Model Selection for Available Hardware
Choosing open models whose size and quantization fit your GPU capacity while meeting quality requirements, since the largest model you can technically load may not serve acceptably.
Custom Software
Hire On-Prem LLM Engineers
On-prem LLM engineers deploy and operate language models inside your own infrastructure. They handle model selection for available hardware, GPU capacity planning, inference serving, and quality benchmarking against hosted alternatives, so organizations that cannot send clinical data to external providers still have working AI capability.
The reason to do this is data residency, not cost. On-premise inference is usually more expensive per token than hosted APIs once hardware, power, and staffing are counted, and open models generally trail frontier hosted models in capability. The tradeoff is worth making when policy or contract prohibits data leaving, and rarely otherwise. Taction Software states that plainly, and our hire dedicated developers hub covers hosted alternatives.
Tell Us Your Requirements
Our experts are ready to understand your business goals.
Trusted Partners
Trusted by Industry Leaders Worldwide
Recognition
Awards & Recognitions
What On-Prem LLM Engineers Build
The work is infrastructure and operations rather than model development. Getting a model serving reliably at acceptable latency within your hardware budget is the substance, and capacity planning determines whether the deployment supports one pilot or a department. The work below reflects that. Benchmarking appears prominently because organizations need evidence about what they gave up relative to a hosted option.
Inference Serving Infrastructure
Deploying serving frameworks with batching, quantization, and memory management, since naive deployment wastes most of the throughput available hardware could deliver.
GPU Capacity Planning and Scheduling
Sizing hardware against expected concurrency and context length, and scheduling across workloads, since GPU memory rather than compute usually sets the concurrency ceiling.
Quality Benchmarking Against Hosted Options
Measuring what the on-premise model achieves relative to hosted alternatives on your tasks, so leadership understands the capability tradeoff being accepted.
Model Update and Version Management
Establishing how new open models are evaluated and rolled out, since capability improves rapidly and static deployments fall behind within months.
Monitoring, Cost, and Utilization Reporting
Tracking utilization, latency, and effective cost per request, since idle GPU capacity is a substantial expense that hosted alternatives do not incur.
Healthcare Context Driving On-Prem Deployment
The decision is usually made for reasons outside engineering: a policy prohibiting PHI leaving the environment, a contract with a research partner, or a governance committee unwilling to accept provider retention terms. Understanding which reason applies matters, because some are addressable through agreements and configuration rather than through hardware. The context below spans the healthcare work you assign.
01
Data Residency Is the Legitimate Driver
Where policy or contract prohibits clinical data reaching external providers, on-premise inference is the answer. Where it does not, hosted options are usually better and cheaper.
02
Zero Retention Agreements May Resolve the Concern
Providers offer terms limiting logging and retention. Where the objection is retention rather than transmission, an agreement may address it without hardware investment.
03
Open Models Trail Frontier Capability
Deployable open models generally perform below leading hosted models. That gap should be measured on your tasks rather than assumed to be either negligible or disqualifying.
04
Hardware Investment Ages Quickly
GPU purchases depreciate against rapidly improving alternatives. Capacity acquired for today’s models may be poorly matched to what becomes available within a year.
05
Operational Burden Falls on You
Hosted inference is someone else’s operational problem. On-premise deployment means your team handles availability, updates, and capacity permanently.
06
Clinical Boundaries Are Unchanged
Deployment location does not alter what the system may do. Human review, grounding, and safety enforcement requirements apply identically to on-premise models.
Technical Skills for On-Premise Inference
This is systems and infrastructure engineering with GPU-specific expertise. Serving optimization determines whether hardware delivers acceptable throughput, and most naive deployments achieve a fraction of what is possible. The competencies below reflect that. Weight serving optimization and capacity planning above model knowledge, since selecting a model is straightforward and making it serve well is not.
Inference Serving Framework Operation
Deploying and tuning serving frameworks with continuous batching, paged attention, and appropriate quantization for the hardware and latency target.
GPU Infrastructure and Memory Management
Understanding memory consumption across model weights, activations, and context, since context length drives memory in ways that surprise teams planning by parameter count.
Quantization and Quality Tradeoff Evaluation
Applying quantization with measured quality impact, since aggressive quantization increases throughput while degrading output in ways that require task-specific evaluation.
