▲ 253 ▼ SpaceX is buying Cursor for $60 billion (www.businessinsider.com) submitted 4 months ago by sanitation@lemmy.today to c/technology@lemmy.world 106 comments fedilink hide all child comments
[+] GoatSynagogue@lemmy.world 10 points 4 months ago (9 children) [deleted] permalink fedilink source parent hideshow 9 child comments replies: [–] VonReposti@feddit.dk 1 point 4 months ago (8 children) There is a significant overlap between developers and gamers. permalink fedilink source parent hideshow 8 child comments replies: [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* (6 children) Also developers often want more ram, and if youre on the mac side, the M series ram works as video ram for loading and running models, so there's a good chance you can already run something better than is typical of others, and apple is focusing on this by adding more NPUs and increasing memory bandwidth. They arent good at training, but can do inference. permalink fedilink source parent hideshow 6 child comments replies: [–] partofthevoice@lemmy.zip 1 point 4 months ago (5 children) I’m on a MacBook with M2, 32GB ram. Literally just tried: gemma4:12b - very slow, unworkable qwen3:8b - very slow, unworkable qwen2.5-coder:7b - slow but workable. Doesn’t use tools properly in OpenCode. Well, I guess I’ll try again next year. For context: my home pc is running gemma4:31b just fine. It’s also a beefy ass desktop, though. permalink fedilink source parent hideshow 5 child comments replies: [–] fluxx@mander.xyz 3 points 4 months ago (3 children) Are you running an mlx model? If not, try that. My m4 macbook runs qwen3.6-35b-a3b lightning fast. Has its issues, but fast nonetheless. permalink fedilink source parent hideshow 3 child comments replies: [–] NotMyOldRedditName@lemmy.world 1 point 4 months ago (2 children) What kind of context length can you get with that, and how much ram? permalink fedilink source parent hideshow 2 child comments replies: [–] fluxx@mander.xyz 2 points 4 months ago (1 child) I have a model with 64GB of ram. I've limited context to 16k, in an effort to make it more stable, but tbh - it is rather unreliable no matter what I do. With my setup - mlx_lm and webui, it frequently collapses or loops, no matter the settings. I have done a lot of debugging and have concluded it is probably inherent model behavior. permalink fedilink source parent hideshow 1 child comment replies: [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* That's lame about the looping, but ya I don't think that's a mlx issue, I've had it on my desktop with my nvidia card as well. I also tried fussing with configurations, and I was never sure if it was the models or my settings. I was mainly toying around with LLama based models. permalink fedilink source parent [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* You might be doing something wrong, models that size shouldn't be that slow if properly configured on a 32gb m2 You need a metal optimized client and model, not the same models you'd run on your desktop machine. permalink fedilink source parent load more comments (1 reply)
[–] VonReposti@feddit.dk 1 point 4 months ago (8 children) There is a significant overlap between developers and gamers. permalink fedilink source parent hideshow 8 child comments replies: [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* (6 children) Also developers often want more ram, and if youre on the mac side, the M series ram works as video ram for loading and running models, so there's a good chance you can already run something better than is typical of others, and apple is focusing on this by adding more NPUs and increasing memory bandwidth. They arent good at training, but can do inference. permalink fedilink source parent hideshow 6 child comments replies: [–] partofthevoice@lemmy.zip 1 point 4 months ago (5 children) I’m on a MacBook with M2, 32GB ram. Literally just tried: gemma4:12b - very slow, unworkable qwen3:8b - very slow, unworkable qwen2.5-coder:7b - slow but workable. Doesn’t use tools properly in OpenCode. Well, I guess I’ll try again next year. For context: my home pc is running gemma4:31b just fine. It’s also a beefy ass desktop, though. permalink fedilink source parent hideshow 5 child comments replies: [–] fluxx@mander.xyz 3 points 4 months ago (3 children) Are you running an mlx model? If not, try that. My m4 macbook runs qwen3.6-35b-a3b lightning fast. Has its issues, but fast nonetheless. permalink fedilink source parent hideshow 3 child comments replies: [–] NotMyOldRedditName@lemmy.world 1 point 4 months ago (2 children) What kind of context length can you get with that, and how much ram? permalink fedilink source parent hideshow 2 child comments replies: [–] fluxx@mander.xyz 2 points 4 months