On-Premises AI & Private LLM Platform

Q: Why run LLMs on-premises instead of using cloud APIs?

On-premises LLMs provide several critical advantages: Data privacy: Your data never leaves your infrastructure Compliance: Simplified regulatory compliance for GDPR, HIPAA, or industry-specific requirements Cost control: Predictable costs without per-token pricing that scales with usage Customization: Full control over model selection, fine-tuning, and optimization Performance: Consistent latency without internet dependency For organizations processing sensitive data or requiring high-volume AI capabilities, on premises AI deployment with a self hosted LLM often provides better economics and control than cloud alternatives.

Q: How do you ensure the AI platform remains secure?

Security is built into every layer: Network isolation: LLM infrastructure operates within your secure network perimeter Authentication & authorization: Integration with your existing identity systems Audit logging: Complete traceability of all AI requests and responses Data governance: No external API calls, no data leaving your infrastructure Model provenance: Verifiable model sources, scanning for vulnerabilities We implement security controls appropriate for your compliance requirements, whether that's financial services, healthcare, or government regulations.

Enterprise AI consulting — from GPU hardware to production-ready self hosted LLM platforms.

Generative AI is transforming how organizations analyze information, automate workflows, and interact with data. But most enterprises — especially in DACH — cannot use cloud-based LLM services due to data privacy restrictions, regulatory requirements, unpredictable costs, vendor lock-in, and model behavior risks.

Acosom provides end-to-end AI infrastructure consulting and builds fully private, on-premises or hybrid AI platforms based on open-source LLMs, GPU clusters, and secure MLOps pipelines — designed specifically for enterprise environments. We cover the entire stack: GPU hardware selection and MIG partitioning, model selection and quantization (GGUF, GPTQ, AWQ), inference server deployment (vLLM, TensorRT-LLM), RAG pipeline and vector database setup, and ongoing operations.

This is your AI capability. Running on your hardware. With your security posture.

What Your Organization Gains

Our enterprise AI consulting delivers a sustainable, governed AI capability that transforms your business operations while maintaining full control.

Full LLM Isolation — No Shared Models, No Leakage Risk

Your models run exclusively on your hardware, fully isolated at GPU, OS, and network levels. No workloads are shared with other customers, eliminating risks of prompt leakage, cross-tenant contamination, or model poisoning.

Enterprise-Grade Security & Compliance

All inference, embeddings, and vector data remain inside your perimeter. We align with DACH regulatory frameworks for finance, healthcare, energy, and public sector. You get auditability, RBAC, encryption, and policy enforcement across the entire AI stack.

Predictable, Transparent Cost Structure

Once deployed, the cost of inference becomes fixed and controllable. No token-based billing. No unpredictable cloud spikes. Perfect for long-term budgeting and strategic AI adoption.

High Performance & Low Latency

Local GPU inference provides faster responses, higher throughput, and zero external dependencies. Ideal for real-time automation, support agents, monitoring systems, and operational workflows.

Tailored AI Models Tuned to Your Business

We evaluate and optimize models to understand your terminology, workflows, and policies. Fine-tuned or instruction-adjusted LLMs become internal experts — far more accurate and reliable than generic cloud models.

A Sustainable, Governed Internal AI Capability

Beyond infrastructure, you gain a scalable AI foundation: MLOps & LLMOps best practices, governance & risk controls, model lifecycle management, integration with existing data/streaming pipelines, and support for private RAG and internal AI agents. Your organization becomes AI-ready — safely and sustainably.

Common Enterprise Use Cases

From documents to code—private AI enables practical automation across your organization.

Vision & Document AI

Process images, scanned documents, and visual data entirely on-premises. Replace legacy OCR with intelligent document understanding that extracts meaning, not just text. Automate invoice processing, contract analysis, technical drawing interpretation, and quality inspection—all without sending sensitive visuals to external APIs.

Voice & Audio AI

Speech-to-text, call transcription, meeting summarization—Whisper and similar models running on your infrastructure. Transcribe calls, generate meeting notes, and build voice interfaces without sending sensitive audio to external APIs. Perfect for any environment where conversations contain confidential information.

Code & Developer Assistance

Private Copilot alternative for your engineering teams using state-of-the-art open-weights models like GLM, DeepSeek-Coder, and GPT-OSS. Code completion, refactoring suggestions, documentation generation, and bug detection—all running locally. Your proprietary codebase never leaves your infrastructure while developers get the productivity boost of AI-assisted coding.

