Private & On-Prem AI Platforms

AI infrastructure and platform software under your control — from simple on-prem deployments to enterprise-scale GPU platforms.

Not every organization needs a complex AI cluster. But every organization that works with sensitive data, regulated workloads, or proprietary models needs control.

Private AI platforms are about ownership, isolation, predictability, and operability — whether you run a single self-hosted model on-prem or operate a multi-tenant AI platform serving chats, applications, and agent systems across teams.

Acosom works with platform architects, infrastructure engineers, and technical decision-makers to design and operate private AI platforms that run reliably in production.

This expertise is about control, not just deployment.

What Organizations Gain

When AI platforms are designed for control, predictability, and operability.

Data Sovereignty & Compliance

Sensitive data remains within controlled environments and jurisdictions. Models, prompts, embeddings, and inference behavior stay under organizational control.

Model & IP Protection

Models, prompts, embeddings, and inference behavior are part of organizational intellectual property. Private platforms protect these assets from external exposure.

Cost Predictability

Token-based pricing does not scale well for sustained or high-volume workloads. Private infrastructure provides predictable cost envelopes for production AI.

Performance & Latency Control

Real-time, user-facing, and system-integrated AI use cases require controlled latency. Private platforms eliminate external API dependencies.

Deployment Flexibility

Choose between single-node on-prem, private cloud, trusted regional providers, or hybrid setups. Infrastructure matches regulatory and operational constraints.

Control Plane & Operability

Clear separation between model lifecycle management, access control, policy enforcement, and inference execution. Platforms designed to be operated long-term.

Why Organizations Build Private AI Platforms

Public AI APIs are convenient — until constraints appear.

Organizations invest in private AI platforms when they need data sovereignty and compliance, model and IP protection, cost predictability, performance and latency control, and independence from hyperscalers.

Private AI platforms are not anti-cloud by default — they are control-first. The key decision is not cloud versus on-prem, but where AI is allowed to run — and under which controls.

Deployment Models: On-Prem, Private & Regional Cloud

Private AI does not imply a single deployment model.

Single-Node On-Prem Deployments

Ideal for smaller organizations or focused use cases. One or two GPUs, one model, minimal routing, and full control. Often easier to operate, closer to the hardware, and more transparent to debug.

Private Cloud Deployments

AI workloads running on controlled infrastructure with standardized operations. Provides scale while maintaining organizational control over data and models.

Trusted Regional Cloud Providers

Used where data residency, legal jurisdiction, and regional independence matter. National or regional providers instead of global hyperscalers.

Hybrid Setups

Different environments for fine-tuning and inference, or regional isolation by country. Enables flexibility while respecting regulatory and operational boundaries.

Platform Architecture: Control Plane vs Inference Plane

A private AI platform is not “a model on a server” — it is a platform with clear separation of responsibilities.

Control Plane

Responsible for model lifecycle management, access control and authentication, policy enforcement, versioning and rollout, and auditability and traceability. Applications consume inference as a service without managing models or GPUs directly.

Inference Plane

Responsible for serving models on GPU-backed infrastructure, handling requests at scale, isolating workloads between teams or tenants, and delivering predictable latency and throughput.

Compute, GPU & Inference Topology

This is where private AI becomes real engineering.

Simple Setups for Small & Mid-Sized Organizations

Many organizations do not need a cluster. For smaller workloads, a single GPU server with one or two models, simple access control, and no multi-node routing is often easier to operate and entirely sufficient.

GPU Virtualization Limits

GPUs cannot be virtualized like CPUs. Fine-grained sharing happens inside a node, not across virtual machines. One GPU is typically assigned to one workload or partition. Technologies such as NVIDIA MIG allow partitioning within a GPU, but do not remove the need for careful platform design.

When Clusters Become Necessary

Clusters are introduced when models exceed a single GPU, throughput must scale horizontally, multiple teams share infrastructure, or high availability is required. This leads to pools of GPU-backed nodes, separation of fine-tuning and inference resources, and explicit scheduling and routing layers.

Model Selection, Optimization & Serving

Private platforms work best with open-weight models.

