TL;DR: Started experimenting with a $50, 13-year-old "best money can buy" build and a collection of obsolete NVIDIA GPUs to see whether they could run modern LLMs. Ended up building a functioning, locally hosted, voice-controlled AI bridge crew for Star Citizen, with five specialist AI officers coordinated by an executive AI called Riker(I know, but I had to call it something). The next goal is to give each officer independent visual awareness, autonomous decision-making, and the ability to communicate naturally with the captain, commander and each other.
This project started with a simple question: could I make obsolete NVIDIA Kepler GPUs run modern AI models?
Using an ASUS Rampage IV Black Edition, an i7-4960X, and a collection of GTX Titan cards, I eventually got five Kepler GPUs working with legacy CUDA drivers and a patched llama.cpp. Qwen2.5-32B ran at roughly 2.8 tokens per second, which was hardly impressive. But running five independent 7B models produced around 72 tokens per second in aggregate.
That got me thinking. Instead of having five models doing the same thing, why not give each one a job?
And that's how BigBoy Kepler became AI Bridge Crew.
The system consists of five AI specialists: Helm, Tactical, Engineering, Sensors, and Communications. Each handles a specific operational area of a spacecraft, while a separate executive AI coordinates the crew, interprets commands, and manages responses.
I also developed Eyes, a custom python application that observes Star Citizen's HUD and MFDs and extracts live telemetry, including speed, fuel, power allocation, shields, contacts, and ship damage. That information feeds into the crew's operational awareness rather than relying entirely on information invented by the language models.
The system already works. Using push-to-talk, Whisper speech recognition, and Kokoro speech synthesis, I can speak directly to Riker. I can request an all-stations report and receive individual responses from each specialist, followed by Riker's summary. One complete test delivered the first voice response in 6.47 seconds.
Riker also maintains persistent operational information, remembers contracts across restarts, and can retrieve live UEX commodity and trading data. You can ask "Riker. Wheres the best place to buy Gold?" And Riker will answer location and price. Works with items too.
This is just whats been done so far.
The next stage is changing how Eyes works. Rather than simply extracting and interpreting HUD information centrally, Eyes will identify Areas of Interest and send the relevant images directly to individual specialists. Each specialist will use vision-capable models to analyse its own displays, maintain situational awareness, and draw conclusions independently. Each will have its own state engine and database, so it can keep things in memory, like the different layouts of ships, HUDs, MFDs and other relevant information.
Engineering will understand component health, power distribution, cooling, and damage. Tactical will evaluate threats, weapons, and defensive capability. Helm will monitor navigation and flight conditions. Sensors will track and identify contacts. Communications responsibilities will increasingly be integrated into Riker.
Each officer will also have its own priorities and operational thresholds. They won't need to be asked about everything. If Engineering detects a critical failure or Tactical identifies an incoming missile, they should interrupt and report it automatically.
The other major goal is proper conversational interaction between the crew.
I want to be able to ask Riker for a damage assessment, have Riker order Engineering to report, and hear Engineering respond with "Commander..." before delivering its findings. Riker should acknowledge that report, potentially consult another officer, and then brief the captain. Every exchange will be generated and voice-rendered, with officers maintaining their own responsibilities and context.
None of that full inter-officer conversation and autonomous reporting system is implemented yet, but it's where the project is heading.
Hardware upgrades are also planned, replacing Kepler with Pascal GPUs to support more capable specialist models and eventually direct visual analysis. There's plenty of work ahead on latency, memory, coordination, and ship-specific awareness.
The ultimate goal isn't five chatbots pretending to be spaceship officers. It's an autonomous, conversational bridge crew that understands what's happening aboard your ship, coordinates its own specialists, alerts you when something matters, and responds naturally to orders.
All running locally, without depending on cloud AI services.
What started as "Can these ancient GPUs run an LLM?" has turned into "Can I build a local AI bridge crew for Star Citizen?"
I am building this for me out of curiosity and boredom, but is there anything any one else could see a use for this? I am tossing up whether to build a modular installer because not everyone else has 30GB of Kepler laying around.
Edit: Full disclosure, I used Codex to do my UI work. I am terrible at that part. Its not a skill I have. I am a DevOps Engineer, so I can do the complicated systems work and engine stuff, but I am horrible at UI.