Ideal GPU for Applied ML

Depends on the type of work. Usually the VRAM is the limiting factor.
If you work for a company/client then they would ideally have a production environment where you can just spin up some GPUs.

For local, more is usually better, so if you have $$ get a 4090 or 5090(in a few months).
I would not recommend anything less than 16gb of vram so performance/cost wise a 4070 seems like the best or you can buy a useless macbook and rent GPUs by the hr.

For running large language models (LLMs) in applied machine learning, the ideal GPU should have a combination of high computational power, ample memory, and efficient memory bandwidth. Here are the key specifications to consider:

Key Specifications

1. CUDA Cores:
   • CUDA cores are essential for parallel processing, which is crucial for training and inference of LLMs. More CUDA cores generally mean better performance.
   • For example, the NVIDIA GeForce RTX 4090 offers a significant number of CUDA cores, making it a strong contender for LLM tasks.
2. Tensor Cores:
   • Tensor cores are specialized for matrix operations, which are fundamental in deep learning. They significantly accelerate training and inference times.
   • The NVIDIA A100 and H100 GPUs are known for their high number of Tensor cores, making them ideal for high-performance AI applications.
3. VRAM (Video RAM):
   • Adequate VRAM is critical for handling large datasets and model parameters. For LLMs, it is recommended to have at least 16GB of VRAM, with higher amounts being preferable for larger models.
   • The NVIDIA RTX 4090 and A100 GPUs offer substantial VRAM, with the A100 providing up to 48GB of GDDR6 memory.
4. Memory Bandwidth:
   • High memory bandwidth ensures that data can be transferred quickly between the GPU and the rest of the system, which is vital for maintaining performance during intensive computations.
   • The NVIDIA A100 and H100 GPUs provide high memory bandwidth, which is essential for efficient LLM processing.
5. Clock Speed:
   • A higher clock speed can improve the performance of individual cores, contributing to overall computational efficiency.
   • The MSI GeForce RTX 4060 Ventus Graphics Card, for instance, has a clock speed of 2505MHz, which is quite competitive for its class.

Recommended GPUs

1. NVIDIA GeForce RTX 4090:
   • Offers a high number of CUDA and Tensor cores, along with substantial VRAM and memory bandwidth. It is well-suited for both gaming and deep learning tasks.
2. NVIDIA A100:
   • Designed specifically for high-performance AI applications, it provides a significant number of CUDA and Tensor cores, along with 48GB of GDDR6 memory and high memory bandwidth.
3. NVIDIA H100:
   • Similar to the A100 but with further optimizations for AI workloads, making it one of the top choices for running LLMs.
4. NVIDIA RTX 4070 Super:
   • A more budget-friendly option that still offers excellent performance with a good balance of CUDA cores, VRAM, and memory bandwidth.

Conclusion

For applied machine learning tasks involving LLMs, the ideal GPU should have a high number of CUDA and Tensor cores, ample VRAM (at least 16GB), and high memory bandwidth. The NVIDIA GeForce RTX 4090, A100, and H100 are among the top choices that meet these criteria, providing excellent performance for both training and inference of large language models.