Your private AI.
Your building.
Your data.
Run Llama, Mistral, DeepSeek, or any open-weight LLM on hardware you own. No API bills. No data leaving your network. No vendor dependency. eRacks ships it pre-configured and ready on day one.
Six reasons serious organizations
stop renting and start owning
Cloud AI worked fine for experimentation. Now that AI is in your workflows, the economics and risks have shifted.
Data sovereignty
Your prompts, documents, and outputs never leave your infrastructure. Critical for HIPAA, GDPR, SOC 2, and attorney-client privilege.
Predictable cost
One hardware investment replaces unpredictable monthly API bills. At scale, on-premise is up to 18× cheaper per million tokens.
Zero-latency responses
No network hop, no rate limits, no throttling. Local inference is faster than cloud for interactive applications.
Full customization
Fine-tune on your domain data. Modify system prompts freely. Integrate directly with your internal tools — no API restrictions.
Air-gap capable
Works completely offline. Ideal for secure facilities, classified environments, or locations with restricted internet access.
No vendor lock-in
Switch models freely. Move from Llama to Mistral to Qwen in minutes. Your infrastructure is yours — not a subscription.
Which GPU for which models?
| GPU / VRAM | Model Size Range | Example Models | Tier |
|---|---|---|---|
| RTX 4090 — 24GB | 7B – 32B | Llama 3.3 8B, Qwen 2.5 Coder 32B, Phi-3 Medium, Mistral 7B | Entry |
| RTX 5090 — 32GB | 7B – 70B (Q4) | DeepSeek-R1 32B, Llama 3.3 70B (quantized), Qwen 2.5 72B | Entry+ |
| RTX 6000 Ada — 48GB | 30B – 70B (full) | Llama 3.3 70B (full), Qwen 2.5 72B, DeepSeek-R1 70B | Pro |
| 2× RTX 6000 Ada — 96GB | 70B – 180B | Llama 3 405B (quantized), large MoE models, multi-modal | Pro+ |
| 4× H100 — 320GB HBM | 70B – 405B (full) | Llama 3 405B, fine-tuning any 70B+, custom training runs | Research |
Purpose-built for every
AI workload and budget
Private AI infrastructure for teams that cannot send data to the cloud - choose the model that matches your scale. Every system is custom-configured: pick the drives, networking, RAID, and OS that fit your workload.
The fastest path to a private AI assistant. Single GPU, 1U rackmount or desktop. Run 7B-32B models out of the box.
- Form factor1U rackmount or desktop
- CPUAMD Ryzen 9 / Threadripper
- GPU1x RTX 4090 / 5090 (24-32GB)
- RAM64-128GB DDR5
- Storage2TB NVMe
- Pre-installedOllama - Open WebUI - Ubuntu 26.04
2U rackmount Threadripper Pro with up to 3 GPUs. Handles 70B models, RAG pipelines, and multi-user inference at full throughput.
- Form factor2U Rackmount
- CPUAMD Threadripper Pro
- GPU1-3x RTX 6000 Ada / PRO 6000
- RAM128-256GB DDR5 ECC
- Storage2-8TB NVMe + ZFS option
- Pre-installedOllama - vLLM - Open WebUI - PyTorch
4U enclosed rackmount with 4 full-size GPUs. Production inference, fine-tuning, and team-scale RAG. The professional sweet spot.
- Form factor4U Rackmount
- CPUAMD EPYC Genoa
- GPU1-4x RTX PRO 6000 / H100
- RAM256-512GB DDR5 ECC
- StorageHigh-density NVMe RAID
- Pre-installedPyTorch - vLLM - Qdrant - Jupyter
Open-frame mining-style chassis with 6 full-size GPUs. PCIe 5.0 bifurcation risers, EPYC Genoa native lanes - no PLX switch overhead.
