Not the best on AI/LLM terms, but I assume that training the models was done on Nvidia, while inference (using the model/getting the data from the model) is done on Huawei chips
To add:
Training the model is a huge single-cost expense, while inference is a continuous expense.
Yes. You still need similar ones if you want to run the models really fast, but not nearly the same amount or cost. That’s how people run LLMs on their laptops. You don’t even need a GPU, a multi-core CPU is sufficient, just not very fast at it.
Not the best on AI/LLM terms, but I assume that training the models was done on Nvidia, while inference (using the model/getting the data from the model) is done on Huawei chips
To add: Training the model is a huge single-cost expense, while inference is a continuous expense.
Wait, so after you train, you don’t need all those fancy Nvidia chips?
They should make one place where there is an overabundance of geo thermal energy and train all models there…
Yes, so R&D and finalizing the model weight is done on NVIDIA GPUs (I guess you need an excessive amount of VRAM).
Inference is probably gonna be offloaded to consumers in the end where the NPU is taking care of the inference cost (See Apple, Qualcomm etc)
Yes. You still need similar ones if you want to run the models really fast, but not nearly the same amount or cost. That’s how people run LLMs on their laptops. You don’t even need a GPU, a multi-core CPU is sufficient, just not very fast at it.