ago (1 child) I have a model with 64GB of ram. I've limited context to 16k, in an effort to make it more stable, but tbh - it is rather unreliable no matter what I do. With my setup - mlx_lm and webui, it frequently collapses or loops, no matter the settings. I have done a lot of debugging and have concluded it is probably inherent model behavior. permalink fedilink source parent hideshow 1 child comment replies: [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* That's lame about the looping, but ya I don't think that's a mlx issue, I've had it on my desktop with my nvidia card as well. I also tried fussing with configurations, and I was never sure if it was the models or my settings. I was mainly toying around with LLama based models. permalink fedilink source parent [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* You might be doing something wrong, models that size shouldn't be that slow if properly configured on a 32gb m2 You need a metal optimized client and model, not the same models you'd run on your desktop machine. permalink fedilink source parent load more comments (1 reply)
[–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* (6 children) Also developers often want more ram, and if youre on the mac side, the M series ram works as video ram for loading and running models, so there's a good chance you can already run something better than is typical of others, and apple is focusing on this by adding more NPUs and increasing memory bandwidth. They arent good at training, but can do inference. permalink fedilink source parent hideshow 6 child comments replies: [–] partofthevoice@lemmy.zip 1 point 4 months ago (5 children) I’m on a MacBook with M2, 32GB ram. Literally just tried: gemma4:12b - very slow, unworkable qwen3:8b - very slow, unworkable qwen2.5-coder:7b - slow but workable. Doesn’t use tools properly in OpenCode. Well, I guess I’ll try again next year. For context: my home pc is running gemma4:31b just fine. It’s also a beefy ass desktop, though. permalink fedilink source parent hideshow 5 child comments replies: [–] fluxx@mander.xyz 3 points 4 months ago (3 children) Are you running an mlx model? If not, try that. My m4 macbook runs qwen3.6-35b-a3b lightning fast. Has its issues, but fast nonetheless. permalink fedilink source parent hideshow 3 child comments replies: [–] NotMyOldRedditName@lemmy.world 1 point 4 months ago (2 children) What kind of context length can you get with that, and how much ram? permalink fedilink source parent hideshow 2 child comments replies: [–] fluxx@mander.xyz 2 points 4 months ago (1 child) I have a model with 64GB of ram. I've limited context to 16k, in an effort to make it more stable, but tbh - it is rather unreliable no matter what I do. With my setup - mlx_lm and webui, it frequently collapses or loops, no matter the settings. I have done a lot of debugging and have concluded it is probably inherent model behavior. permalink fedilink source parent hideshow 1 child comment replies: [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* That's lame about the looping, but ya I don't think that's a mlx issue, I've had it on my desktop with my nvidia card as well. I also tried fussing with configurations, and I was never sure if it was the models or my settings. I was mainly toying around with LLama based models. permalink fedilink source parent [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* You might be doing something wrong, models that size shouldn't be that slow if properly configured on a 32gb m2 You need a metal optimized client and model, not the same models you'd run on your desktop machine. permalink fedilink source parent
[–] partofthevoice@lemmy.zip 1 point 4 months ago (5 children) I’m on a MacBook with M2, 32GB ram. Literally just tried: gemma4:12b - very slow, unworkable qwen3:8b - very slow, unworkable qwen2.5-coder:7b - slow but workable. Doesn’t use tools properly in OpenCode. Well, I guess I’ll try again next year. For context: my home pc is running gemma4:31b just fine. It’s also a beefy ass desktop, though. permalink fedilink source parent hideshow 5 child comments replies: [–] fluxx@mander.xyz 3 points 4 months ago (3 children) Are you running an mlx model? If not, try that. My m4 macbook runs qwen3.6-35b-a3b lightning fast. Has its issues, but fast nonetheless. permalink fedilink source parent hideshow 3 child comments replies: [–] NotMyOldRedditName@lemmy.world 1 point 4 months ago (2 children) What kind of context length can you get with that, and how much ram? permalink fedilink source parent hideshow 2 child comments replies: [–] fluxx@mander.xyz 2 points 4 months ago (1 child) I have a model with 64GB of ram. I've limited context to 16k, in an effort to make it more stable, but tbh - it