Model Strategy

We apply a pragmatic approach: evaluate models programmatically under real workloads, systematically compare results from different LLMs, benchmark accuracy, latency, and resource usage, and avoid “one model fits all” assumptions. This includes models such as Qwen and NVIDIA Nemotron.

Optimization & Serving

Production-grade inference typically involves quantization, batching and request shaping, model-aware scheduling, and optimized runtimes such as vLLM or TensorRT-LLM. Models are deployed independently, enabling safe upgrades, rollbacks, and isolation between teams.

Inference Routing & Network Layer

Once more than one GPU or node is involved, routing becomes mandatory.

Request Routing

The routing layer directs requests to the correct model instance. Different models, versions, and configurations may run on different nodes. Routing decisions are based on model requirements, not just availability.

Load Balancing

Balances load across GPUs and nodes. AI-aware balancing considers GPU memory, current batch sizes, and model-specific characteristics. Not just round-robin HTTP distribution.

Tenant & Model Isolation

Enforces tenant and model isolation. Ensures workloads from different teams or applications do not interfere. Critical for multi-tenant platforms and regulatory compliance.

Session Affinity

Maintains session affinity when required. Stateful interactions and conversation history benefit from consistent routing to the same inference instance. Enables warm caches and context reuse.

Backpressure & Rate Limits

Applies backpressure and rate limits. Protects GPU resources from overload. Ensures fair resource allocation across consumers and prevents cascading failures.

Data Access, Memory & Security Boundaries

Running a usable chat or agent platform requires more than inference.

Memory Beyond a Single Session

Conversational systems and agents require context across interactions. Session memory handles short-term state, while long-term user and domain memory persists knowledge across sessions using structured databases, vector databases for semantic recall, and graph databases for relationships and history.

RAG as an Access Pattern

Retrieval-augmented generation (RAG) is an access pattern, not a feature toggle. It allows the platform to control what data is exposed to the model, enforce governance and regional restrictions, and audit AI outputs. This ensures private AI does not become a new data-leak vector.

Operating Local Chat & Agent Platforms

User-facing systems for local chats and internal AI assistants require correct configuration of memory stores, strict access control and identity integration, controlled model backends, and lifecycle management. Running a fully local chat platform is a software platform problem, not just a hardware setup.

Operability, Cost & Lifecycle Management

Running AI in production is an operational challenge.

Production Observability

We design for GPU utilization and saturation metrics, latency and throughput observability, model version tracking, predictable cost envelopes, safe upgrades and rollback, and incident isolation and response.

Enterprise-Scale Operations

At larger scale, this includes rolling updates of GPU nodes, draining and replacement strategies, and capacity planning for inference pools. This is where private AI platforms move from demos to operable systems.

Technologies

Technologies support private AI architecture — they do not define it.

NVIDIA GPUs

GPU acceleration for AI workloads. Foundation for inference and training. NVIDIA MIG enables GPU partitioning within a single device for multi-tenant workloads.

Qwen

Open-weight language models. Used for private LLM deployments across multiple languages and use cases. Strong performance and local control.

NVIDIA Nemotron

NVIDIA open-weight models. Optimized for enterprise use cases. Designed for integration with NVIDIA inference infrastructure.

vLLM

High-performance inference runtime. Optimized for serving large language models. Supports batching, quantization, and efficient GPU utilization.

TensorRT-LLM

NVIDIA inference optimization framework. Provides optimized model serving with reduced latency and increased throughput. Deep integration with NVIDIA hardware.

Kubernetes

Container orchestration platform. Used for deploying and managing AI infrastructure at scale. Enables GPU node pools and scheduling.

Pinecone

Vector database for semantic search. Used for RAG systems and memory layers. Managed and self-hosted options available.

Milvus

Open-source vector database. High-performance semantic search for AI applications. Supports large-scale embedding storage and retrieval.

Weaviate

Vector database with native AI integration. Supports hybrid search and GraphQL queries. Used for building AI-native applications.

Qdrant

High-performance vector database. Rust-based implementation optimized for speed and efficiency. Supports filtering and hybrid search.

Neo4j

Graph database for relationship modeling. Used for knowledge graphs and evolving user profiles. Native graph queries and traversal.

Apache JanusGraph

Distributed graph database. Scalable graph storage and traversal. Used for large-scale relationship and lineage tracking.

