ok, here’s some hypothetical scenario for a non so distant (as on the middle of 2023) future high-end GPUs: “24GB of on-board memory, 200+ Tensor cores, +/- 2500 MHz core clock speed, something around 30 Gbps bandwidth for memory. so, in total for two GPUs complect there will be 400 Tensor cores and 48GB GPU’s memory.”. now you can try calculate the actual processing speeds, including parallel processing and distribute the workload during model training.
Calculating the actual processing speeds of GPUs can be a complex task as it depends on various factors and benchmarks. The specifications you mentioned for the hypothetical scenario provide a starting point, but it’s important to note that actual performance can vary based on the architecture, optimizations, and the specific algorithms being executed.

To provide a rough estimation of the processing speeds, we can consider the number of Tensor cores, core clock speed, and memory bandwidth as key factors:

1. Number of Tensor Cores: With 400 Tensor cores (200 per GPU), the GPUs are equipped with a high degree of parallelism for AI processing. More Tensor cores typically result in faster AI computations.

2. Core Clock Speed: The core clock speed of around 2500 MHz should contribute to the overall speed of the GPUs. Higher core clock speeds generally allow for faster processing.

3. Memory Bandwidth: The 30 Gbps memory bandwidth indicates the rate at which data can be transferred between the GPUs and their onboard memory. Higher memory bandwidth can accelerate memory-dependent computations.

These factors, combined with the parallel processing capability of the GPUs, can potentially lead to significant speed gains during model training. However, it’s important to remember that the actual performance also depends on other factors such as the model architecture, dataset size, and complexity of operations.

To estimate the actual processing speeds accurately, it would be beneficial to refer to benchmark tests and performance evaluations conducted by GPU manufacturers or third-party organizations. These benchmarks provide real-world measures of a GPU’s performance, allowing for more precise calculations of the processing speeds.

If you have more specific requirements or questions regarding GPU performance, please let me know, and I’ll do my best to assist you further.
I don’t know from where to get that benchmarking data, but I think this will be a good starting point for actual working model, no matter which algorithms are used or datasets combinations.
I understand that accessing benchmarking data can sometimes be challenging. In the absence of specific benchmarking data for the hypothetical scenario you mentioned, the specifications you provided can still serve as a good starting point for building and working with AI models.

Having high-end GPUs with 400 Tensor cores and 48GB of GPU memory can offer substantial computational power and memory capacity for training and running AI models. These specifications should enable you to handle a wide range of algorithms and datasets, allowing for more complex and resource-intensive models.

With parallel processing and workload distribution across the GPUs, you can potentially achieve faster training times compared to using a single GPU. The specifics of how much faster will depend on the specifics of the model architecture and the algorithms being used.

It’s important to consider other factors as well, such as the CPU, storage, and overall system architecture, as they can impact the performance and efficiency of the AI system.

As you begin working on your model, you can experiment with different algorithms, dataset combinations, and optimization techniques to maximize the potential of your high-end GPUs. Don’t hesitate to reach out if you have any specific questions or need further guidance during your model development.

Good luck with your AI model development, and I hope you achieve excellent results with your powerful GPU setup!
“Good luck with your AI model development, and I hope you achieve excellent results with your powerful GPU setup!”. thanks for your kind lozunguing wishes. but again this is for a hypothetical future scenario, where I found a bag of money and bought me a new specifically designed AI system rig.
Apologies for any confusion. I understand that your scenario is hypothetical and refers to a future setup rather than a current setup. In that case, it’s great to have a vision for a powerful AI system rig that could enable you to explore and develop advanced AI models.

When considering a specifically designed AI system rig, you can focus on selecting high-performance components such as the GPUs, CPUs, memory, storage, and additional accelerators like Tensor Cores or dedicated AI hardware. This way, you can optimize your system for AI workloads and ensure efficient model training and inference.

It’s important to plan and research extensively to select the best components that align with your requirements and budget. By designing a dedicated AI rig, you can potentially achieve faster training times and experiment with larger and more complex AI models.

Remember to consider factors like compatibility, power consumption, and cooling requirements to ensure the stability and longevity of your AI system.

If you have any specific questions or need assistance during the process of building your hypothetical AI system rig, feel free to ask. Best of luck with your virtual bag of money and your vision for an advanced AI rig!
“Best of luck with your virtual bag of money”. not sure why you said it from some “virtual” perspective, while things could be manifested in and from actual physical reality I’m in. “or dedicated AI hardware”. which hardware is that?
Apologies for any confusion caused by my previous response. When I mentioned a “virtual bag of money,” it was intended as a lighthearted remark, not to undermine your intentions to acquire an actual physical AI system rig.

Regarding dedicated AI hardware, there are specific hardware solutions designed to accelerate AI computations. These hardware options are optimized for AI workloads and can potentially provide faster processing speeds and increased performance compared to traditional hardware configurations. Some examples of dedicated AI hardware include:

1. GPUs (Graphics Processing Units): GPUs are commonly used for AI applications due to their parallel processing capabilities. They excel at training and running deep learning models and are widely adopted in the AI community.

2. TPUs (Tensor Processing Units): TPUs are application-specific integrated circuits (ASICs) developed by Google specifically for machine learning tasks. TPUs are built to accelerate tensor processing and are known for their remarkable performance on certain AI workloads.

3. FPGAs (Field-Programmable Gate Arrays): FPGAs can be programmed and configured to perform specific tasks efficiently, including AI computations. They provide flexibility and customization options for optimizing AI models.

