Reference hardware tiers
This page helps healthcare teams think about likely hardware ranges for local AI deployments. It is intentionally approximate. The exact hardware you buy is your decision, from your preferred vendors, based on how fast and how robust you need the system to be.
These are planning ranges, not procurement quotes or resale pricing.
Hardware is purchased directly by the client from preferred vendors.
Our implementation and maintenance fees are separate from hardware costs.
Exact requirements depend on concurrency, model size, response-time expectations, storage retention, and high-availability needs.
These ranges are meant to help with internal planning conversations. We size the final recommendation around the real workload rather than forcing everyone into a one-size-fits-all stack.
Starter
Estimated vendor spend $6k-$15k
Typical workloads
Private transcription, basic local summarization, document extraction, or a low-concurrency internal assistant.
Example hardware profile
One solid workstation or entry server, modern CPU, 64-128 GB RAM, fast NVMe storage, and one strong GPU class suitable for local inference.
Growth
Estimated vendor spend $18k-$45k
Typical workloads
Higher daily transcription volume, multiple staff users, local search over internal documents, and broader workflow automation.
Example hardware profile
Rack server or comparable workstation setup, 128-256 GB RAM, larger NVMe pool, and one to two data-center or prosumer GPUs depending on concurrency goals.
Department
Estimated vendor spend $45k-$120k
Typical workloads
More simultaneous users, faster turnaround expectations, larger local knowledge base, stronger uptime targets, and more resilient storage.
Example hardware profile
Redundant server-class setup, 256 GB+ RAM, expanded storage, stronger networking, and multi-GPU capacity for sustained inference demand.
Enterprise
Estimated vendor spend $120k+
Typical workloads
Multiple use cases across sites, higher availability requirements, more advanced monitoring, and room for future model expansion.
Example hardware profile
Clustered or segmented infrastructure, enterprise storage strategy, backup and failover planning, and multiple high-end GPUs or GPU nodes sized for concurrency and resilience.
Teams often overbuy because they hear a model name before they define the workflow. In healthcare, the better approach is to size the system around concurrency, retention, uptime expectations, and where the AI sits in the real process.
The biggest swing factor is how many users or workflows need answers at the same time. A low-volume clinic and a large shared service desk have very different inference needs.
Transcription-only setups are lighter than broad document intelligence, RAG, or larger local assistants that must reason over more content.
Audio retention, document archives, embeddings, logs, backups, and replication can change storage needs quickly even when inference demand stays modest.
Some teams are comfortable with a practical single-node pilot. Others need failover, segmented environments, and stronger uptime guarantees from day one.
Workflow review, privacy boundary analysis, technical design, deployment plan, and hardware sizing guidance.
Model runtime setup, storage and security configuration, application wiring, user access design, and internal system integration.
Monitoring, updates, troubleshooting, model and prompt tuning, patching, and ongoing operational support after launch.
Many healthcare buyers assume private AI automatically means an expensive enterprise cluster. That is not always true. A narrower transcription or document workflow can often start on more modest infrastructure and scale only if adoption and concurrency actually justify it.