llm-hardware-lab

Inspiration

Choosing hardware for local LLMs is often confusing. We wanted to make the decision clearer by combining memory, speed, compatibility, and cost in one tool.

What it does

LLM Hardware Lab helps users compare local LLM hardware based on model size, quantization, context length, budget, performance, and memory requirements.

How we built it

We built it with HTML, CSS, and JavaScript, using a data-driven recommendation system and responsive interface. It is deployed as a public website with Sites.

Challenges we ran into

The main challenge was turning complex hardware and model constraints into simple, useful recommendations for different users.

Accomplishments that we're proud of

We created a practical tool that connects technical specifications with real purchasing decisions while keeping the interface fast and easy to use.

What we learned

Hardware recommendations depend on more than raw performance. Memory, bandwidth, compatibility, budget, and intended use all matter together.

What's next for llm-hardware-lab

We plan to add more models and hardware, real-world benchmark data, user-submitted configurations, and personalized recommendations.

Built With

• ai-hardware
• developer
• edge-ai
• gpu-computing
• hardware-benchmark
• llm
• local-ai
• machine-learning
• model-deployment
• open-source