Comfac GPU Scaling and AI Research Goals

this is the gpu scaling project. How comfac will sell GPU solutions.

Objective: To develop and scale a high-performance AMD-based AI compute cluster, capable of running large-scale models (e.g., Qwen 2.5 235B) and supporting educational and R&D initiatives through open collaboration with partner schools.

Goals and Steps:

1. Platform and Motherboard Selection

   • Identify and procure a motherboard or server platform that supports extensive GPU scaling and PCIe bifurcation (similar to the setup demonstrated by PewDiePie).
   • Ensure compatibility with ROCm and vLLM for distributed inference and multi-GPU coordination.

2. Initial Scaling (Pilot Models)

   • Begin with well-known, stable models to validate infrastructure performance and reliability.
   • Pilot hardware: AMD Radeon PRO R9700 AI or equivalent AI-focused GPU.
   • Validate thermal performance, power delivery, and driver stability for continuous inference workloads.

3. Progressive Hardware Replication

   • Once stable results are achieved with R9700 PRO, replicate the same environment using RX 7900 XTX and other AMD GPUs to benchmark performance scaling.
   • Document compatibility issues, driver updates, and quantization performance metrics.

4. Cluster and Swarm Development

   • Establish a Cluster System for large-model distributed inference and training.
   • Build a Swarm System capable of parallelizing smaller AI instances (e.g., 7700 and lower-end GPU nodes) for local and academic deployment.
   • Optimize inter-node communication, synchronization, and monitoring tools for mixed hardware setups.

5. Funding and Laboratory Deployment

   • Fund the creation of a dedicated AI Lab focused on testing, documentation, and educational use.
   • Provide access to partner schools for research, benchmarking, and AI model fine-tuning.

6. Open Compute and Tokenization Participation

   • Study and participate in open-source projects that allow community-based compute contributions (similar to Folding@home).
   • Learn and experiment with decentralized compute-sharing models that enable contributors to sell tokens or compute time securely and transparently.

Reference: Inspirational video:

End Goal: To make Comfac and its academic partners a recognized hub for open, scalable, and sustainable AI research using AMD technologies.