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GLM 5.2 has just been released 🔥 Here it's already running with MLX on two Mac Studios (M3 Ultra). This is comparable to the latest closed models, with weights you can download, quantize, distill, fine-tune, run.

Pedro Cuenca

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83,401 次观看 • 1 个月前 •via X (Twitter)

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GLM-4.7 Flash by Z.ai running on M3 Ultra using MLX! 4bit at 81 toks/s 🔥 8bit at 64 toks/s 🔥 (video below) Model converted from transformers to MLX on the fly with Codex + GPT-5.2 HIgh!

GLM-4.7 Flash by Z.ai running on M3 Ultra using MLX! 4bit at 81 toks/s 🔥 8bit at 64 toks/s 🔥 (video below) Model converted from transformers to MLX on the fly with Codex + GPT-5.2 HIgh!

Ivan Fioravanti ᯅ

75,425 次观看 • 6 个月前

Running Kimi K2.5 on my desk. Runs at 24 tok/sec with 2 x 512GB M3 Ultra Mac Studios connected with Thunderbolt 5 (RDMA) using EXO Labs / MLX backend. Yes, it can run clawdbot.

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Alex Cheema

3,293,604 次观看 • 6 个月前

Playing with Qwen3-TTS is and MLX-Audio locally on Mac Studio M3 Ultra 🔥 Amazing model by Qwen and great work by Prince Canuma bringing this magic to MLX! Tuning the voice with instructions feels like magic! Command and prompts to run it are in the video.

Playing with Qwen3-TTS is and MLX-Audio locally on Mac Studio M3 Ultra 🔥 Amazing model by Qwen and great work by Prince Canuma bringing this magic to MLX! Tuning the voice with instructions feels like magic! Command and prompts to run it are in the video.

Ivan Fioravanti ᯅ

35,720 次观看 • 6 个月前

Whoa, GLM-5.2 is INSANE for UI/UX with the right prompting. Open models have finally closed the gap. You have to push it, but it's super creative. I just asked for a beautiful personal site; this was all the model's idea (multiple turns):

Whoa, GLM-5.2 is INSANE for UI/UX with the right prompting. Open models have finally closed the gap. You have to push it, but it's super creative. I just asked for a beautiful personal site; this was all the model's idea (multiple turns):

Anshu

178,213 次观看 • 1 个月前

Running DeepSeek R1 on my desk Uses EXO Labs with Thunderbolt 5 interconnect (80Gbps) to run the full (671B, 8-bit) DeepSeek R1 distributed across 2 M3 Ultra 512GB Mac Studios (1TB total Unified Memory). Runs at 11 tok/sec. Theoretical max is ~20 tok/sec.

Running DeepSeek R1 on my desk Uses EXO Labs with Thunderbolt 5 interconnect (80Gbps) to run the full (671B, 8-bit) DeepSeek R1 distributed across 2 M3 Ultra 512GB Mac Studios (1TB total Unified Memory). Runs at 11 tok/sec. Theoretical max is ~20 tok/sec.

Alex Cheema

992,694 次观看 • 1 年前

🚨 do you understand what is happening?! OpenAI and Anthropic are putting the best models behind approval lists. Meanwhile GLM 5.2 is running locally on a Mac Studio, coding in a loop 24/7. If someone can turn off your AI, you are renting your intelligence layer. Local models are getting so good now that you can create gameplays like this.👇

🚨 do you understand what is happening?! OpenAI and Anthropic are putting the best models behind approval lists. Meanwhile GLM 5.2 is running locally on a Mac Studio, coding in a loop 24/7. If someone can turn off your AI, you are renting your intelligence layer. Local models are getting so good now that you can create gameplays like this.👇

Vadim

14,609 次观看 • 1 个月前

Here's an example of a creation by GLM 5.2 on Design Arena, made from only one prompt. Just six months ago, there was a big enough difference between open and closed weight models that we considered having two separate evaluations. Open weights have come a long way. Congrats to the Z.ai team for this achievement!

Here's an example of a creation by GLM 5.2 on Design Arena, made from only one prompt. Just six months ago, there was a big enough difference between open and closed weight models that we considered having two separate evaluations. Open weights have come a long way. Congrats to the Z.ai team for this achievement!

Grace Li

14,385 次观看 • 1 个月前

Billy's stupid dance is just to make you notice that a new #manga has just been released with him. 39 pages. An adult woman with big curves will show this boy something new😉 You can download the full manga on my Unifans page. #shota #ショタ #aivideo #AIイラスト

Billy's stupid dance is just to make you notice that a new #manga has just been released with him. 39 pages. An adult woman with big curves will show this boy something new😉 You can download the full manga on my Unifans page. #shota #ショタ #aivideo #AIイラスト

Haru

11,854 次观看 • 1 年前

Big win for open-source LLMs! DeepSeek V4 Pro holds the top open-weights score on SWE-bench Verified, in the GPT-5.5 range. GLM 5.2 leads the open-weight intelligence index and sits near the closed frontier on long-horizon coding. But this leaderboard number is a weak proxy for real performance. It comes from one task set, run through one harness, served at one precision. The same weights can even score differently across providers, since many hosts quantize activations to fp8 and drift the model off its reference weights. Real performance is determined based on whether a model can read a repo, make coordinated edits across files, run the tests, and recover when one breaks. By that measure, the top open models hold up, but only inside the right harness. The teams that actually put DeepSeek V4 into production pipelines as a frontier substitute got there through the harness they built around the model, not by picking a stronger model. If you want to see this in practice, Cline (64k+ stars) has actually built that harness around open models, tuned so they run at production quality. And it's tuned so that these LLMs can run at production quality, with plan and act modes, checkpoints, and terminal feedback. ClinePass is the new access layer on top of it. It runs a curated set of those models inside Cline, narrowed to the ones tested for coding-agent use, with 2 to 5x the standard rate limits and no separate provider accounts, keys, or billing to track. The video below shows the setup, and I worked with the team to put this together. It runs alongside custom keys and local models as well, not in place of them.

