On-Premise AI Compute Appliance

Zharis

The power of a cloud — on your own infrastructure.

A rackable hardware appliance that runs performant large language models locally. Cloud-class AI compute in a single rack unit — installed in your datacenter, governed by you, with your data never leaving your walls.

1URack footprint 10 Gb/sI/O fabric 0Cloud dependency
Zharis — 1U on-premise AI compute appliance
What it is

A rackable box that puts AI inside your walls.

Zharis is hardware — a self-contained AI compute appliance you slot into your rack like any other server. It runs large language models locally so your organisation gets cloud-class AI without sending a single byte to a third party.

Rackable hardware

A single 1U unit engineered to mount in a standard 19" rack — the AI appliance is the product, not a cloud subscription.

Runs LLMs locally

Performant language models execute on the appliance itself — assistants, RAG, analysis and automation, with no API call leaving your network.

Your data stays in

Every prompt, document and inference stays inside your infrastructure — sovereignty and compliance by architecture, not by policy.

Cloud-class power

The compute density and bandwidth of a cloud node — a 10 Gb/s I/O fabric and dedicated AI acceleration — in a footprint you own and operate.

Engineered end-to-end by Thelis — born from the ReconnAIssance research program.

Specifications

Under the hood.

Configurations

NVIDIA Jetson AGX Orin 32GB 32 GB Unified LPDDR5
NVIDIA Jetson AGX Orin 64GB 64 GB Unified LPDDR5
NVIDIA Jetson AGX Thor 128 GB Unified LPDDR5

Run any of the well-known open models locally — from Gemma on the NVIDIA Jetson AGX Orin, up to GPT-OSS-120B on the NVIDIA Jetson AGX Thor. The more unified memory, the larger the model you can serve.

Form factor & mechanical

Rack units1U
Dimensions (W×D×H)On request
WeightOn request
Mounting19" rack standard

Compute

PlatformNVIDIA Jetson AGX Orin · NVIDIA Jetson AGX Thor
GPUIntegrated NVIDIA GPU + Tensor Cores
CPUArm-based (Jetson SoC)
AccelerationNVIDIA TensorRT

Memory & storage

Unified memory32 / 64 GB (AGX Orin) · 128 GB (AGX Thor)
Memory architectureUnified — CPU/GPU shared LPDDR5
StorageOn request

Network & I/O

Network10 GbE
PortsOn request
ManagementOn request

Power & thermal

Power supplyOn request
Power draw (typ. / max)On request
CoolingOn request
Operating temperatureOn request

AI & software

Max model sizeMemory- & precision-bound — up to gpt-oss 120B (AGX Thor)
Supported modelsAll classes — LLM, vision, audio, embeddings
LLM runtimesTensorRT-LLM, vLLM, Ollama
FrameworksPyTorch, TensorFlow, ONNX · Docker
OSUbuntu-based Linux (NVIDIA Jetson)
ThroughputModel- & GPU-dependent — benchmarked per deployment
APIOn request

Compliance

CertificationsOn request
Data postureGDPR / NIS2 ready

Final specifications confirmed per deployment. Contact us for the full datasheet.

What you can run

Real AI workloads, entirely local.

Private assistants

Internal copilots and chat assistants grounded in your own knowledge — available to your teams without exposing anything to a public model.

RAG over private documents

Retrieval-augmented generation across your confidential corpus — contracts, records, archives — searchable and queryable, on-premises.

Document & data analysis

Summarisation, extraction, classification and structuring of large document sets — turning unstructured data into usable signal.

Workflow automation

Embed local inference into your processes — triage, routing, generation and decision support — without a recurring per-token cloud bill.

Why on-premise

The AI you need, without the exposure.

Data sovereignty

GDPR, NIS2 and sector-specific compliance from day one. No data transmitted externally — your regulatory posture stays clean and auditable.

Deterministic latency

No network round-trips, no shared-infrastructure contention. Inference happens locally, at consistent speed — independent of external conditions.

Total control

You choose the models, set the access rules and own the update schedule. No vendor lock-in, no surprise pricing — the system evolves as you decide.

Deploy & integrate

Rack it. Connect it. Call the API.

01

Rack

Mount the 1U appliance in your existing rack and power it on — no datacenter overhaul, no specialised infrastructure required.

02

Connect

Attach it to your private network. Everything runs inside your perimeter — the appliance never needs outbound internet access to operate.

03

Integrate

Point your applications at the local API and start serving inference. It slots into your stack like any internal service.

Optional — fully managed

Want it deployed, audited and maintained for you?

Zharis runs perfectly on its own. If you would rather have Thelis handle implementation, quarterly audits and ongoing maintenance, the Local AI Bundle wraps the appliance in a complete managed engagement.

Book a demo

See Zharis run AI
inside your walls.

Tell us about your infrastructure and your AI use case, and we will set up a live demonstration of the appliance running your kind of workload — on-premises.