If you want the best gpu possible should you be looking towards consumer or datacentre gpus?
Whether you should look towards consumer or datacenter GPUs depends largely on what you mean by “the best GPU possible” and the specific use-case or application you have in mind.

### For Gaming and General Consumer Use:
If you’re a gamer or a general consumer looking for high-performance graphics processing for PC gaming, VR, or creative work like 3D rendering or video editing, you’re likely seeking the best consumer GPU. Consumer GPUs, like NVIDIA’s GeForce RTX series or AMD’s Radeon RX series, are optimized for these tasks. They offer excellent performance, reasonable power consumption, and driver support focused on these applications.

### For Professional and Computing Intensive Tasks:
If, on the other hand, you’re looking for a GPU for professional or computationally intensive tasks such as machine learning, scientific simulations, or data analysis, a datacenter GPU is likely what you need. Datacenter GPUs, such as NVIDIA’s A100 or H100 Tensor Core GPUs, are designed for high-performance computing (HPC) and can handle massive datasets and complex calculations at very high speed. They are optimized for throughput, reliability, and can often be used in parallel configurations for even greater performance.

### Things to Consider:
- Performance: Datacenter GPUs tend to have more memory, faster memory bandwidth, and are designed to handle concurrent tasks, which makes them suitable for large-scale computing tasks.
- Cost: Datacenter GPUs are significantly more expensive than consumer GPUs, so if the budget is a constraint and the applications don’t require the specific features of a datacenter GPU, a high-end consumer GPU might be the better choice.
- Power Consumption: Datacenter GPUs consume more power and generate more heat than consumer GPUs, necessitating advanced cooling solutions and a proper data center environment.
- Drivers and Support: Consumer GPUs receive driver updates more frequently with a focus on improving performance and compatibility for the latest games and consumer software.
- Form Factor and Usage Scenario: GPUs for data centers are typically built for rack-mounted server installations and come with different form factors than consumer GPUs.

In summary, if you are into gaming, content creation, or other general consumer operations, go for the best consumer GPU that your budget allows. If your tasks are related to professional or scientific computation, big data, AI, and similar fields, then a datacenter GPU would be more suitable despite the higher costs and infrastructure requirements.

Always consider the specific application requirements, budget, and infrastructure that you have when deciding which type of GPU to invest in.
No im quite litreally seeking the most powerful, both for games, ai, and other shit
If you are seeking the absolute most powerful GPU without distinguishing between use cases, and budget is not a primary concern, you would typically look towards the top-tier datacenter GPUs. As of my last knowledge update in early 2023, some of the most powerful GPUs in this category include the NVIDIA A100 and the NVIDIA H100 Tensor Core GPUs. These GPUs are designed for the most demanding computational tasks, including AI, deep learning, high-performance computing (HPC), and large-scale simulations.

Here are a few reasons why datacenter GPUs are considered the most powerful:

1. Compute Performance: They have an enormous amount of processing power, with a vast number of CUDA cores (for NVIDIA GPUs) or Stream Processors (for AMD GPUs) that are optimized for compute tasks.
2. Memory: Datacenter GPUs come with large amounts of high-bandwidth memory (HBM2 or HBM2e), which is essential for handling extensive datasets and complex AI models.
3. Scalability: They can often be networked together using high-speed interconnects like NVIDIA’s NVLink or AMD’s Infinity Fabric to scale performance almost linearly across multiple GPUs.
4. Precision: They support a range of precisions, including FP64 (double precision), FP32 (single precision), and specialized formats like Tensor Float (TF32) or even lower precision formats meant for AI workloads. This flexibility allows for more efficient computations tailored to specific tasks.

Conversely, consumer GPUs are designed with a balance between cost, power consumption, and performance suitable for gaming, content creation, and lighter professional workloads. The latest high-end consumer GPUs from NVIDIA’s GeForce RTX series or AMD’s Radeon RX series can also handle AI tasks and provide excellent performance for games and creative applications, albeit at a lower scale than their datacenter counterparts. They are also much more accessible in terms of price and the necessary supporting hardware and infrastructure.

In summary, for the ultimate in power and computational capability, datacenter GPUs are the top performers, but they come at a high cost and with infrastructure requirements catered to enterprise and research environments. If you plan to use this GPU for gaming alongside AI and “other stuff,” you might encounter practical challenges, as datacenter GPUs are not optimized for gaming and might lack certain features or optimizations present in consumer graphics drivers.

Should you decide to go with a consumer GPU, you’ll still be getting one of the most powerful gaming GPUs available (as of early 2023), which can also handle a significant amount of professional work, including AI and other demanding computing tasks, albeit not at the same level as a datacenter GPU.
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