Self-Hosted LLM & On-Prem AI Infrastructure Consulting
Run open-weight models on GPUs you control — private, compliant, and free of per-token lock-in. We build the self-hosted LLM platform; your team owns it.
For the platform team: production model serving on GPU-scheduled Kubernetes. For the CIO and CFO: data sovereignty, predictable cost, and no dependency on a single AI vendor.
Why enterprises self-host their LLMs
A self-hosted LLM puts an open-weight model — Llama, Qwen, Mistral, Gemma, DeepSeek — on infrastructure you own, instead of a metered third-party API. For regulated and data-sensitive organizations, that is the difference between sending proprietary data outside your boundary and keeping it inside a private LLM you control end to end.
THNKBIG is a US-based platform-engineering consultancy that builds on-prem AI and self-hosted LLM infrastructure for enterprises across healthcare, financial services, government, defense, manufacturing, and energy. We are model- and vendor-neutral: we deploy the open model, serving runtime, and GPU platform that fit your workload — not a stack we happen to resell.
Data sovereignty and compliance
On-prem and air-gapped AI keeps sensitive data — PHI, financial records, classified material, trade secrets — inside your own network. We build private LLM platforms with no outbound internet dependency, private model registries, encryption in transit and at rest, RBAC, and CIS/kube-bench hardening, so the platform can stand up to HIPAA, SOC 2, PCI, FedRAMP, and ATO review.
Predictable cost at scale
Hosted LLM APIs price per token, so cost scales linearly — and unpredictably — with usage. A self-hosted LLM converts that variable spend into fixed GPU capacity you can plan around. We model your real token volume against GPU cost, then keep utilization high with MIG partitioning, time-slicing, and demand-based autoscaling so you pay for work done, not idle capacity. For high-volume workloads, the break-even against a metered API often arrives within the first year.
No vendor lock-in
Building on open models on your own GPU Kubernetes means you are never captive to one provider's pricing, rate limits, deprecations, or terms. You can swap models, run several in parallel, and fine-tune on your own data. Every engagement ends with GitOps-managed infrastructure-as-code and runbooks, so your team owns and operates the private AI platform independently.
The full self-hosted LLM stack
From GPU platform to production model serving to the internal AI applications your users touch.
Private model serving
High-throughput inference with vLLM, Triton, or TGI — plus Ollama for fast internal use. Batching, streaming, and quantization tuned to your latency and GPU-memory budget.
GPU-scheduled Kubernetes
NVIDIA GPU Operator, MIG partitioning, and bin-packing on RKE2, OpenShift, or upstream — on-prem, hybrid, or air-gapped, so GPUs stay busy and workloads stay isolated.
RAG & internal AI apps
Embeddings, vector database, and orchestration for retrieval-augmented generation, then hands-on work with app owners to ship internal AI applications on your private model.
Air-gapped & hardened
Private registries (Harbor, Hugging Face mirror), encryption, RBAC, and CIS/kube-bench hardening for HIPAA, FedRAMP, and ATO-grade environments.
Cost & GPU governance
Utilization dashboards, autoscaling, and right-sizing so a fixed GPU fleet serves the most tokens — with the API break-even modeled against your real usage.
Observability & handover
DCGM, Prometheus, and Grafana for GPU and inference metrics, runbooks, and knowledge transfer so your team operates the platform without us.
A self-hosted LLM platform we've shipped to production
Open models, GPU-scheduled Kubernetes, and RAG — architected for sovereignty, cost, and scale.
Internal AI applications
Chat · copilots · inference APIs
RAG & orchestration
Embeddings · vector database · query pipeline
Model serving runtime
vLLM · Triton · TGI · Ollama · continuous batching · KV-cache
GPU-scheduled Kubernetes
NVIDIA GPU Operator · MIG · time-slicing · RKE2 / OpenShift
GPU nodes & open-weight models
H100 · A100 · L40S · Llama · Qwen · Mistral · Gemma · MiniMax
Observability
DCGM · Prometheus · Grafana
GPU utilization, memory pressure, and inference latency tracked in real time.