is rather unreliable no matter what I do. With my setup - mlx_lm and webui, it frequently collapses or loops, no matter the settings. I have done a lot of debugging and have concluded it is probably inherent model behavior. permalink fedilink source parent hideshow 1 child comment replies: [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* That's lame about the looping, but ya I don't think that's a mlx issue, I've had it on my desktop with my nvidia card as well. I also tried fussing with configurations, and I was never sure if it was the models or my settings. I was mainly toying around with LLama based models. permalink fedilink source parent [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* You might be doing something wrong, models that size shouldn't be that slow if properly configured on a 32gb m2 You need a metal optimized client and model, not the same models you'd run on your desktop machine. permalink fedilink source parent
[–] fluxx@mander.xyz 3 points 4 months ago (3 children) Are you running an mlx model? If not, try that. My m4 macbook runs qwen3.6-35b-a3b lightning fast. Has its issues, but fast nonetheless. permalink fedilink source parent hideshow 3 child comments replies: [–] NotMyOldRedditName@lemmy.world 1 point 4 months ago (2 children) What kind of context length can you get with that, and how much ram? permalink fedilink source parent hideshow 2 child comments replies: [–] fluxx@mander.xyz 2 points 4 months ago (1 child) I have a model with 64GB of ram. I've limited context to 16k, in an effort to make it more stable, but tbh - it is rather unreliable no matter what I do. With my setup - mlx_lm and webui, it frequently collapses or loops, no matter the settings. I have done a lot of debugging and have concluded it is probably inherent model behavior. permalink fedilink source parent hideshow 1 child comment replies: [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* That's lame about the looping, but ya I don't think that's a mlx issue, I've had it on my desktop with my nvidia card as well. I also tried fussing with configurations, and I was never sure if it was the models or my settings. I was mainly toying around with LLama based models. permalink fedilink source parent
[–] NotMyOldRedditName@lemmy.world 1 point 4 months ago (2 children) What kind of context length can you get with that, and how much ram? permalink fedilink source parent hideshow 2 child comments replies: [–] fluxx@mander.xyz 2 points 4 months ago (1 child) I have a model with 64GB of ram. I've limited context to 16k, in an effort to make it more stable, but tbh - it is rather unreliable no matter what I do. With my setup - mlx_lm and webui, it frequently collapses or loops, no matter the settings. I have done a lot of debugging and have concluded it is probably inherent model behavior. permalink fedilink source parent hideshow 1 child comment replies: [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* That's lame about the looping, but ya I don't think that's a mlx issue, I've had it on my desktop with my nvidia card as well. I also tried fussing with configurations, and I was never sure if it was the models or my settings. I was mainly toying around with LLama based models. permalink fedilink source parent
[–] fluxx@mander.xyz 2 points 4 months ago (1 child) I have a model with 64GB of ram. I've limited context to 16k, in an effort to make it more stable, but tbh - it is rather unreliable no matter what I do. With my setup - mlx_lm and webui, it frequently collapses or loops, no matter the settings. I have done a lot of debugging and have concluded it is probably inherent model behavior. permalink fedilink source parent hideshow 1 child comment replies: [–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* That's lame about the looping, but ya I don't think that's a mlx issue, I've had it on my desktop with my nvidia card as well. I also tried fussing with configurations, and I was never sure if it was the models or my settings. I was mainly toying around with LLama based models. permalink fedilink source parent
[–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* That's lame about the looping, but ya I don't think that's a mlx issue, I've had it on my desktop with my nvidia card as well. I also tried fussing with configurations, and I was never sure if it was the models or my settings. I was mainly toying around with LLama based models. permalink fedilink source parent
[–] NotMyOldRedditName@lemmy.world 2 points 4 months ago* You might be doing something wrong, models that size shouldn't be that slow if properly configured on a 32gb m2 You need a metal optimized client and model, not the same models you'd run on your desktop machine. permalink fedilink source parent