Stop paying
$1,200/mo
for AI you
could own.
The average SMB team paying for ChatGPT, Claude, Copilot, and Perplexity
spends $800–$2,400/month — money that evaporates forever.
An eRacks AI server pays for itself in under a year, then runs for free.
We build it. We pre-install Ubuntu, Ollama, and your models. You ship it.
Your team is running private AI on day one.
After 3 years: cloud = $26,820 spent.
eRacks = $12,995 spent, server still running.
At higher API usage? Break-even moves to 6–10 months.
From order to running AI
in under a week
We do the configuration work. You receive a server that's ready to use, not a pile of parts to assemble.
Tell us your use case
Document analysis? Code assistant? Image generation? Customer support? Each use case maps to a specific GPU tier, RAM requirement, and pre-installed model. Fill out the quote form or call us — we'll recommend the right config.
We build and configure it
We assemble your server, install Ubuntu 24.04 LTS, CUDA drivers, Ollama, Open WebUI, and pull the model(s) you want. Everything is tested and running before it ships. You don't touch a command line unless you want to.
Plug in and browse to your AI
Connect power and ethernet. Your server is on your network. Open a browser on any computer in the office and go to http://your-server:3000 — Open WebUI greets you with a ChatGPT-style interface, backed by your private LLM.
Pull new models any time
The AI landscape moves fast. New models come out weekly. On your eRacks server, adding a new model is: ollama pull llama4:70b. Done. No waiting for your vendor to support it, no price increase, no request to send.
The best open-weight models
for business use in 2026
All of these run on eRacks hardware. We pre-install whichever you choose.
The benchmark standard. Excellent general assistant, strong reasoning, broad knowledge. 70B matches GPT-4 class on most business tasks.
The leading open-source coding model. Outperforms heavily quantized 70B models on code tasks. Fits in 24GB VRAM (32B Q4). GPT-4o-level coding.
Chain-of-thought reasoning model. Exceptional at math, logic, and structured analysis. Transparent thinking process — you see how it reaches conclusions.
Fast, efficient, and highly capable for its size. Mistral 7B is the best lightweight model for high-throughput applications like customer-facing chatbots.
Punches above its weight class. Strong at summarization, document QA, and instruction following. Excellent for document-heavy workflows on modest hardware.
Google's open-weight offering. Strong multilingual performance and solid instruction following. Good choice for customer-facing applications needing wide language coverage.
eRacks vs. cloud AI subscriptions
| ChatGPT / Claude / Copilot | eRacks AI Server | |
|---|---|---|
| Monthly cost | $20–$30/user/month forever | $0/month after purchase |
| Data privacy | Prompts sent to external servers | Everything stays on your hardware |
| HIPAA / GDPR compliance | Requires BAA, still external | Air-tight: no external data transfer |
| Model selection | Vendor's choice only | 100+ open-weight models, any time |
| Rate limits | Yes, even on paid plans | None — it's your hardware |
| Custom fine-tuning | Not available (or expensive API) | Full LoRA/QLoRA fine-tuning included |
| Works offline | No | Fully air-gapped capable |
| Vendor lock-in | Completely | Zero — open source stack |
| OS | No access | Ubuntu 24.04 LTS, full root |
Common questions from
small business owners
What exactly is an "open source AI server"?
It's a Linux server (Ubuntu, in our case) running open-weight AI models locally using free software. "Open-weight" means the model weights are publicly available — anyone can download and run Llama, Mistral, or Qwen without paying a license fee. We pre-install Ollama (the model runtime), Open WebUI (the browser interface), and pull the model(s) you want. Your team accesses it just like a website — no technical skills required for day-to-day use.
Will this replace ChatGPT for my team?
For most everyday business tasks — drafting documents, summarizing, answering questions, writing code, analyzing data — yes, absolutely. Modern open-weight models like Llama 3.3 70B and Qwen 2.5 match GPT-4 class performance on most benchmarks. There are edge cases where frontier cloud models still have an edge (very recent events, specialized domains), but for the 80–90% of what a typical business team uses AI for, local models are fully competitive.
How technical does my team need to be?
To use the AI: not technical at all. Open WebUI looks and works like ChatGPT — just a browser window. To manage the server: basic Linux comfort helps, but it's mostly just Ubuntu system updates and occasional Ollama commands. We document everything and can provide remote setup assistance. For teams with no IT staff, we recommend the AINSLEY-EDGE — it's the lowest-maintenance option we offer.
What if a better model comes out next month?
You run ollama pull new-model:70b and it's on your server in minutes. This is one of the biggest advantages of the local approach — you're not waiting for your vendor to add support for the latest model, you're not paying extra for it, and you're not locked to a model version the vendor chose. The open-weight model ecosystem moves fast, and your eRacks server keeps pace automatically.
Is this actually secure enough for client data?
More secure than cloud APIs, for a simple reason: the data never leaves your network. With cloud AI providers, even enterprise contracts involve your data traveling to and being processed on external infrastructure. With an eRacks server, inference is entirely local — there is no transmission to third parties. For regulated industries like healthcare, legal, and finance, this is often the only architecture that satisfies compliance requirements without significant legal exposure.
Own your AI infrastructure.
Own your data.
Tell us your use case and we'll spec the right server. No obligation, no sales pressure — just an honest recommendation from the team that's been building Linux servers since 1999.
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