On-Prem LLM Deployment Consulting
Deploy LLMs inside the firewall when regulated data cannot leave — with serving, identity, reconstructable traces, and eval gates your operators can run.
- Service
- LLM Platform
- Industry
- Enterprise
- Updated
- 2026-08-25
- Engagement
- 4 wks
On-prem LLM deployment consulting designs and ships language-model serving inside your firewall or air-gapped environment — model selection, GPU sizing, identity, reconstructable traces, and eval gates — typically in four weeks when hardware is already in place, with you owning the IP and no data leaving for shared training.
Why teams pick this engagement
LLM Platform × EnterpriseData stays inside
Inference, logs, and evals run on infrastructure you control. No shared training. Zero-retention on our side. SOC 2-aligned engagement practices.
Capacity you can defend
We size tokens, concurrency, and GPU SKUs against a measured golden set — not a vendor slide about tokens per second on a demo prompt.
Serving you operate
vLLM or equivalent, model registry, secrets, and rollback in your datacenter or private cloud. Client owns IP and runbooks at handover.
Platform and security jointly
Network zones, identity, patching, and on-call are designed with the people who will get paged. We do not leave a science cluster behind.
Four weeks if metal exists
Standard implementation is four weeks when GPUs and network path are already approved. Procurement and financial-services review typically add time to 8–12 weeks.
Reconstructable on-prem traces
Traces stay on your side of the firewall: input class, model version, tools, output. An auditor should not need internet to replay a decision.
Key takeaways
- 01
On-prem is for data and residency rules that VPC-hosted private serving still cannot meet, not a default for every regulated industry.
- 02
Hardware, quantization, and context length must be sized on your golden set; blog tokens-per-second numbers will not survive production concurrency.
- 03
Inside-the-firewall models still need identity, tool allowlists, traces, rubric judges, and CI gates.
- 04
Client owns IP, configs, and evals. Work stays on your infrastructure. Zero-retention, no shared training, SOC 2-aligned process.
- 05
Four weeks is standard when GPUs exist; financial-services review and hardening typically take 8–12 weeks.
What the engagement covers
How we work
- 01
Discover
Data residency rules, existing GPU estate, identity, and whether on-prem is required versus private VPC serving.
- 02
Design
Serving topology, network, secrets, eval harness, and capacity envelope reviewed with platform and security.
- 03
Build
Bring up inference, traces, and the eval suite on your metal or private cloud with weekly operator sessions.
- 04
Validate
Load, failure injection, reconstructable traces, and a no-egress check. Package evidence for second-line review.
- 05
Enable
Handover of registry, runbooks, and CI so your team can patch, pin, and roll back without a vendor on the call.
Take the playbook with you
The working documents from real engagements — free, in exchange for an email. They’re useful whether or not we ever talk.
On-Prem LLM Hardware and Serving Worksheet
GPU class, context length, concurrency, and quantization trade-offs sized against a real workload rather than a blog benchmark.
Get the worksheet ·Firewall LLM Deployment Runbook Outline
Network, secrets, registry, rollback, and eval gates — the operating document your platform team inherits.
Get the outline ·