On-Premise LLM Deployment
FISTA Solutions deploys open-weight LLMs on your own hardware when data cannot leave your network: GPU capacity sizing from real traffic, a production serving stack, model selection against your tasks, monitoring and upgrade paths — plus an honest comparison against managed alternatives before you buy hardware.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does on-premise LLM deployment include?
On-premise deployments cover capacity sizing against real traffic, serving stack selection and configuration, model selection and evaluation on your tasks, gateway and application integration, monitoring and alerting, and the runbooks your team needs to operate it.
- 01
Capacity sizing
GPU count, memory, and batching characteristics derived from your traffic shape and latency targets.
Hardware - 02
Serving stack
Production inference serving with batching, quantization where appropriate, and scaling behavior configured.
Serving - 03
Model selection
Open-weight models evaluated on your actual tasks rather than chosen from leaderboards.
Models - 04
Integration
A gateway exposing an API applications use, so switching models later is configuration not code.
Integration - 05
Monitoring and runbooks
Utilization, latency, and quality monitoring with runbooks so your team can operate it without FISTA.
Operations
Requirements
Which requirements shape on-premise LLM deployment?
On-premise inference trades external dependency for operational burden. Requirements cover honest capacity sizing, the serving stack's real throughput under your traffic, model quality on your tasks, upgrade paths, and the staffing to run GPUs reliably.
| Requirement | Why it matters | How FISTA implements it |
|---|---|---|
| Capacity reality | Underestimating GPUs is the classic failure. | Sizing from measured traffic shape, context lengths, and latency targets, with headroom and a scaling plan. |
| Throughput under load | Benchmarks do not reflect your traffic. | Load testing with your real prompt and output length distribution, not synthetic uniform requests. |
| Model quality on your tasks | Leaderboards do not predict your results. | Open-weight candidates evaluated on your golden set before hardware commitments are finalized. |
| Upgrade path | Models improve faster than hardware amortizes. | Serving stack and gateway designed so model swaps do not require application changes. |
| Operational staffing | GPUs need people. | Runbooks, monitoring, and an honest statement of the operational load your team is accepting. |
Where AI fits
How should you sequence on-premise LLM deployment?
Decide on-premise deliberately: confirm the constraint is real, evaluate open-weight quality on your tasks, model total cost including operations, then pilot on modest hardware before committing capital.
- 01
1. Confirm the constraint
Is external inference genuinely prohibited, or is a private cloud arrangement sufficient?
- 02
2. Evaluate quality first
Open-weight candidates scored on your golden set, because capability must be adequate before economics matter.
- 03
3. Model total cost
Hardware, power, hosting, and staffing compared with managed inference at your real volume.
- 04
4. Pilot before capital
A modest pilot proves throughput and quality before a large hardware purchase.
- 05
5. Keep the gateway
An abstraction layer so a later move back to managed inference is configuration, not a rebuild.
Cost and timeline
What does on-premise LLM deployment cost, and how long does it take?
Cost is driven by GPU capacity, serving efficiency, and operational staffing; timeline by hardware procurement and data center access. FISTA does not quote blind: the scoping call returns a sizing model and a total-cost comparison.
The honest comparison comes first. At low and moderate volumes managed inference is usually cheaper once staffing and depreciation are counted; at sustained high volume or under hard residency constraints, on-premise wins. FISTA models both.
Hardware procurement is a long lead time. Discovery identifies what is needed and starts that process, because GPU availability rather than engineering usually sets the launch date.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI deployment?
FISTA deploys AI in four phases: an assessment that inventories workloads, data boundaries, and constraints and produces a target architecture; a platform build with networking, identity, secrets, and observability as code; a migration with evaluation gates and shadow traffic; and a production cutover with dashboards, budgets, runbooks, and rollback.
- 1
Assess and target
Workload inventory, data classification, latency and volume profile, compliance constraints, and a target architecture with cost model.
OutputTarget architecture, cost model
- 2
Build the platform
Networking, identity, key management, model endpoints, gateway, tracing, and evaluation pipeline delivered as infrastructure-as-code.
OutputPlatform as code, control matrix
- 3
Migrate with gates
Move applications behind the gateway, run evaluation and shadow traffic, and tune routing, caching, and capacity.
OutputEval reports, shadow results
- 4
Cut over and operate
Graduated production rollout, dashboards for quality, latency, and cost, runbooks, on-call, and a change process with rollback.
OutputProduction platform with SLOs
Why FISTA
Why choose FISTA Solutions for on-premise LLM deployment?
FISTA sizes on-premise deployments from your real traffic, evaluates open-weight quality before hardware is bought, and states the operational burden plainly. Work is contracted through a US entity with full IP assignment.
On-Premise LLM specifics
- The total-cost comparison against managed inference is produced before hardware is recommended, including staffing.
- Capacity is sized from your measured traffic shape and latency targets rather than from vendor benchmarks.
- Open-weight model quality is evaluated on your golden set before capital is committed.
- A gateway keeps model and even deployment-mode changes as configuration rather than application rewrites.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What platform teams ask before deploying AI.
Straightforward guidance for evaluating scope, fit, and the next step.
01Is on-premise LLM deployment cheaper?
At sustained high volume it can be, once utilization is high. At low or bursty volume it usually is not, because GPUs idle and staffing is a real cost. FISTA models both against your actual traffic before recommending.
02Are open-weight models good enough?
For many tasks yes, and the gap continues to narrow. Whether they are good enough for your tasks is an empirical question answered by evaluating candidates on your golden set, which FISTA does before hardware decisions.
03How many GPUs do we need?
That depends on concurrency, context lengths, output lengths, and latency targets. Sizing comes from your measured traffic rather than a rule of thumb, with headroom and a scaling plan included.
04Who operates it after deployment?
Your team, with runbooks and monitoring delivered, or FISTA under a managed operations retainer. The operational burden is stated plainly before you commit either way.
05How long does on-premise deployment take?
Engineering typically takes weeks to a couple of months; hardware procurement and data center access usually determine the actual timeline.
Scoped in writing before you commit
Decide on-premise with numbers, not instinct.
Bring your traffic, constraints, and tasks. The scoping call returns a sizing model, an evaluation plan, and a total-cost comparison.