Justinaquino: Add Applied Model Evaluation - Synopsis mail-agent model-selection test (2026-07-31)

2026-07-31T13:02:32Z

Add Applied Model Evaluation - Synopsis mail-agent model-selection test (2026-07-31)

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 * 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.
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BabiSender: Created page with "= Comfac GPU Scaling and AI Research Goals = == 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 bifurcati..."

2026-02-25T06:59:07Z

Created page with "= Comfac GPU Scaling and AI Research Goals = == 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 bifurcati..."

New page

= Comfac GPU Scaling and AI Research Goals =

== 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.

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== 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.

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== Reference ==

* Inspirational video: [https://youtube.com/qw4fDU18RcU?si=TJ8hYQPIjuQuiORk Watch on YouTube]

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== End Goal ==

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

Comfac GPU Scaling and AI Research Goals - Revision history

Justinaquino: Add Applied Model Evaluation - Synopsis mail-agent model-selection test (2026-07-31)