Lets say I gave you 25k to just build the most powerful computer for mainly ai, meaning the requirements are a powerful gpu, proportional ram obviously, and 10tb ssd.
If I were to configure a high-end workstation primarily for AI tasks with a $25,000 budget, I would focus on integrating the most powerful datacenter-grade GPUs available, ensuring that the rest of the system is balanced in terms of CPU, RAM, and storage to support the GPU’s capabilities. Here’s an approximate configuration for an AI-oriented workstation with the components meeting your requirements:

### GPU:
- 2x NVIDIA A100 80GB or H100 GPUs: These are currently among the most powerful GPUs for AI and high-performance computing tasks. As of my last update, each A100 can provide up to 312 teraflops of deep learning performance. The H100, being the newer generation, would provide even better performance, but availability might be limited.

### CPU:
- AMD Threadripper PRO or Intel Xeon W-Series: These are high-end workstation processors that offer many cores and threads, suitable for parallel tasks and capable of feeding data to the powerful GPUs without becoming a bottleneck.

### Motherboard:
- Workstation Motherboard compatible with chosen CPU: It must support multiple GPUs, preferably with PCIe 4.0 or PCIe 5.0 slots, and accommodate the memory and storage requirements.

### RAM:
- 256 GB DDR4 or DDR5 ECC RAM: Adequate for most AI and machine learning tasks, and supports error-correcting code (ECC) for better data integrity.

### Storage:
- 2x 4TB PCIe NVMe M.2 SSD: For ultra-fast storage and quick data access, put in a RAID 0 or RAID 1 configuration depending on whether you prioritize speed or redundancy.
- 2x 1TB PCIe NVMe M.2 SSD: Additional SSDs for the operating system, applications, and overflow.

### Power Supply:
- 2000W+ Platinum or Titanium PSU: High-efficiency power supply to support multiple high-end GPUs and other components.

### Cooling:
- Custom Liquid Cooling Solution or High-End Air Cooling: To ensure that both the CPU and GPUs stay cool under heavy loads. Datacenter GPUs often come with passive cooling, so they rely on the server airflow system, and for workstation use, they might require third-party cooling solutions.

### Case:
- Full-Tower or Server Chassis: It must support the form factor of the motherboard and provide adequate space for airflow or a custom cooling solution.

### Additional Considerations:
- Adequate cooling is critical, as high-performance components generate significant heat.
- A professional-grade motherboard is essential to handle high-power CPUs and multiple GPUs while providing reliability for 24/7 operation.
- Premium, high-capacity power supply to ensure stable and reliable power delivery to all components.
- Incorporating a UPS (Uninterruptible Power Supply) to safeguard data and hardware against power anomalies.

Please note that prices and availability can vary greatly depending on your location and the current market situation. It would be essential to check the compatibility of all components, especially the GPUs with the motherboard and power supply, as well as the physical space in the chosen case. Additionally, sourcing these components may require reaching out to specialized suppliers or directly to manufacturers, especially for enterprise-grade GPUs like the NVIDIA A100 or H100.

Lastly, building a system of this caliber may demand expertise in high-end workstation assembly and configuration to ensure that all components work together correctly and are optimized for the desired AI applications.
How much do you think this might cost?
As of my last knowledge update in early 2023, here’s a rough cost estimate for a high-end AI-focused workstation with the components previously mentioned:

### GPU:
- 2x NVIDIA A100 80GB GPUs: Each GPU can cost approximately $10,000 to $15,000, depending on the supplier and current market conditions.

### CPU:
- AMD Threadripper PRO or Intel Xeon W-Series: Prices can range from
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