Big win for open-source LLMs! DeepSeek V4 Pro holds the top open-weights score on SWE-bench Verified, in the GPT-5.5 range. GLM 5.2 leads the open-weight intelligence index and sits near the closed frontier on long-horizon coding. But this leaderboard number is a weak proxy for real performance. It comes from one task set, run through one harness, served at one precision. The same weights can even score differently across providers, since many hosts quantize activations to fp8 and drift the model off its reference weights. Real performance is determined based on whether a model can read a repo, make coordinated edits across files, run the tests, and recover when one breaks. By that measure, the top open models hold up, but only inside the right harness. The teams that actually put DeepSeek V4 into production pipelines as a frontier substitute got there through the harness they built around the model, not by picking a stronger model. If you want to see this in practice, Cline (64k+ stars) has actually built that harness around open models, tuned so they run at production quality. And it's tuned so that these LLMs can run at production quality, with plan and act modes, checkpoints, and terminal feedback. ClinePass is the new access layer on top of it. It runs a curated set of those models inside Cline, narrowed to the ones tested for coding-agent use, with 2 to 5x the standard rate limits and no separate provider accounts, keys, or billing to track. The video below shows the setup, and I worked with the team to put this together. It runs alongside custom keys and local models as well, not in place of them.

Avi Chawla

44,124 次观看 • 1 个月前

i just tested to put GLM-5.2 on my rig. 753B parameter MoE. 2x RTX PRO 6000 Blackwells, Threadripper PRO 9995WX with 1TB DDR5. prefills at 64 tok/s. decode holds at 13-15. system RAM bandwidth is the bottleneck. running UD-Q4_K_XL 4-bit. 69.7% on Aider Polyglot, within two points of BF16. left the 5090 off. mismatched VRAM unbalances the split. this is what local AI looks like now. massive frontier models on a "desktop rig". how awesome is this? and this is the worst it'll ever be.

i just tested to put GLM-5.2 on my rig. 753B parameter MoE. 2x RTX PRO 6000 Blackwells, Threadripper PRO 9995WX with 1TB DDR5. prefills at 64 tok/s. decode holds at 13-15. system RAM bandwidth is the bottleneck. running UD-Q4_K_XL 4-bit. 69.7% on Aider Polyglot, within two points of BF16. left the 5090 off. mismatched VRAM unbalances the split. this is what local AI looks like now. massive frontier models on a "desktop rig". how awesome is this? and this is the worst it'll ever be.

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48,719 次观看 • 1 个月前

I tested MTPLX v2 with QWEN 3.6 27B and compared it with oMLX without cache on M5 Max and DGX Spark on vllm using nvfp4 model version. More details in 🧵 I've reached 82.8 tps of max decoding speed! 🔥 Custom Metal Kernel design specifically for this model and for Apple Silicon is just perfect! This is the way forward! Great job Youssof Al Toukhi Look at the website here! 👇 Here a website with recap, built with GLM 5.2 running locally 💪 First chart and preview from the website.

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28,378 次观看 • 6 个月前

Qwen has just released a model on par with GPT-4o... And you can run it locally easily 🤯 Yep. GPT-4o level AI running offline on a laptop. - Fully open source - Only 3B active parameters - 262k context length natively Quick steps to run it on your machine and details below

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Paul Couvert

504,191 次观看 • 1 年前

CENSORED AI #Platform is Up!🟢 We know you've been waiting for this moment, now it's finally here! $CENS platform is live and ready to revolutionize the way you interact with technology. This is just the beginning, and we are thrilled to have you with us on this journey

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Some newly released London run clips from Minho's youtube with CHANYEOL ✨️ [singing keep your head down] 🍒 : i usually just run on a treadmill, so this is almost like my first time running outside? but it feels really good? 🏃‍♂️: but running outside is definitely better! when we run on the treadmill, we get tired really fast? but outside, we can somehow keep on running longer!

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dcSpark

16,499 次观看 • 3 年前

I can't believe this is real I have GLM 5.2 running 100% locally on my Mac Studio. 2 bit quant. The results I'm getting are better than Opus 4.8 It's now powering my Hermes Agent and Codex. 100% free, local, private super intelligence on my desk I also have it in a loop coding for me 24/7 now I thought we were at least a year away from this type of event. It happened today. The model takes up about 250gb of memory. So you can technically run it on a Mac Studio with 256gb, but you probably want the 512gb memory version (please tell me you listened to me 5 months ago when these were sitting on store shelves) With Fable gone, I now have Opus 4.8 level intelligence on my desk for free. This is the future. Local, private, secure, personal super intelligence. If you're still writing off local AI as a fad or engagement bait, you are officially delusional

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621,464 次观看 • 1 个月前

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