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OpenCode + GLM-4.7-Flash 8bit. llama-server vs mlx_lm.server. Using one M3 Ultra for each host. Same prompts, you can check duration for each step in the video. Amazing experience on both side! Local coding AI model are becoming reality! 🔥

52,143 次观看 • 9 个月前 •via X (Twitter)

31 条评论

Jairo Torregrosa 的头像
Jairo Torregrosa9 个月前

having some issues with llama-server. feels like this version works way better than the one i’m on. the model never stop and just keeps responding. on unsloths gguf rn

Ivan Fioravanti 的头像
Ivan Fioravanti9 个月前

Update to latest 7820 and use: llama-server \ --model ~/models/GLM-4.7-Flash-Q8_0.gguf \ --host 127.0.0.1 \ --port 8080 \ --ctx-size 202752 \ --predict 131072 \ --temp 0.7 \ --top-p 0.95 \ --min-p 0.01 \ --gpu-layers all \ --kv-offload \ --cache-type-k f16 \ --cache-type-v f16 \ --parallel 1 \ --cont-batching \ --no-warmup

Jairo Torregrosa 的头像
Jairo Torregrosa9 个月前

this fixed it, ty one thing i found - with Q8_0 model (30GB) + f16 cache at 200K ctx, you need ~50GB VRAM which overflows the L40S swapping to --cache-type-k q8_0 --cache-type-v q8_0 cuts cache in half 40GB total, fits clean on 48GB

Jairo Torregrosa 的头像
Jairo Torregrosa9 个月前

running on L40S via DigitalOcean (had some free credits lol). getting 97 tok/s with flash attn auto-enabled. appreciate the help

Jacopo Nardiello 的头像
Jacopo Nardiello9 个月前

This is great. I’m going to buy a decent machine with the new M5, I really want to be able to do this and have decent local models at work.

Ivan Fioravanti 的头像
Ivan Fioravanti9 个月前

M5 🔥🔥🔥

David Nix 的头像
David Nix9 个月前

What hardware do you run, mac studio?

Ivan Fioravanti 的头像
Ivan Fioravanti9 个月前

Yes, two of them.

David Nix 的头像
David Nix9 个月前

Well there goes the family vacation budget 🤣

Ivan Fioravanti 的头像
Ivan Fioravanti9 个月前

Wait for the M5! 😉

David Nix 的头像
David Nix9 个月前

totally

Alpha111 的头像
Alpha1119 个月前

Is it good??

Ivan Fioravanti 的头像
Ivan Fioravanti9 个月前

Yes, fast and good enough. It's not Opus, it's ok if you drive specific changes in the code.

MAGUID 的头像
MAGUID9 个月前

Amazing progress for on-device AI

Anoop 的头像
Anoop9 个月前

Honestly, I think the way prices are becoming cheaper by the day, Running it locally is great for learning and backup, but I think using the cloud services is probably the best path

Ivan Fioravanti 的头像
Ivan Fioravanti9 个月前

I agree in the short run, but long run, if we'll have enough local power, why not?

Anoop 的头像
Anoop9 个月前

Yes, I agree. I think the bigger point is by the time you get enough power on your local compute rigs. The cloud providers will have stayed ahead by two or three exponents and you can get today's cloud performance in 2 years on local but that be like using GPT 3 right now

Amr - relax researcher 😌 的头像
Amr - relax researcher 😌9 个月前

When do you think local LLMs will be affordable?

Ivan Fioravanti 的头像
Ivan Fioravanti9 个月前

This year!

Amr - relax researcher 😌 的头像
Amr - relax researcher 😌9 个月前

By a better Hardware or a better LLMs/Spare attentions?

not the Devin you're thinking it's a different one 的头像
not the Devin you're thinking it's a different one9 个月前

much envy i have

Ivan Fioravanti 的头像
Ivan Fioravanti9 个月前

M5 is needed, this is good enough, but we need more GPU power to really push Local coding AI to the max!

Alli Next Gen 的头像
Alli Next Gen9 个月前

which one had better latency tho? and did you notice any memory issues?

Kier 的头像
Kier9 个月前

try pi

jc 的头像
jc9 个月前

Meh i should see what i can get away with on my m4 max 128gb ram

Graphics Noob 的头像
Graphics Noob9 个月前

interesting how they’re different. could it just be temperature vs different inference engines?

JBaba 的头像
JBaba9 个月前

Yeah all we need is quality and we are off to the races.

Kalpit Champanery 的头像
Kalpit Champanery9 个月前

I was experimenting with this same setup on my m4 max and it is incredible. Although at some point I did thought that claude code is beemttwr but this is definitely better than what we we're doing 6 months back.

timugen 的头像
timugen9 个月前

That’s exciting!

Nicole Young 的头像
Nicole Young9 个月前

local coding models are def the future! how did you find the performance differences on M3 Ultra? 🔥

Gregor 的头像
Gregor9 个月前

Same tech can produce wildly different results depending on the setup. You're highlighting the importance of optimization in local AI models. Makes me wonder what other hardware combinations could lead to similar breakthroughs.

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