- Form factorOpen-frame chassis
- CPUAMD EPYC 9004 (Genoa, 128 PCIe 5.0 lanes)
- GPU6x RTX PRO 6000 / H100
- RAM256-768GB DDR5 ECC
- RisersJMT bifurcation x16 -> 2x x8
- StatusIn development - Q3 2026 GA
Maximum density open-frame: 8 GPUs on EPYC Genoa native PCIe 5.0 lanes. For training and large-scale inference where bandwidth matters.
- Form factorOpen-frame chassis
- CPUAMD EPYC 9004 (Genoa, 128 PCIe 5.0 lanes)
- GPU8x RTX PRO 6000 / H100
- RAM512GB-1TB DDR5 ECC
- RisersJMT bifurcation x16 -> 2x x8
- StatusIn development - Q3 2026 GA
4U enclosed flagship: 4-8 GPU training server with full ML stack. Fine-tune your own models on your own data, in your own datacenter.
- Form factor4U Rackmount (enclosed)
- CPUDual AMD EPYC
- GPU4-8x H100 / RTX PRO 6000
- RAM512GB-2TB DDR5 ECC
- StorageNVMe RAID + ZFS
- Pre-installedPyTorch - cuDNN - Jupyter - W&B
Industries choosing on-premise AI
Contract & Document Analysis
Analyze contracts, flag liability clauses, and summarize case files — without sending a word to OpenAI.
HIPAA-Compliant AI
Clinical notes, patient intake, and records review on a server that never touches the public internet.
Private Financial Intelligence
Client data, portfolio analysis, and internal knowledge bases — fully contained inside your perimeter.
Private Code Assistant
Qwen 2.5 Coder and DeepSeek-R1 run locally as your team's code assistant. No IP ever leaves your network.
Air-Gapped Deployments
Fully offline AI for classified or restricted environments. No internet required, ever.
Model Fine-Tuning
Domain-specific model training on proprietary datasets. LoRA/QLoRA fine-tuning on your own hardware.
Local Image & Video AI
Stable Diffusion, video generation, and multimodal workflows running on dedicated hardware you own.
Private Campus AI
Student and faculty AI tools hosted entirely on-premise, without sending academic work to third parties.
What people ask before
buying their first AI server
Can I run ChatGPT-quality AI on my own server?
Yes. Open-weight models like Llama 4, Qwen 2.5, and DeepSeek-R1 match or exceed GPT-3.5 performance on most tasks — and approach GPT-4 on coding and reasoning benchmarks. A single RTX 4090 (24GB) comfortably runs 7B–32B models. The eRacks AIDAN with RTX 6000 Ada handles 70B models at full precision.
What open-source LLMs can I run?
Any model available in GGUF or HuggingFace format: Llama 3/4, Mistral, Mixtral, Qwen 2.5, DeepSeek-R1, Phi-3, Gemma 2, Command-R, and hundreds more. Ollama, which we pre-install, lets you pull and run any compatible model with a single command. New models release weekly and you can run them immediately.
Is this HIPAA or GDPR compliant?
An on-premise AI server is the strongest available architecture for compliance. When inference runs locally, no PHI, PII, or sensitive data ever leaves your network. eRacks ships Ubuntu 26.04 LTS with no telemetry or proprietary software — clean, auditable infrastructure. You should still consult your compliance team, but the data-sovereignty question is definitively answered by keeping everything on-premise.
How much do I save vs. cloud API costs?
At high usage volumes, on-premise inference is up to 18× cheaper per million tokens than premium cloud APIs. A team sending 10 million tokens per day to GPT-4 might pay $15K–$30K per month. A one-time eRacks server at $11,082 pays for itself in 30–60 days — then runs for years.
Do I need IT staff to manage this?
Not necessarily. The AILSA and AIDAN ship with Ollama and Open WebUI pre-installed, so non-technical staff can use them via a browser interface from day one. We configure Ubuntu for stability and low maintenance. For larger deployments with vLLM or RAG pipelines, basic Linux familiarity helps — or we can provide setup consulting.
Can you pre-install a specific LLM for me?
Yes. Tell us which model you want running at first boot and we'll configure it. Just add a note to your quote request. We can also pre-load multiple models and configure Open WebUI with your branding and default settings.
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