From 2019: https://heartbeat.fritz.ai/deep-learning-has-a-size-problem-...

> Earlier this year, researchers at NVIDIA announced MegatronLM, a massive transformer model with 8.3 billion parameters (24 times larger than BERT)

> The parameters alone weigh in at just over 33 GB on disk. Training the final model took 512 V100 GPUs running continuously for 9.2 days.

Running this model on a "regular" machine at some useful rate is probably not possible at this time.

Sorry, but I don't see the link between the quote and your sentence.

Inference on GPU is already very slow on the full-scale non-distilled model (in the 1-2 sec range iirc), on CPU it would be an order of magnitude more.

The inference latency would also be prohibitive.