Vast majority of folks are using from the cloud today. If you want to spend big on local ML there are other options. Maybe a future starfighter with panther lake and unified mem is in the cards, but not today.
1. "AI" is a marketing term used by the likes of OpenAI/Anthropic/Google. LocalLLaMa communities prefer to use "LLM" or "model". So for a lot of people "AI" is just a service (see 4.)
2. "AI capability" is an irrelevant spec and marketing slug. The hardware specs will give you the needed infomation to consider a model[0][1].
3. If you'll want to run a model locally, you'd know that a midrange notebook isn't the device to look for. Instead, look at workstations with discrete graphic cards + lots of VRAM (24GB+), Strix Halo APUs or a MacBook with lots of RAM, or some dedicated workstations like the NVIDIA DGX Spark[2].
4. An inference engine can run anywhere, you can pick any LLM hosting service. LLM clients just expect an API endpoint anyway.
No, AI capabilities of some sort are obviously important. But I know a lot of people don't appreciate that.
But you aren't seriously suggesting that graphics hardware is irrelevant are you?
Vast majority of folks are using from the cloud today. If you want to spend big on local ML there are other options. Maybe a future starfighter with panther lake and unified mem is in the cards, but not today.
The few things that make me agree with GP:
1. "AI" is a marketing term used by the likes of OpenAI/Anthropic/Google. LocalLLaMa communities prefer to use "LLM" or "model". So for a lot of people "AI" is just a service (see 4.)
2. "AI capability" is an irrelevant spec and marketing slug. The hardware specs will give you the needed infomation to consider a model[0][1].
3. If you'll want to run a model locally, you'd know that a midrange notebook isn't the device to look for. Instead, look at workstations with discrete graphic cards + lots of VRAM (24GB+), Strix Halo APUs or a MacBook with lots of RAM, or some dedicated workstations like the NVIDIA DGX Spark[2].
4. An inference engine can run anywhere, you can pick any LLM hosting service. LLM clients just expect an API endpoint anyway.
[0]: https://www.canirun.ai/
[1]: https://www.caniusellm.com/
[2]: https://www.nvidia.com/en-us/products/workstations/dgx-spark...
Off topic but I like your username! Ironically I have matching 2003 CR85 and CR250's but not the 125 :P