Model Evaluation on Local Tasks
Benchmarking candidate models against your actual use cases rather than public leaderboards, which correlate poorly with performance on specific clinical tasks.
Integration Into Clinical Applications
Exposing inference through interfaces applications consume. Our healthcare integration work covers the connectivity into clinical workflow.
Operations, Monitoring, and Capacity Management
Running the deployment with availability monitoring, utilization tracking, and capacity forecasting, since this becomes permanent operational responsibility.
How We Evaluate On-Prem LLM Engineers
The distinguishing question is what throughput they achieved on given hardware. Engineers who tuned serving report specific numbers and the optimizations behind them; those who deployed defaults achieved a fraction of capacity without knowing. Our assessment centers on serving optimization, capacity planning, and honest benchmarking. Our delivery process includes review points for reassessing fit.
Throughput Achieved and How
We ask what concurrency and latency they reached on specific hardware. Engineers who deployed without tuning wasted most of the capacity purchased.
Quality Comparison Against Hosted
We ask how the on-premise model compared. Engineers who never benchmarked cannot tell leadership what capability was traded for residency.
Capacity Planning Accuracy
We ask how their sizing estimates held. Underestimating context memory is the common error and produces a deployment that cannot serve expected concurrency.
Quantization Decisions
We ask what quantization they applied and what it cost in quality. Aggressive quantization without task evaluation degrades output invisibly.
Model Update Handling
We ask how they moved to a newer model. Deployments without an update path fall behind quickly as open model capability improves.
Verified Deployment Experience
We describe which on-premise deployments each engineer operated and at what scale. We do not claim hardware or vendor certifications for engineers who lack them.
Engagement Options for On-Premise Deployment
Engagements should begin by confirming that on-premise is actually required, because the requirement frequently traces to a retention concern addressable through provider agreements. Structures below reflect that. Where residency is genuinely mandatory, we scope hardware honestly, including the operational commitment organizations tend to underestimate.
Requirement Validation First
Establishing whether the objection is transmission or retention. Where agreements resolve it, hosted inference is cheaper, more capable, and carries no operational burden.
Benchmark Before Hardware Purchase
Evaluating candidate models on your tasks using rented GPU capacity before committing to hardware, so sizing follows measured requirements rather than estimates.
A Single Engineer for One Deployment
Suits a bounded deployment with defined workloads and existing infrastructure capability. One engineer maintains consistency in serving configuration and monitoring.
Augmenting Your Infrastructure Team
Where you own data center operations, staff augmentation adds inference expertise within your existing operational standards and hardware environment.
Full Team for Private AI Programs
A dedicated healthcare development team suits programs building applications, serving infrastructure, evaluation, and monitoring together in a private environment.
Fixed-Scope Deployment Build
Where hardware and workloads are defined, a fixed-scope build under our engagement models delivers serving infrastructure with benchmarking and monitoring.
Tell Us Why the Data Cannot Leave
Share the policy or contract driving the requirement, your expected workload, and your infrastructure capability. Some objections are resolved by agreements rather than hardware.
Residency, Operational Ownership, and Unchanged Boundaries
On-premise deployment addresses where data goes and changes nothing about what AI may do clinically. We build to HIPAA-aligned practices where HIPAA applies; software cannot be HIPAA certified. Where intended use may create diagnostic or treatment claims, SaMD classification is assessed during discovery. Human review, grounding, and safety enforcement requirements apply identically regardless of hosting.
01
Clinical Boundaries Unchanged by Hosting
On-premise models do not diagnose, prescribe, triage, or determine eligibility. Deployment location has no bearing on what output may influence without human review.
02
Safety Enforcement Still Outside the Model
Guardrails, grounding verification, and deterministic safety checks apply to on-premise inference exactly as to hosted, since the model is equally probabilistic.
03
Evaluation Requirements Are Identical
On-premise capabilities require the same evaluation and monitoring as hosted ones. Residency does not substitute for evidence that output quality is adequate.
04
Operational Responsibility Transfers to You
Availability, capacity, updates, and incident response become your organization’s permanent obligation, which is a real cost beyond the hardware purchase.
05
Sensitive Workload Isolation
Behavioral health and similar workloads may warrant separate deployment. We built CHIPSS, a behavioral health system, where isolation was foundational.