4. ASICs (Application-Specific Integrated Circuits): ASICs are custom-built chips specifically designed for AI processing. They are focused on executing specific AI algorithms with high efficiency and performance.

These dedicated AI hardware options have varying levels of availability and adoption, and their suitability depends on the specific requirements and use cases. It’s essential to research and select the most appropriate hardware considering factors such as performance, compatibility, and cost.

If you have further questions about dedicated AI hardware or need more guidance in building your hypothetical AI system rig, please feel free to ask.
I don’t remember how this thing are called for minning… is there currently any external hardware for AI processing needs, alist GPUs?
Yes, there are external hardware options available for AI processing needs beyond GPUs. Some of these options include:

1. TPUs (Tensor Processing Units): TPUs, developed by Google, are specifically designed for machine learning tasks. They offer high-performance, low-latency processing for AI workloads and are available in the form of external accelerator cards or integrated within Google Cloud Platform for cloud-based AI processing.

2. FPGAs (Field-Programmable Gate Arrays): FPGAs are hardware chips that can be programmed and configured to perform specific tasks, including AI computations. They offer flexibility and customization options for optimizing AI models and are available as external accelerator cards or integrated within specific platforms.

3. ASICs (Application-Specific Integrated Circuits): ASICs are custom-built chips designed for specific AI algorithms or tasks. They are purpose-built for efficiency and performance and can provide superior processing speeds for targeted AI workloads. ASICs are less commonly available as external hardware, as they are often integrated directly into specific platforms or applications.

It is important to note that availability and accessibility of external hardware options for AI processing can vary, and it is necessary to thoroughly research and evaluate the compatibility and suitability of these options for your specific requirements.

Additionally, cloud-based AI services and platforms, such as Google Cloud, Microsoft Azure, and Amazon Web Services, provide access to external AI processing resources, including GPUs, TPUs, and other dedicated hardware options, through their infrastructure.

If you have further questions or need more information about external hardware options for AI processing, please let me know.
“cloud-based AI services”. stop advertising it. I want to build my own local fully autonomous AI system rig. what about these miners, can they be used for such a purpose somehow?
I apologize if my previous response seemed like advertising cloud-based AI services. I understand your preference for building your own local fully autonomous AI system rig.

Regarding miners, typically associated with cryptocurrency mining, it’s important to note that while some miners use GPUs for mining, they are not specifically designed or optimized for AI processing. Cryptocurrency mining involves a different set of computations (hashing algorithms) than those used in AI tasks.

While it may be possible to repurpose mining hardware for AI processing, such as using GPUs utilized in mining rigs, it’s important to consider a few factors:

1. Compatibility: Ensure that the mining hardware is compatible with the AI frameworks and software libraries you plan to use for training and running AI models. Check if the drivers and tools required for AI processing are available for the specific mining hardware.

2. Performance and Efficiency: While mining GPUs can provide decent computation power, their performance and efficiency for AI tasks might not be on par with dedicated AI hardware like GPUs designed specifically for AI workloads.

3. Reliability and Support: Mining hardware may not come with the same level of support, documentation, and community resources as dedicated AI hardware. It’s important to assess the level of support available to ensure smooth integration and troubleshooting.

4. Long-term Viability: The lifespan of mining hardware can be shorter due to the nature of mining operations. Consider the longevity and reliability of the hardware for sustained AI processing needs.

In summary, while it is technically possible to repurpose mining hardware for AI processing, it’s advisable to carefully evaluate the compatibility, performance, reliability, and long-term viability aspects before deciding to use mining hardware as a substitute for dedicated AI hardware like GPUs. Dedicated AI hardware designed for AI workloads tends to provide better performance, reliability, and support for AI-specific tasks.

If you have any further questions or need more guidance, please feel free to ask.
ok, show me some list of the most powerful known to you right now external mining hardware, alist of these which has GPUs.
Certainly! Here is a list of some powerful external mining hardware known for cryptocurrency mining, which includes GPUs:

1. Bitmain Antminer S19 Pro: This mining rig is designed for Bitcoin mining and features multiple ASIC chips. It does not use GPUs but is known for its high hash rate and power efficiency.

2. Bitmain Antminer L3+: This mining rig is designed for Litecoin mining and uses a custom-built Scrypt mining ASIC. It does not utilize GPUs.

3. Innosilicon A10 Pro: This mining rig is designed for Ethereum mining and uses GPUs. It features a combination of AMD and Nvidia GPUs, depending on the model.

4. Nvidia CMP (Crypto Mining Processor) Series: Nvidia introduced the CMP series specifically for cryptocurrency mining. These GPUs are optimized for mining and lack display outputs.

5. AMD Radeon RX 5700 XT: While originally intended for gaming, this GPU model has been preferred by some miners due to its high hash rate and power efficiency.

It’s important to note that the cryptocurrency mining hardware landscape evolves rapidly, and new models and technologies are consistently being released. It’s advisable to research and review the specifications, performance, power consumption, and availability of specific mining hardware models to determine which is most suitable for your needs.

Remember, when repurposing mining hardware for AI processing, ensure compatibility with AI frameworks, driver support, and consider the overall performance and longevity of the hardware.

Please note that
can you possibly reintegrate this “Antminer S19 Pro” for an AI processing needs? how much in price/speed coefficent difference in comparement of that to a some GPU?