Multimodal Model Serving

We deploy and optimize vision-language models for enterprise document and image workflows.

Model Selection: Qwen-VL, InternVL, Paddle-VL, Gemma-3, Pixtral, GLM-V—evaluated for your specific document types and accuracy requirements.

Capabilities: Structured extraction from invoices, contracts, forms, and technical documentation with layout-aware processing. Visual inspection, diagram interpretation, product recognition, and defect detection.

Optimization: Vision model quantization, batched image processing, and efficient multimodal inference integrated via REST/gRPC endpoints.

Speech & Audio Serving

We deploy and optimize speech models for enterprise audio workflows.

Model Selection: Whisper (large-v3, turbo), Parakeet, Canary, Conformer—selected based on accuracy, latency, and language requirements.

Capabilities: Real-time and batch transcription, speaker diarization and identification, meeting summarization and action item extraction.

Infrastructure: GPU-accelerated inference with streaming endpoints for live transcription.

Code Model Serving

We deploy private code assistants that integrate with your development environment.

Model Selection: GLM, DeepSeek, Qwen-Coder, GPT-OSS.

Capabilities: Code completion and infilling, code review and bug detection, documentation generation, refactoring suggestions, unit test generation, natural language to code.

Integration: Agentic coding frameworks and IDE extensions. Fine-tune models to your codebase, coding conventions, and specific library versions—delivering suggestions that match how your team actually writes code.

Success Story

Enterprise AI That Actually Delivers

Moving from AI experimentation to production requires more than infrastructure — it demands AI infrastructure consulting from a partner who understands your compliance landscape, security requirements, and organizational readiness.

Acosom has helped organizations across banking, insurance, and manufacturing deploy self hosted LLM platforms that deliver measurable business value while maintaining full data sovereignty. Our consultants advise on which GPUs to buy, how many you need, how to partition them with MIG, which LLM to run, how to quantize it, and how to build the full platform around it — from inference server to RAG pipeline to MLOps.

What We Build for You — Technical Blueprint

From GPU architecture to agentic AI, we deliver the complete private LLM platform stack.

GPU Hardware Consulting & Architecture

GPU Selection: We advise on the optimal GPU vendor and architecture — NVIDIA A100, H100, L40S, or alternatives — based on inference vs. training needs, memory requirements, parallel workloads, and cost targets. This includes how many GPUs you actually need and whether MIG partitioning can reduce your hardware spend.

Server Design: We design optimal configurations including CPU choice & NUMA considerations, motherboard & chipset selection, NVMe storage layout, PCIe topology, and cooling & power design.

Your infrastructure becomes AI-optimized — right-sized, not over-provisioned.

AI Platform Runtime & Request Routing

Running private LLMs in production requires more than GPUs — it needs a reliable runtime layer.

Request routing & load balancing across GPUs or GPU partitions
Session-aware chat inference with preserved conversation context
Multi-GPU & multi-node inference where scale requires it
Isolation between applications and teams
This ensures internal AI systems behave like stable, enterprise services, not experimental demos.

GPU Partitioning (MIG) & Isolation

MIG lets one GPU become several isolated GPU instances, each with dedicated SMs, memory controllers, copy engines, and isolated error boundaries.

We implement MIG configuration, Kubernetes GPU Operator, GPU device plugin integration, CUDA visibility rules, and guidelines on when not to use MIG. This enables secure multi-tenant AI workloads inside your organization.

Model Selection & Quantization Consulting

We benchmark and validate open-source LLMs such as Qwen, DeepSeek, GLM, GPT-OSS, Mistral, and more — and determine the right quantization strategy (GGUF, GPTQ, AWQ, fp8/int8/fp4) to balance accuracy against hardware requirements.

Evaluation includes accuracy on your data, multilingual ability (DE/EN/FR), reasoning quality, and latency & throughput benchmarks. You choose the model and quantization level that fits your domain — not one tied to a cloud vendor.

Model Optimization

We maximize speed and reduce hardware requirements via TensorRT-LLM, vLLM optimized serving, quantization (fp8/int8/fp4/int4, AWQ/GPTQ), FlashAttention/PagedAttention, speculative decoding, and fine-tuning via LoRA/QLoRA.

Depending on your accuracy, throughput, and memory requirements, we apply weight quantisation, KV cache quantisation, or mixed-precision strategies as needed.

