4 ms·
It's only 175 billion parameters, so presumably it can fit on a single computer with 1024 GB RAM.
by mortehu 6y ago
It's only 175 billion parameters, so presumably it can fit on a single computer with 1024 GB RAM.
- Voloskaya 6y agoOn CPU the latency would be absolutly prohibitive to the point of being useless.
- typon 6y agoFor training yes, but not for inference.
- leogao 6y agoThe inference latency would also be prohibitive.
- Voloskaya 6y agoInference 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.
- kordlessagain 6y agoFrom 2019: https://heartbeat.fritz.ai/deep-learning-has-a-size-problem-ea601304cd8 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.
- woko 6y agoSorry, but I don't see the link between the quote and your sentence.
- stingraycharles 6y agoWouldn't you need this model to be in GPU RAM instead of regular RAM, though?