021 with 80 DGX A100 nodes, to develop the MegaMolBART generative model for molecules, trained on about 1.45 billion compounds ([27]).
In June 2025, IonQ, AstraZeneca, AWS, and NVIDIA demonstrated a quantum-accelerated drug discovery workflow achieving over a 20-fold improvement in time-to-solution for the Suzuki-Miyaura reaction, a widely used method for synthesizing small-molecule pharmaceuticals ([28]). The hybrid system integrates IonQ's Forte quantum processor with NVIDIA CUDA-Q and AWS infrastructure. AstraZeneca's Centre for Genomics Research has also set an ambitious goal to analyze up to 2 million genomes by 2026, supported by advanced AI and machine learning tools.
Merck & Co. (USA) – KERMT Model for Small-Molecule Discovery
In December 2025, Merck & Co. partnered with NVIDIA to produce the KERMT model for small-molecule drug discovery, pretrained on approximately 11 million molecules for ADMET (absorption, distribution, metabolism, excretion, toxicity) property prediction ([29]). This represents another major pharma company contributing proprietary AI models to the industry ecosystem.
Insilico Medicine (USA/Hong Kong) and Others
Insilico Medicine continues to leverage NVIDIA GPUs via cloud services and NVIDIA's Inception program. Its generative chemistry platform (Chemistry42) and target discovery tools run on NVIDIA Tensor Core GPUs. Insilico reportedly used GPU-powered AI to design a novel anti-fibrosis drug that entered clinical trials in under 2.5 years – a process speed-up credited in part to NVIDIA technology.
IQVIA, a leading CRO, is using NVIDIA's AI Foundry to train custom AI models on its 64 petabyte real-world data repository ([30]). Illumina is partnering with NVIDIA to accelerate genomic analysis (e.g. DNA sequencing data processing with DRAGEN). Google's Isomorphic Labs is partnering with Novartis and Eli Lilly to discover new treatments using AI.
NVIDIA BioNeMo Platform Expansion (January 2026)
At the J.P. Morgan Healthcare Conference in January 2026, NVIDIA announced a major expansion of the BioNeMo platform, an open development platform enabling lab-in-the-loop workflows for AI-driven biology and drug discovery. BioNeMo provides a development platform to generate and process data, train, optimize and deploy models – enabling the industry to turn data into a competitive engine for discovery and maximize the probability of success while minimizing R&D costs, currently estimated at $300 billion per year industry-wide.
New BioNeMo features include BioNeMo Recipes for accelerating biological foundation model training, nvMolKit (a GPU-accelerated cheminformatics tool), and new NVIDIA Clara open models including RNAPro for RNA structure prediction and ReaSyn v2 for ensuring AI-designed drugs are practical to synthesize. Model builders using BioNeMo now include Basecamp Research (EDEN family of AI models), Boltz PBC (Boltz Lab for AI-driven molecular design), Chai Discovery, Apheris, Dyno Therapeutics, OpenFold, and Terray Therapeutics.
The Vera Rubin Architecture – Next Generation (2026+)
Looking ahead, NVIDIA's Vera Rubin architecture, announced at CES 2026, represents the next evolution of AI compute for pharma. Now in full production, Vera Rubin promises a 5× increase in inference performance and 10× reduction in inference token cost versus Blackwell ([31]). The platform features:
- Rubin GPU: 336 billion transistors, up to 50 PFLOPs of NVFP4 inference (5× higher than Blackwell)
- HBM4 memory: Up to 288GB per GPU with 22 TB/s bandwidth
- Vera Rubin NVL72: 72 Rubin GPUs and 36 Vera CPUs delivering 3.6 EFLOPS of inference performance
The Lilly-NVIDIA co-innovation lab will be built on Vera Rubin architecture, and major pharma companies with established AI programs are expected to upgrade as systems become available in the second half of 2026. NVIDIA is framing Vera Rubin as ideal for agentic AI, advanced reasoning models, and mixture-of-experts (MoE) models – capabilities increasingly critical for complex drug discovery workflows.
In summary, the pharma/biotech sector worldwide is "getting serious" about AI, evidenced by substantial investments in NVIDIA's top-tier GPUs. From startup biotechs like Recursion building record-breaking supercomputers, to biopharma leaders like Amgen and BioNTech standing up their own AI clusters, and consortium efforts in Japan and Europe – the trend is clear. These organizations are not just dabbling; they are establishing AI as a core competency and backing it with the necessary infrastructure. The table below compiles some of the key players and initiatives, along with their known NVIDIA GPU deployments and AI use cases, as documented by publicly available sources.
