Phase 1: Initial GPU Compute Node
This project funds the primary hardware component under the Micro Data Center Campaign: our first dedicated high-density GPU compute node. This single bare-metal server serves as the computational engine required to begin pre-training, fine-tuning, and evaluating the proprietary ML models powering our fundraising platform. Funds collected here directly purchase enterprise GPUs, server motherboards, high-frequency RAM, and specialized server enclosure hardware.
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Phase 1: Initial GPU Compute Node
This project funds the primary hardware component under the Micro Data Center Campaign: our first dedicated high-density GPU compute node. This single bare-metal server serves as the computational engine required to begin pre-training, fine-tuning, and evaluating the proprietary ML models powering our fundraising platform. Funds collected here directly purchase enterprise GPUs, server motherboards, high-frequency RAM, and specialized server enclosure hardware.
Artificial Intelligence and Data Science
Organizer
Tech Ventures
Project Objective: Core Compute Acceleration
Under our overarching Micro Data Center initiative, this specific project finances the core engine of the entire cluster: the Initial GPU Compute Node. Without dedicated acceleration silicon, training and testing our fundraising optimization models in-house is impossible.
Instead of paying recurring, non-equity rental costs to third-party cloud providers, this project secures physical ownership of our initial training pipeline hardware.
Specific Hardware Funded by This Project
Accelerator Hardware: Enterprise-grade GPUs capable of distributed mixed-precision training (FP16/BF16) and transformer fine-tuning.
Host Processing: Dual multi-core server processors to eliminate I/O bottlenecks during batch operations.
System Memory: 256GB+ ECC DDR5 memory to sustain large in-memory dataset handling and parallel data loading.
Storage Backbone: High-endurance NVMe M.2 drives for high-throughput checkpointing and dataset caching.
Deliverables & Implementation Steps
Procurement: Acquire primary accelerator cards, chassis, host processors, and memory modules.
Bench Testing: Run 72-hour thermal stress tests, CUDA memory checks, and power stability baselines.
Cluster Integration: Mount into the central server rack, connect to local network fabric, and configure the training scheduler.
Initial Model Verification: Execute benchmark runs on our platform's recommendation models to verify throughput.
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