Published: March 6, 2026
Source: GitHub
Category: AI Infrastructure
Language: Rust 🦀
What Just Happened
ollamaMQ just dropped and it is solving a problem every Ollama user with multiple team members has faced: GPU resource exhaustion when multiple people hit your local LLM at once.
Built in Rust with a real-time TUI dashboard, ollamaMQ acts as a smart proxy that queues incoming requests and dispatches them sequentially to your Ollama backend. No more crashes when three developers simultaneously ask your local Qwen model to review their code.
If you are running Ollama in a team environment, this is not optional—it is essential infrastructure.
Why This Matters for Ollama Users
The Problem:
- GPU Exhaustion — Multiple concurrent requests crash Ollama or make it unresponsive
- No Fair Scheduling — One user’s massive context window blocks everyone else
- No Visibility — No idea what is in the queue or who is using resources
- Resource Conflicts — Multiple teams competing for the same GPU
ollamaMQ Solves All of It:
- Smart Queuing — Requests queue automatically, dispatched sequentially
- Fair-Share Scheduling — Per-user limits prevent resource hogging
- Real-Time TUI Dashboard — See queue depth, active requests, GPU usage
- Round-Robin Dispatch — Fair distribution across multiple Ollama instances
What It Actually Does
✅ Request Queuing
# Multiple users hit your Ollama instance
# ollamaMQ queues them automatically
# Dispatches one at a time to prevent GPU overload✅ Per-User Fair Share
Each user gets a fair allocation. Prevents one power user from dominating GPU time.
✅ Real-Time TUI Dashboard
Built with Ratatui: Queue depth, active requests, GPU/CPU usage, per-user metrics.
✅ Drop-In Replacement
Zero code changes. Fully compatible with Ollama REST API.
How to Get Started
# Install
cargo install ollamaMQ
# Run
ollamaMQ --port 8080 --ollama-url http://localhost:11434
# Use
OLLAMA_URL = "http://localhost:8080"🔗 GitHub: https://github.com/Chleba/ollamaMQ
Related Resources
Apply for 0% APR Business Funding →
Infinite Leverage Masterclass — $97
AI Agent Blueprint — $27
Book Studio Session — $497
Subscribe to Letters from the Edge
P.S. — If you are running Ollama with more than one user, you need a message queue. ollamaMQ is the first one built specifically for LLM workloads.