A private alternative to Claude, running on your own GPUs.
Claude is popular with developers and analysts for long documents and coding. Open models like gpt-oss 120B, GLM-5.3 and Qwen3 Coder Next now cover much of that work, and with Loadcore they run on servers you control instead of a third-party cloud.
Where Claude shines, and what it costs you
Teams pick Claude for reading long documents, careful writing and coding agents. The trade-off is the same as with any hosted model: every prompt is processed by the provider, usage is billed per token, and you depend on the provider's capacity, limits and model schedule.
For code, contracts, research data and customer information, many companies would rather keep that traffic inside their own network.
Open models that cover the same ground
| Claude use case | Open model | Notes |
|---|---|---|
| Long document analysis | GLM-5.3 | 1M token context, multi-GPU server |
| Reasoning and analysis | gpt-oss 120B | Apache 2.0, one 80 GB GPU |
| Coding agents | Qwen3 Coder Next | 80B MoE with 3B active, fast on long sessions |
| Frontier-class work | DeepSeek V4.1 Flash | MIT licensed, 1M context, multi-GPU |
| Everyday assistant | Gemma 4 31B | fits one 48 GB GPU |
Open models now handle the bulk of daily work at a level most users cannot tell apart from hosted models, and new releases arrive every few months. With Loadcore you pull the next one from Hugging Face and serve it the same day.
How Loadcore runs them
- One agent per GPU server installs llama.cpp, downloads GGUF weights and serves an OpenAI-compatible endpoint.
- One console to register servers, start instances with your context size and GPU layers, and follow usage.
- Departments and per-model grants so the expensive frontier model stays with the teams that need it.
- Distributed instances split one large model across up to eight servers on your local network, using GPU memory you already have.
Point your tools at it
Any tool that speaks the OpenAI API works. Set the base URL to your agent and use a personal sk-lc- key:
curl http://your-server:9090/v1/chat/completions \
-H "Authorization: Bearer sk-lc-..." \
-H "Content-Type: application/json" \
-d '{"model": "your-model.gguf", "messages": [{"role": "user", "content": "Summarize this contract"}]}'
Check the numbers
Per-token pricing for frontier models adds up quickly for heavy users. Compare it with a fixed GPU bill in the LLM cost calculator, or read how the self-hosted ChatGPT alternative setup works for a whole company.
Frequently asked questions
Can an open model replace Claude for coding?
For many teams, yes. Qwen3 Coder Next and GLM-5.3 are built for agentic coding tools such as opencode, aider and Continue. They handle long coding sessions well, and with a 262K to 1M token context they can work across large codebases.
Can I use Claude Code with a self-hosted model?
Claude Code is built for Anthropic's models. For a self-hosted setup, use an agent tool that speaks the OpenAI API, such as opencode, aider or Continue, and point it at your Loadcore Agent.
How long a context can open models handle?
Several current open models support very long contexts: Qwen3.8 and Gemma 4 support 262K tokens, and GLM-5.3 and DeepSeek V4.1 Flash support 1M tokens. Long contexts need more GPU memory, so size your servers for the context you actually use.
What leaves our network?
Prompts and completions do not. The agent sends heartbeats, download progress, instance status and API key checks to the Loadcore control plane. Model weights download from Hugging Face directly to your server.
Give your team a private ChatGPT alternative on your own GPU servers. Open models, an OpenAI-compatible API, per-department access and no prompts leaving your network.
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.