Healthcare organizations are under pressure to reduce administrative costs, improve diagnostic accuracy, and enhance patient experiences. To achieve this, they are rapidly moving AI from pilots into production, requiring infrastructure that delivers low latency, strong security, predictable costs, and reliability at scale.
Healthcare AI teams are leading the charge in deploying and fine-tuning open-source models, as well as training their own custom models. From document processing and image recognition to frontier research like drug discovery, purpose-built models are beating generally intelligent models at domain-specific tasks.
However, deploying these models in production requires overcoming new bottlenecks in inference. Serving thousands of concurrent users with predictable latency, robust security, and controlled unit economics presents new engineering challenges. With the Baseten Inference Stack and access to NVIDIA HGX B200 from Vultr, AI engineering teams in the healthcare space are able to quickly ship products backed with fast, reliable inference.
Healthcare AI solutions delivering value today
Healthcare is one of the earliest and fastest-growing industries in adopting AI, outpacing many other sectors in its application and impact. But three use cases stand out today where we’re seeing immediate impact:
Document processing and back-office automation: automated processing of millions of documents turns unstructured data into usable information, reducing administrative burden.
Clinical assistants and patient service agents: multi-modal agents grounded in medical research and electronic health records support both in-person and virtual care.
Image recognition for diagnostic assistance: in radiology and beyond, image recognition models help providers identify illnesses and issues.
These tasks share common factors: they are latency sensitive, require complex interactions between multiple models and modalities, and serve a high volume of traffic. Most importantly, they are mission-critical. Healthcare providers and patients rely on these tools to be fast and highly available, as well as cost-effective at scale.
To take ownership over these outcomes, AI engineering teams are increasingly adopting open-source models for both improved domain-specific performance and better control over infrastructure and serving.
From foundation models to real-world healthcare
Out of the box, most AI models – regardless of sophistication or general benchmarks – aren’t well-suited to domain-specific healthcare tasks. Many AI engineering teams in the space choose to adopt and fine-tune open-source foundation models, or even train new models from scratch, to ensure that their system is accurate enough for real-world use.
Some models that healthcare companies rely on heavily include:
Large language models like GPT OSS to deliver core intelligence across systems.
Vision language models like GLM 4.5V and Llama 4 for image and document processing.
Automatic speech recognition models like Whisper for transcription.
Multi-modal embedding models to power search, retrieval, and classification.
These extremely large models have demanding infrastructure requirements. To deliver state-of-the art performance with strong unit economics, teams turn to NVIDIA HGX B200 instances for inference.
Large model inference on NVIDIA HGX B200
NVIDIA HGX B200 systems can run inference for extremely large models, even models like Kimi K2 with over 1 trillion parameters. Based on the advanced NVIDIA Blackwell architecture, NVIDIA HGX B200 systems offer:
Baseten’s Inference Stack maximizes the performance of the Blackwell architecture to deliver lower latency and higher throughput inference. With open models, developers can make their own tradeoffs between latency and throughput to stay within a latency budget while achieving cost savings for high-volume inference.
As an example, Baseten achieved state of the art performance on NVIDIA accelerated computing for GPT OSS within a day of launch, and maintains the lowest latency available on independent third party OpenRouter at the time of writing.
Baseten achieves leading latency, throughput, and uptime on NVIDIA GPUs for GPT OSS 120BRunning healthcare AI inference with Baseten and Vultr on NVIDIA HGX B200
Inference in production is about more than just benchmark performance. Baseten and Vultr work together to help engineering teams overcome the technical and operational challenges of shipping in production.
Both platforms are deeply committed to security and privacy. With HIPAA-compliant infrastructure and software, models run on dedicated deployments where traffic is not mixed between customers, or in self-hosted deployments within healthcare providers' own VPCs. This provides a secure and compliant foundation for building AI services.
With Baseten, teams get more flexible access to Vultr Cloud GPUs. Often, getting allocations of in-demand hardware like NVIDIA HGX B200 systems requires rigid multi-year commitments. Baseten lets teams access GPUs seamlessly across regions, unlocking more capacity and flexibility for deployments.
With state-of-the-art performance, flexible and cost-effective GPU access, and a HIPAA-compliant infrastructure platform, Baseten and Vultr ensure that AI engineering teams in the healthcare space can go to market fast while maintaining a low TCO for their AI systems. Whether you’re reducing administrative overhead, deploying patient-facing agents, or enhancing diagnostic workflows, Vultr and Baseten deliver the secure, cost-effective infrastructure needed to bring AI into real-world healthcare,
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