Model Serving Infrastructure

We build high-performance, secure model serving using vLLM, TensorRT-LLM, Ollama (Enterprise setup), and custom PyTorch servers.

Features include autoscaling, batching optimization, authentication, audit logging, token streaming, and monitoring dashboards. Your internal services can call AI with the same ease as an external API — but fully private.

Technologies & Tools for AI Platforms

The right technology stack enables scalable, high-performance private LLM deployments.

vLLM

High-throughput LLM inference engine with PagedAttention and continuous batching. Optimizes memory usage and maximizes GPU utilization for production LLM serving at scale.

TensorRT-LLM

NVIDIA’s optimized inference runtime for LLMs. Delivers peak performance on NVIDIA GPUs through kernel fusion, quantization, and multi-GPU/multi-node tensor parallelism.

Qwen

High-performance multilingual open-weight LLM with strong European language support. Excellent reasoning capabilities and available in sizes from 0.5B to 72B parameters for various deployment scenarios.

NVIDIA Nemotron

Enterprise-grade open-weight models optimized for business applications. Strong instruction-following, factual accuracy, and specialized variants for different use cases.

Why Choose Acosom

Why run LLMs on-premises instead of using cloud APIs?

On-premises LLMs provide several critical advantages:

Data privacy: Your data never leaves your infrastructure
Compliance: Simplified regulatory compliance for GDPR, HIPAA, or industry-specific requirements
Cost control: Predictable costs without per-token pricing that scales with usage
Customization: Full control over model selection, fine-tuning, and optimization
Performance: Consistent latency without internet dependency

For organizations processing sensitive data or requiring high-volume AI capabilities, on premises AI deployment with a self hosted LLM often provides better economics and control than cloud alternatives.

Which open-source LLMs do you recommend?

The best model depends on your specific use case. We evaluate and benchmark:

Qwen: Strong multilingual performance, excellent multimodal capabilities, reliable structured output
DeepSeek: Strong reasoning capabilities, competitive coding and math performance
GLM: Top-tier agentic coding, multi-step reasoning, excellent tool use and UI generation
GPT-OSS: OpenAI’s open-weight reasoning models, strong tool use and agentic tasks
Mistral/Mixtral: Well-established models with strong community support, efficient MoE architecture

We benchmark each model on your actual data and use cases, measuring accuracy, latency, and resource requirements before recommending a specific model.

What hardware is required to run LLMs on-premises?

Hardware requirements vary significantly based on several factors:

Model selection: Different models have different memory and compute requirements
Quantization strategy: fp8/int8/fp4/int4 quantization can dramatically reduce memory needs
Throughput requirements: Higher request volume may require additional GPUs or load balancing
Use case: Chat inference, batch processing, and RAG workloads have different resource profiles

We evaluate your specific requirements and optimize accordingly. Through quantization, efficient serving, and proper model selection, many production LLM deployments run on modest hardware configurations rather than expensive multi-node clusters. We right-size infrastructure to your actual needs, not theoretical maximums.

Can we fine-tune models for our specific domain?

Yes. Fine-tuning adapts open-source models to your specific use cases, terminology, and domain knowledge. We implement:

LoRA/QLoRA: Efficient fine-tuning with minimal resource requirements
Domain adaptation: Training on your documents, knowledge bases, and examples
Evaluation: Measuring accuracy improvement on your specific tasks

Fine-tuning improves accuracy on domain-specific tasks and enables the use of smaller, specialized models. This reduces costs and latency while maintaining or improving accuracy, and keeps sensitive data private and under full on-premises control.

How long does it take to deploy an on-premises LLM platform?

A production-ready on premises AI platform typically takes 8-14 weeks:

Weeks 1-3: Use case definition, model evaluation, hardware sizing
Weeks 4-6: Infrastructure setup, model optimization, initial deployment
Weeks 7-10: Integration with existing systems, fine-tuning (if needed)
Weeks 11-14: Production deployment, monitoring, documentation

Proof-of-concept deployments demonstrating specific capabilities are possible in 2-3 weeks.

How do you ensure the AI platform remains secure?

Security is built into every layer:

Network isolation: LLM infrastructure operates within your secure network perimeter
Authentication & authorization: Integration with your existing identity systems
Audit logging: Complete traceability of all AI requests and responses
Data governance: No external API calls, no data leaving your infrastructure
Model provenance: Verifiable model sources, scanning for vulnerabilities

We implement security controls appropriate for your compliance requirements, whether that’s financial services, healthcare, or government regulations.