Pharma/Biotech Companies Using NVIDIA H100/Blackwell GPUs
| Company | Country | Type | NVIDIA GPU Used | AI Use Case(s) | Source / Evidence |
|---|---|---|---|---|---|
| Eli Lilly | USA | Pharma | Blackwell Ultra (1,016 GPUs via DGX B300 SuperPOD) | World's largest pharma AI factory; AI agents for drug design, digital twins for manufacturing, $1B co-innovation lab with NVIDIA | ([1]) (1,016× Blackwell Ultra GPUs, 9+ exaflops) |
| Recursion Pharmaceuticals | USA | Biotech (TechBio) | H100 (504 GPUs via DGX SuperPOD) | AI-driven drug discovery; training vision foundation models on cellular images; massive virtual screening; Boltz-2 model development | ([11]) (504× H100 supercomputer, TOP500 #35) |
| Amgen (deCODE Genetics) | USA (Iceland site) | Biotech/Pharma | H100 (31× DGX H100 nodes = 248 GPUs) | Generative AI on 200+ PB human genomic data; creating "human diversity atlas" for target discovery and precision medicine models | ([32]) (SuperPOD with 248× H100 for drug discovery AI) |
| BioNTech (with InstaDeep) | Germany | Biotech/Pharma | H100 (224 GPUs in "Kyber" cluster) | Scaling AI for immunotherapy; Bayesian Flow Networks for protein generation; InstaNovo for proteomics; 15B+ parameter models | ([19]) (Kyber cluster with 224× H100) |
| Novo Nordisk (via Gefion) | Denmark | Pharma | H100 (1,528 GPUs in "Gefion" supercomputer) | BioNeMo for generative drug discovery; single-cell models; agentic AI workflows; biomedical LLMs | ([26]) (Multiyear Gefion partnership for drug discovery) |
| Merck & Co. | USA | Pharma | H100/Blackwell (via BioNeMo) | KERMT model for ADMET prediction; trained on 11M molecules for small-molecule drug discovery | ([29]) (Dec 2025 NVIDIA partnership) |
| Astellas Pharma (via Tokyo-1) | Japan | Pharma | H100 (16× DGX H100 nodes = 128 GPUs) | Molecular dynamics simulations, LLMs and generative chemistry via Tokyo-1 shared supercomputer | ([21]) (Using Tokyo-1 H100 AI supercomputer) |
| AstraZeneca | UK | Pharma | A100 + quantum-hybrid | MegaMolBART generative model; quantum-accelerated drug discovery (20× speedup with IonQ/AWS/NVIDIA); 2M genome analysis target by 2026 | ([28]) (Quantum-hybrid workflows) |
| Novo Nordisk Foundation (DCAI) | Denmark | Non-profit/Research | H100 (1,528 GPUs in "Gefion" supercomputer) | National AI supercomputer for biotech, genomics, and healthcare research; sovereign AI for precision medicine | ([33]) (Gefion SuperPOD with 1,528× H100) |
(Table notes: Eli Lilly's Blackwell Ultra deployment (October 2025) represents the first major pharma adoption of NVIDIA's next-generation architecture, marking a significant milestone. The Lilly-NVIDIA $1 billion co-innovation lab (January 2026) is the largest disclosed AI collaboration in pharma. Novo Nordisk appears twice – once for their direct partnership with DCAI/Gefion, and once for the Novo Nordisk Foundation's broader role funding Gefion. AstraZeneca's entry has been updated to reflect their 2025 quantum-accelerated drug discovery work. Vera Rubin architecture systems are expected to enter pharma deployments in the second half of 2026, with the Lilly-NVIDIA co-innovation lab being an early adopter.)
Conclusion
The rapid adoption of NVIDIA's cutting-edge GPUs (H100, Blackwell, and soon Vera Rubin) by pharmaceutical and biotech companies illustrates a paradigm shift in the industry. Drug discovery and development – once the realm of slow, trial-and-error experiments – is being transformed into a compute-intensive, AI-driven process. The January 2026 announcements at the J.P. Morgan Healthcare Conference – including Eli Lilly's $1 billion co-innovation lab with NVIDIA and the massive BioNeMo platform expansion – mark a watershed moment where the industry's transformation from theoretical to operational becomes undeniable.
Companies that invest in powerful AI infrastructure can iterate faster on ideas, sift through vast chemical and biological spaces, and potentially bring therapies to patients more efficiently. The evidence is mounting: Recursion and Amgen are reducing discovery timelines from years to months, BioNTech is training 15-billion-parameter models for protein engineering, and Eli Lilly's 9-exaflop AI factory represents computational power unimaginable just a few years ago. Whether it's a nimble startup building the world's fastest pharma supercomputer, or an established pharma giant partnering to stand up a national AI center, these efforts all point to an AI arms race in life sciences.
Importantly, these are no longer theoretical experiments; they are backed by concrete evidence of GPU purchases, deployments, and projects delivering measurable results. Generative AI models are churning out drug leads, AI vision models are deciphering cell images, language models are reading biomedical literature at scale, and agentic AI systems are beginning to automate entire discovery workflows – all enabled by the computational heft of modern GPUs. As NVIDIA continues to advance its GPU architecture – with Vera Rubin promising 5× inference performance and 10× cost reduction versus Blackwell – we can expect even more ambitious AI projects in pharma. The endgame is compelling: a future where "AI factories" for drug discovery run within pharma companies, continuously designing and testing virtual drugs, much like automated fabs. The companies highlighted here have positioned themselves at the forefront of this revolution, and their work is likely to shape how quickly AI delivers new medicines and health breakthroughs to society.
Looking ahead to 2026 and beyond: The industry is expected to see continued consolidation of AI infrastructure, with major pharma companies either building dedicated AI factories (like Eli Lilly) or partnering with sovereign AI initiatives (like Novo Nordisk with Gefion). Regulators are expected to publish more concrete frameworks for validating AI in drug approvals, building on 2025 drafts, which could unlock further investor interest as regulatory uncertainty decreases. Industry R&D spending on AI compute – already estimated at over $8 billion annually across the pharma sector – is projected to grow substantially as the ROI from early adopters becomes more widely demonstrated.
In summary, pharma and biotech firms serious about AI are investing heavily in NVIDIA GPU technology. They are coupling these hardware investments with talent and data to create a new kind of R&D engine – one where computing power and scientific insight go hand in hand. The early adopters have set a high bar, demonstrating that with the right supercomputing resources, tasks once deemed intractable (like searching a chemical universe of billions of molecules) become feasible ([10]). This is an exciting time at the intersection of computing and biology, and 2026 promises to be the year when pharma AI transitions from competitive advantage to competitive necessity – ensuring companies have the GPUs, AI models, and agentic workflows needed to compete in the next era of drug discovery.
For more insights on AI and data science in the pharmaceutical industry, check out our articles on data science in life sciences, generative AI proof of concepts, and commercial analytics software.
External Sources (33)
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