Amazon Neptune

Managed graph database. Supports both property graph and RDF models. Used when graph workloads remain in AWS.

PostgreSQL

Relational database for structured data. Used for session stores, user metadata, and explicit facts. Foundation for many AI platform data layers.

Redis

In-memory data store. Used for session caching, rate limiting, and temporary state. Essential for high-performance AI platforms.

MongoDB

Document database for flexible schemas. Used for conversation history, configuration, and semi-structured AI metadata.

LibreChat

Open-source chat platform. Provides session handling, memory, and UI components. Designed for private LLM deployments.

Open WebUI

Local AI chat interface. Supports multiple models and backends. Built for self-hosted and private environments.

How This Expertise Is Applied

This expertise underpins private LLM deployments in regulated environments, sovereign and regional AI platforms, enterprise AI foundations serving multiple teams, fully local chat and assistant platforms, and AI workloads migrated away from hyperscalers.

It integrates naturally with:

Frequently Asked Questions

Do we really need our own AI infrastructure?

Not always — but there are clear cases where private infrastructure becomes necessary.

You likely need private AI when:

Your data is regulated or sensitive (GDPR, healthcare, financial)
Models or prompts contain proprietary intellectual property
Token costs for sustained usage become prohibitive
Latency and performance are critical for user experience
You need independence from specific cloud vendors

You might not need it when:

Your use case is exploratory or low-volume
Data sensitivity is minimal
Token-based pricing is acceptable
External API dependencies are not a concern

We help organizations make this decision based on actual constraints, not trends.

What's the smallest viable private AI setup?

Many organizations start with a single GPU server.

A minimal viable setup includes:

One or two NVIDIA GPUs (e.g., NVIDIA RTX 5090 / RTX Pro 6000, AMD Radeon AI PRO R9700, NVIDIA DGX Spark)
One open-weight model (e.g., Qwen or Nemotron)
Simple access control and routing
Basic monitoring and lifecycle management

This is often sufficient for:

Internal chat systems for small teams
Proof-of-concept AI applications
Regulated use cases with limited scale

You do not need Kubernetes or multi-node clusters to start.

How do you handle model memory and conversational state?

Memory is layered: session memory and long-term memory.

Session memory:

Short-term conversational state reconstructed per request
Includes conversation history, tool outputs, temporary summaries
Typically held in-memory or fast caches (Redis)

Long-term memory:

Persisted knowledge spanning sessions
Stored in structured databases (explicit facts, rules, permissions)
Vector databases for semantic recall
Graph databases for relationships and evolving profiles

The model itself remains “stateless” besides the KV-Cache. Relevant memory is queried, curated, and injected into the prompt at inference time based on identity, intent, and policy constraints.

What is the role of RAG in private AI platforms?

RAG is an access pattern, not a feature toggle.

RAG allows the platform to:

Control what data is exposed to the model
Enforce governance and regional restrictions
Provide auditability and reasoning about AI outputs
Prevent private AI from becoming a new data-leak vector

In practice, RAG systems involve:

Controlled document ingestion and indexing
Policy-based retrieval filtering
Explicit access boundaries per user and role
Audit trails for what was retrieved

This ensures AI remains compliant with organizational policies.

When do you need GPU clusters instead of single servers?

Clusters are necessary when single servers are insufficient.

You need clusters when:

Models exceed single-GPU memory (e.g., large foundation models)
Inference throughput requires horizontal scaling
Multiple teams share infrastructure
High availability and redundancy are required

Single servers are sufficient when:

Models fit comfortably on up to 8 GPUs
Workload is focused and predictable
Operational simplicity is more valuable than scale

We design for the smallest architecture that meets actual constraints.

Can you help with platforms that are already running?

Yes. Many of our engagements involve improving existing AI platforms.

Common improvement areas:

Adding proper control planes and lifecycle management
Introducing memory and state management for chats and agents
Implementing multi-tenancy and isolation
Optimizing inference performance and GPU utilization
Adding observability and cost tracking
Migrating from external APIs to private infrastructure

We assess current architecture, identify gaps, and evolve platforms incrementally.

Building private AI platforms that must run reliably in production? Let’s talk about your infrastructure and control requirements.

Discuss Your AI Platform