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Startonix / Modular-AI

Advanced AI Training and Building Repository
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Supercomputer #194

Open Startonix opened 5 months ago

Startonix commented 5 months ago

  1. Central Processing Unit (CPU)

    • Model: IBM Cyclops-64
    • Core Architecture: Massively parallel processing, ideal for extensive computational tasks and large-scale simulations.
    • Performance: High throughput and efficiency in parallel processing environments.

  2. Tensor Processing Unit (TPU)

    • Model: Google TPU v5P
    • Purpose: Accelerates machine learning workloads, enhancing AI training and inference capabilities.

  3. Graphics Processing Unit (GPU)

    • Model: NVIDIA RTX 6000 ADA
    • Purpose: High-performance GPU for AI training, deep learning, and graphical simulations.

  4. Language Processing Unit (LPU)

    • Model: Groq LPU
    • Purpose: Optimized for AI inference with low latency and high throughput.

  5. Neuromorphic Processor

    • Model: Intel Louhi2
    • Purpose: Emulates neural structures for real-time adaptive learning and AI tasks.

  6. Field Programmable Gate Arrays (FPGAs)

    • Type: Microchip Technology Fusion Mixed-Signal FPGAs
    • Purpose: Customizable processing, high-speed data acquisition, and real-time signal processing.

  7. Quantum Computing Components

    • Provider: Xanadu Quantum Technologies
    • Purpose: Executes complex quantum algorithms and simulations.
    1. Silicon Photonic Components
      • Purpose: High-speed data transfer and improved performance through optical connections.

  8. Memory (RAM)

    • Upgraded Capacity: 1TB DDR4 ECC RAM
    • Brands: Professional-grade from manufacturers like Micron or Samsung.
    • Advantages: Higher capacity and advanced error correction for stability and efficiency in extensive computations.

  9. Storage

    • Primary: Texas Instruments Solid-State Solution Drive
    • Capacity: 4TB
    • Additional: High-performance NVMe SSDs (e.g., Samsung 980 PRO 2TB)
    • Advantages: Superior data access speeds and substantial storage capacity for large datasets.

  10. Power Supply Unit (PSU)

    • Custom Model: Custom-built, high-efficiency PSU (1800W or higher)
    • Brand: Specialized manufacturers like Super Flower or EVGA.
    • Advantages: Ensures stable and efficient power delivery to all components with enhanced power management features.

  11. Cooling Solutions

    • Type: Custom liquid cooling with phase-changing materials
    • Provider: Specialized cooling solution providers such as EKWB or custom-built by experts.
    • Advantages: Maintains optimal temperatures with cutting-edge cooling technology, ensuring peak performance under heavy computational loads.

  12. Chassis

    • Model: Custom-built chassis for optimal airflow and component layout
    • Provider: High-end case manufacturers like CaseLabs or custom designs.
    • Advantages: Ensures excellent ventilation, modular design for easy upgrades, and professional aesthetics.
Startonix commented 5 months ago

The IBM A2 is an open source massively multicore capable and multithreaded 64-bit Power ISA processor core designed by IBM using the Power ISA v.2.06 specification. Versions of processors based on the A2 core range from a 2.3 GHz version with 16 cores consuming 65 W to a less powerful, four core version, consuming 20 W at 1.4 GHz.

Startonix commented 5 months ago

https://github.com/OpenPOWERFoundation

Startonix commented 5 months ago

Central Processing Unit (CPU)

Model: AMD Ryzen Threadripper Pro 5995WX Advantages: High multi-threaded performance, suitable for extensive data processing and AI model training.