What is On-Premises LLM Deployment?

Run frontier-class language models on your own hardware with zero data leaving your network. Oaken AI provides on-premises llm deployment services for established businesses looking to implement AI that delivers measurable results.

Who needs on-premises llm deployment?

On-Premises LLM Deployment is designed for established businesses — professional services firms, local businesses, agencies, and e-commerce companies — that want to save time and reduce manual work through AI automation. If your team spends hours on repetitive tasks each week, this service can help.

How long does on-premises llm deployment take to implement?

Oaken AI delivers working systems in as little as 7 days. We start with your highest-impact bottleneck and build a functional system before expanding to other areas. No multi-month assessments or slide decks — just results.

Do I need technical expertise for on-premises llm deployment?

No. Oaken AI handles the entire technical implementation. You do not need to hire an AI team, learn to code, or understand machine learning. We build systems your existing team can use and maintain.

On-Premises LLM Deployment | Self-Hosted AI Models | Oaken AI

Why On-Premises LLMs

Open-weight models now rival proprietary APIs in capability. Deploying them on-premises gives you the same intelligence without sending a single token to an external server. For organizations handling protected health information, attorney-client privileged documents, classified material, or proprietary trade secrets, on-prem deployment is the onlyarchitecture that satisfies both legal counsel and security teams.

Zero Data Exfiltration

Every prompt, response, and intermediate computation stays within your physical network boundary. No API calls leave your facility. Air-gap compatible for the most sensitive environments.

Model Selection Freedom

Choose from any open-weight model at any parameter scale. Swap models without vendor lock-in. Fine-tune on proprietary data you would never upload to a third party.

Predictable Cost Structure

No per-token billing that scales unpredictably. One-time hardware investment plus electricity and maintenance. At high inference volumes, on-prem costs 3-10x less than API pricing over a 3-year horizon.

Full Customization Control

Apply custom system prompts, guardrails, output formatting, and domain-specific fine-tuning. No terms-of-service restrictions on use cases. Your models serve your business logic exclusively.

On-Prem LLM Deployment Pipeline

Assess

Workload profiling and model sizing

Provision

GPU hardware and networking

Optimize

Quantization and benchmarking

Deploy

Inference engine and API layer

Monitor

Performance and capacity tracking

Assess

Workload profiling and model sizing

Provision

GPU hardware and networking

Optimize

Quantization and benchmarking

Deploy

Inference engine and API layer

Monitor

Performance and capacity tracking

On-Premises LLM Architecture

Model Quantization and Optimization

Running a 70-billion parameter model at full FP16 precision requires 140 GB of GPU VRAM. Most organizations do not need that level of precision. Quantization compresses model weights to INT8, INT4, or even lower precision, reducing memory requirements by 2-4x with minimal quality degradation. The key is knowing which quantization method matches your accuracy requirements.

GPTQ quantization. Post-training quantization that compresses weights to 4-bit integers. A 70B model at GPTQ-4bit fits on a single NVIDIA A100 80GB card. Inference quality stays within 1-2% of the full-precision baseline for most enterprise tasks including summarization, extraction, and classification.

AWQ (Activation-Aware Weight Quantization). Preserves the most important weight channels during compression, resulting in better quality than naive quantization at the same bit width. Particularly effective for code generation and mathematical reasoning tasks where precision matters.

GGUF format for CPU+GPU hybrid. When GPU VRAM is limited, GGUF models can split layers between GPU and system RAM using llama.cpp. This enables running 70B models on hardware with only 24 GB of VRAM by offloading some layers to CPU. Throughput is lower but the cost savings can be substantial for low-volume workloads.

Hardware Requirements

The right hardware depends on your model size, concurrency requirements, and latency targets. We profile your actual workload before recommending hardware to avoid both over-provisioning and bottlenecks.

Single-GPU deployments. Models up to 13B parameters at FP16 or 70B at INT4 run well on a single NVIDIA A100 80GB or H100 80GB. Suitable for teams of 10-50 concurrent users with sub-second latency requirements.

Multi-GPU with NVLink. For 70B+ models at higher precision or higher concurrency, tensor parallelism across 2-8 GPUs connected via NVLink provides linear scaling. An 8xH100 node handles 405B-parameter models with throughput sufficient for hundreds of concurrent users.

Inference-optimized servers. Purpose-built servers from NVIDIA (DGX), Dell (PowerEdge XE9680), or Supermicro with optimized cooling, power delivery, and NVLink topology. We specify the exact SKU and configuration for your workload rather than defaulting to the most expensive option.

Who This Is For

On-premises LLM deployment is the right choice for organizations that cannot send data to external APIs under any circumstances. Healthcare systems processing PHI, law firms handling privileged documents, defense contractors working with CUI or classified data, and financial institutions with strict data residency requirements all benefit from this approach.

If your security team would reject a cloud AI proposal, on-prem is the path forward. We help you get from hardware procurement to production inference in weeks, not months.

On-Premises LLM Deployment