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Apple claims M2 Ultra "can train ML workloads, like LLMs"
- postalrat 3y agoSo can my 10 year old android phone.
- isodev 3y agoI wouldn't connect that thing to the internet.
- adammarples 3y agoProbably not "large" language models. M2 has this large unified ram.
- deleted 3y ago[deleted]
- klysm 3y agoSure it can, but it doesn’t have the raw FLOPS to do it fast
- CharlesW 3y agoIt depends on your definition of "fast". https://twitter.com/danielgross/status/1619417508360101889 https://twitter.com/danielgross/status/1619417508360101889 https://twitter.com/natfriedman/status/1665402680376987648 https://twitter.com/natfriedman/status/1665402680376987648
- cypress66 3y agoFirst link is about fp32 which is irrelevant (and 10tflops is a joke). A100 has 300tflops of bf16 Second link is about inference not training
- crtified 3y agoFor reference, this is the original context: The title-linked reddit thread, in turn links to the following YT video, which is of the WWDC23 Apple Keynote speech of June 5. See below the link for a text transcript of that relevant excerpt. https://www.youtube.com/watch?v=GYkq9Rgoj8E&t=666s https://www.youtube.com/watch?v=GYkq9Rgoj8E&t=666s "Finally, the 32-core Neural Engine is 40% faster [than M1 Ultra]. And M2 Ultra can support an enormous 192GB of unified memory, which is 50% more than M1 Ultra - enabling it to do things other chips just can't do. For example, in a single system, it can train massive ML workloads, like large transformer models, that the most powerful discrete GPU can't even process, because it runs out of memory."