A self-hosted ChatGPT alternative for your whole company.
Your people already use ChatGPT, often with data that should never leave the company. Loadcore lets you run open models such as gpt-oss, Qwen and Gemma on GPU servers you control, and hand every team the same kind of chat and API they are used to.
Why companies look for a ChatGPT alternative
ChatGPT made AI useful for everyone in the company. It also created three problems that show up the moment legal, security or finance takes a closer look:
- Data leaves the building. Contracts, customer records, source code and internal strategy end up in prompts that are processed on someone else's infrastructure.
- Costs scale with headcount and usage. Per-seat subscriptions and per-token API bills grow with every new user and every new integration.
- You do not control the model. Models are updated, retired or rate limited on the provider's schedule, not yours.
A self-hosted alternative fixes all three by moving inference onto hardware you control.
What Loadcore gives you
Loadcore is a self-hosted LLM management platform. It has three parts:
- The Loadcore Agent is a single binary you start on each GPU server. It installs llama.cpp, downloads models from Hugging Face and serves an OpenAI-compatible API on your own network.
- The Loadcore Console is where your team works. Admins register servers, pull models and start instances. Members chat with the models they are allowed to use and create personal API keys.
- The Loadcore API is the managed control plane that keeps track of servers, models, departments and keys. It never proxies inference.
The result feels like ChatGPT to your users, but every prompt and every answer stays on your servers.
Pick the open models that fit your work
You are not locked into one model. Common choices:
| Use case | Model | Fits on |
|---|---|---|
| General assistant | Qwen3.8 27B | one 24 to 48 GB GPU |
| Reasoning and analysis | gpt-oss 120B | one 80 GB GPU |
| Coding agents | Qwen3 Coder Next | one 64 to 80 GB GPU |
| Frontier quality | GLM-5.3 | a multi-GPU server |
Run different models on different servers, or the same model on several servers for more throughput. See all model guides for VRAM requirements and recommended settings.
Decide who gets which model
Members belong to departments, and each department is granted specific models. Engineering can get the coding model, Legal can get the large reasoning model, and everyone can get the general assistant. Every request carries a personal key, and the agent checks it on your server before anything runs.
Compare the cost
A GPU server costs the same whether ten or a thousand people use it. For a company with hundreds of users, that usually beats paying per seat or per token. Put in your own numbers with the LLM cost calculator.
Get started
Register your first server in the console, start the agent with one command and pull a model. Most teams serve their first private model the same day. The documentation walks through every step.
Frequently asked questions
Is a self-hosted model as good as ChatGPT?
For most everyday company work, such as drafting, summarizing, translating, answering questions over documents and writing code, current open models like gpt-oss 120B, Qwen3.8 and GLM-5.3 are close enough that users rarely notice. And because the model runs on your hardware, you can switch to a newer open model the day it is released.
Do our existing tools keep working?
Yes, if they speak the OpenAI API. Point the base URL at your Loadcore Agent and use a personal sk-lc- key. The OpenAI SDKs, LangChain, Continue, aider, opencode and curl all work this way.
Does Loadcore see our prompts?
No. Inference requests go from your tools straight to the agent on your own server. The Loadcore control plane only receives heartbeats, download progress, instance status and API key checks.
What hardware do we need?
A Linux server with a GPU that llama.cpp supports. A single 24 to 48 GB card runs strong mid-size models, a single 80 GB card runs gpt-oss 120B, and several servers can share one larger model over your local network.
Run open models with strong reasoning and coding skills on your own GPU servers as a private alternative to Claude. OpenAI-compatible API, per-team access, no per-token fees.
Compare what you pay today for AI subscriptions and per-token APIs with the fixed cost of running open LLMs on your own GPU servers. Enter your own numbers.
Run your first model today.
Register a GPU server, pull an open model and hand your team an API key.