Sovereignty & Security
Air-gapped · encryption · RBAC · CIS
No outbound dependency; controls that support HIPAA, FedRAMP, and ATO pursuit.
Self-hosted AI in production
Real on-prem GPU engagements — from bare-metal buildouts to hardened production LLM platforms in six weeks.
On-Prem AI Cluster Buildout
GPU-scheduled Kubernetes on bare metal for an organization with strict data-sovereignty requirements — private models, no cloud dependency.
Read the full case study → Rapid DeploymentSix-Week AI Rapid Strike
A hardened RKE2 cluster with on-prem NVIDIA GPUs, open models via Hugging Face, and an internal AI application in production — in six weeks.
Read the full case study →From assessment to a platform you own
Assess & Stabilize
Readiness assessment: workloads, data-sensitivity, GPU budget, and API break-even modeled against your real usage.
Build & Harden
Model serving, RAG, and GPU-Kubernetes — on-prem, hybrid, or air-gapped — built and hardened as GitOps infrastructure-as-code.
Operate & Transfer
Observability, runbooks, and knowledge transfer so your team operates the private LLM platform independently.
Insured · client references and certificate of insurance available on request · Backed by senior, US-based platform engineers — meet the team.
Self-hosted LLM: frequently asked questions
What is a self-hosted LLM, and why run one instead of a hosted API?
A self-hosted LLM is an open-weight large language model — Llama, Qwen, Mistral, Gemma, MiniMax, DeepSeek — that you run on infrastructure you control instead of calling a third-party API. Organizations self-host to keep proprietary and regulated data inside their own boundary, to escape per-token pricing that scales unpredictably with usage, and to avoid vendor lock-in to a single model provider. For high-volume or data-sensitive workloads, a private LLM on your own GPUs is frequently both cheaper and more compliant than a metered API.
Which open models and serving runtimes do you deploy?
We are model- and runtime-neutral. We deploy open-weight models via Ollama for rapid internal use, and vLLM, NVIDIA Triton Inference Server, or TGI for high-throughput production serving. Common choices include Llama, Qwen, Mistral, Gemma, Phi, DeepSeek, and MiniMax, plus fine-tuned variants. We select the model and quantization that fit your latency, accuracy, and GPU-memory budget rather than pushing a single stack.
Can you deploy on-prem or air-gapped for HIPAA, FedRAMP, or classified environments?
Yes. On-prem and air-gapped AI is our core work. We build GPU-scheduled Kubernetes on your own hardware — RKE2, OpenShift, or upstream — with private model registries (Harbor, Hugging Face mirror), no outbound internet dependency, encryption in transit and at rest, RBAC, and CIS/kube-bench hardening. This is the pattern regulated healthcare, financial-services, government, and defense buyers need for data sovereignty and ATO.
What does self-hosting an LLM actually cost versus a hosted API?
It depends on volume. Hosted APIs are cheapest at low, spiky usage; a self-hosted LLM wins as sustained token volume grows, because GPU cost is fixed while API cost scales linearly with tokens. We model your real usage against GPU capex/opex — including MIG partitioning, time-slicing, and autoscaling to keep utilization high — so the break-even is a number, not a guess. Many high-volume workloads cross over within the first year.
Do you build RAG and internal AI applications on top of the platform?
Yes. We stand up the retrieval-augmented-generation stack — embeddings, vector database, and orchestration — and work with your application owners to ship internal AI applications that serve real users on the private model. The platform is the foundation; the internal apps are the payoff.
Will our team own and operate the platform, or are we locked into you?
You own it. We are vendor-neutral platform engineers, not a managed-service trap. Every engagement ends with GitOps-managed infrastructure-as-code, runbooks, observability, and knowledge transfer so your team operates the private LLM platform independently. References and a certificate of insurance are available on request.
Ready to make AI operational?
Whether you're planning GPU infrastructure, stabilizing Kubernetes, or moving AI workloads into production — we'll assess where you are and what it takes to get there.
US-based team · All US citizens · Continental United States only