A local LLM server for multiple users
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A local LLM server for multiple users needs a batching engine like vLLM, GPU memory for every user's context, and separate accounts. In Olares' own test, Olares One gave 8 concurrent requests about 81 tokens/s each on Qwen3-30B-A3B with vLLM.
Olares One → Local LLM server → vLLM · Ollama · OpenAI-compatible API → Your models, served on your network
What does a server for multiple users need?
A local LLM server for multiple users needs a batching engine, spare GPU memory for every active context and a separate account for each person. Without batching, requests queue and each person waits for the one before. Without spare memory, long chats crowd each other out. Without accounts, everyone sees everyone's history.
Accounts also let you remove access cleanly when someone leaves. Set a per-request limit on output length and context, too. One user pasting a long report into a 24 GB server can take memory that several short chats would have shared, and the engine then queues the others until it finishes.
How does speed change as users are added?
Speed per user falls as users are added, and the engine decides how far it falls. In Olares' own test on Olares One with Qwen3-30B-A3B, vLLM went from 157 tokens per second for one request to about 81 per request at 8 concurrent. Ollama went from 107 to 32 per request over the same step.
Size for simultaneous requests, not staff count, because most people spend most of their time reading or typing. Measure your own peak before buying. The engines are compared in vLLM or Ollama.
How do accounts and access work?
Accounts work at two levels on most local LLM servers. Open WebUI creates users and decides which models each one sees. On Olares One, Olares OS adds system accounts on top: each person connects with the LarePass app, on the local network or remotely through the built-in VPN, according to Olares' FAQ.
Backups run on a schedule, encrypted, to external locations. Admins can still reach every user's data, so limit admin rights and read securing a local LLM server.
What if one box isn't enough?
If peak load outgrows one box, Olares devices can form a cluster. Olares says a cluster raises total throughput and allows larger models, though a single request doesn't get faster. If users need models above about 32B, a 24 GB server is the wrong base, and 128 GB machines are the better start.
Before buying a second machine, check the cheaper fixes. A smaller or mixture-of-experts main model, a lower context cap, or moving occasional models to Ollama often frees enough room. Compare bigger machines in local LLM server hardware.
Questions & answers
How many users can a local LLM server handle?
Count people who send a request at the same moment, not total staff. Olares' own test shows 8 concurrent requests at about 81 tokens per second each on Olares One with vLLM and Qwen3-30B-A3B.
Can each user have a private chat history?
Yes, with Open WebUI user accounts or Olares OS accounts. Admins can still reach the server's data, so decide who holds admin rights.
Sources for this entry
- Olares blog: local AI hardware benchmarking (Oct 2025) (Company-published)
- Olares One FAQ (Olares docs) (Company-published)
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Cite this entry
Local LLM Server. “A local LLM server for multiple users.” Content reviewed 13 November 2026.
https://localllmserver.com/local-llm-server-multiple-users/