06
Deployments We Would Not Build
We would not build on-premise deployments without evaluation infrastructure, without safety enforcement, or where the capability gap versus hosted alternatives has not been measured and accepted.
Cost to Hire Engineers and Deploy On-Premise
Cost splits between engineering and hardware, with hardware frequently dominating and depreciating quickly. Effective cost per request depends heavily on utilization, since idle GPU capacity is expense without output. We publish no figures on cost comparison against hosted inference, because that depends on your volume and hardware. What we deliver is measured throughput and quality on your own hardware and tasks.
MVP or Single Module
$40,000 to $80,000
Deployment of one model with serving optimization, benchmarking against hosted alternatives, monitoring, and integration into one application, on existing or rented hardware.
Full Platform Build
$80,000 to $200,000
Private inference infrastructure serving multiple applications with capacity management, model update process, evaluation, monitoring, and cost attribution.
Enterprise Deployment
Starting at $200,000
Multi-facility private AI infrastructure with high availability, governance documentation, capacity planning across workloads, and integration into several clinical environments.
Discovery Phase Scoping
Discovery is paid and time-boxed. It produces a requirement validation finding, model benchmarking on your tasks, hardware sizing recommendation, and an itemized fixed-scope estimate.
Cost Drivers to Expect
Expected concurrency and context length, latency requirements, model size and quantization tolerance, availability requirements, existing infrastructure capability, and operational staffing readiness.
Ongoing Support Costs
On-premise inference carries permanent operational cost: hardware depreciation, power, capacity management, model updates, and the staffing to run it continuously.
Third-party licensing, cloud infrastructure, data subscriptions, and hardware are separate from engineering cost and itemised clearly.
Why Deploy Private Inference With Taction
Two questions matter. Whether the vendor validates that on-premise is required, and whether they benchmark honestly against hosted alternatives. Taction Software has built healthcare software since 2013, more than twelve years, with over 200 healthcare projects delivered and ISO 27001 certification. Leadership brings more than twenty years of personal experience in the field, which is separate from company age. Our wider case for Taction sits elsewhere.
Clinical Application Experience
We built Voyant Health, an EHR platform, and CHIPSS, a behavioral health system. Our healthcare case studies reflect the applications private inference must serve.
Experience Under Regulatory Registration
We built Revive Ease and PainKare, both FDA-registered applications. That work informs how we document deployment and evaluation in constrained environments.
ISO 27001 Certified Security Management
Taction Software holds ISO 27001 certification covering our information security management practices. It certifies our internal processes and does not determine your organization’s compliance position.
We Benchmark Against Hosted Honestly
We measure what the on-premise model gives up relative to hosted alternatives on your tasks, so the tradeoff is a decision rather than an assumption.
We Will Test Whether Hosting Is Actually Prohibited
Where the objection is retention rather than transmission, a provider agreement may resolve it. That finding removes a hardware project and most of our scope.
We Will State the Operational Commitment
On-premise inference becomes your permanent responsibility. We say that clearly before purchase, which occasionally causes organizations to reconsider the approach.
FAQs
Frequently Asked Questions
We validate why data cannot leave, benchmark candidate models on your tasks, and size hardware, then present matched candidates. You interview and approve each engineer.
Engineering for one deployment runs $40,000 to $80,000, private infrastructure $80,000 to $200,000, and enterprise deployment starts at $200,000. Hardware, power, and licensing are itemized separately and are substantial.
Our delivery history includes the Voyant Health EHR platform, the CHIPSS behavioral health system, and the FDA-registered applications Revive Ease and PainKare, within more than 200 healthcare projects delivered since 2013.
Usually not, once hardware, power, and staffing are counted. The reason to deploy privately is data residency requirements rather than cost, and we say so before you invest.
That depends on your tasks and should be measured rather than assumed. We benchmark candidates against hosted alternatives so the tradeoff is explicit before hardware is purchased.
Fine-tuning adapts a model to your tasks. This role deploys and operates models in your infrastructure, where serving optimization and capacity planning are the substance.
Share the policy or contract driving the requirement, your expected concurrency and context length, your infrastructure and operational capability, and the engagement model you have in mind. We will test whether an agreement resolves it and benchmark before recommending hardware. We do not promise instant matching or any cost comparison.