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GLM 5.2, open frontier-scale intelligence on Microsoft Foundry with AMD MI300X. Running a Codex goal with an open model never felt this good!

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Alvaro Bartolome

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26,695 görüntüleme • 18 gün önce •via X (Twitter)

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Introducing TogetherLink! An open source CLI to run any open source model inside your favorite coding harness. Run GLM 5.2 directly in Codex and Claude Code.
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Introducing TogetherLink! An open source CLI to run any open source model inside your favorite coding harness. Run GLM 5.2 directly in Codex and Claude Code.

Hassan

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Frontier-level agentic capabilities and long-horizon coding, open source and fully private. That's GLM 5.2, now on Venice. Here's a full walkthrough to build a retro video game with GLM 5.2 and OpenCode, starting from a single prompt:
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Frontier-level agentic capabilities and long-horizon coding, open source and fully private. That's GLM 5.2, now on Venice. Here's a full walkthrough to build a retro video game with GLM 5.2 and OpenCode, starting from a single prompt:

Venice

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Truly unbelievable GLM 5.2 just released and it's an open weights model you can run locally The insane part is, it's just as good as Opus 4.8 Unlimited, free super intelligence running on your desk In this video I cover how it works, and how to set up your first local model:
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Truly unbelievable GLM 5.2 just released and it's an open weights model you can run locally The insane part is, it's just as good as Opus 4.8 Unlimited, free super intelligence running on your desk In this video I cover how it works, and how to set up your first local model:

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GLM-5, the latest frontier open model from Z.ai, is available now on Modal. We partnered with to release an endpoint that will be free for a limited time.
0:45

GLM-5, the latest frontier open model from Z.ai, is available now on Modal. We partnered with to release an endpoint that will be free for a limited time.

Modal

58,785 görüntüleme • 5 ay önce

made this game with GLM-5.2 (claude code) running on my 32x RTX 4090s
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made this game with GLM-5.2 (claude code) running on my 32x RTX 4090s

Ning

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I used GLM-5.2 to optimize this visualization I previously made, and it doesn't feel like an open-source model at all. It's amazing.
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I used GLM-5.2 to optimize this visualization I previously made, and it doesn't feel like an open-source model at all. It's amazing.

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GLM 5.2, Q2_K routed experts (effectively ~2.6 bits) running with SSD streaming on an M5 Max 128GB computer.
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GLM 5.2, Q2_K routed experts (effectively ~2.6 bits) running with SSD streaming on an M5 Max 128GB computer.

antirez

133,452 görüntüleme • 21 gün önce

Introducing GLM 5.2 for autoresearch GLM 5.2 is the first open weights model we've tried on our autoresearch pipeline that's proven capable for real research tasks. With Fable 5's restrictions on research, having an open weights alternative is a huge win for open source Watch it carry out fully async vs colocated sync RL training on Harbor code contests across two 8xH100 nodes on top of SkyRL. Resolves setup issues, tracks runs to completion, and produces a full comparison of throughput and reward stability
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Introducing GLM 5.2 for autoresearch GLM 5.2 is the first open weights model we've tried on our autoresearch pipeline that's proven capable for real research tasks. With Fable 5's restrictions on research, having an open weights alternative is a huge win for open source Watch it carry out fully async vs colocated sync RL training on Harbor code contests across two 8xH100 nodes on top of SkyRL. Resolves setup issues, tracks runs to completion, and produces a full comparison of throughput and reward stability

alphaXiv

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Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.
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Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.

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Running OpenClaw with Minimax 2.7 on atomicbot.ai We asked to recreate a legendary game — it built Flappy Bird, but with a lobster 🦞 New Open Source frontier model did this extremely well, big shoutout to MiniMax (official)
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Running OpenClaw with Minimax 2.7 on atomicbot.ai We asked to recreate a legendary game — it built Flappy Bird, but with a lobster 🦞 New Open Source frontier model did this extremely well, big shoutout to MiniMax (official)

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With the launch of GLM 5.2 this week, I see everyone asking "have open models caught up to closed models?" The more interesting question that's getting missed: what can you do with an open model that you can't do with a closed one? You can specialize them. And when you do, the number of economically valuable tasks open models can do actually subsumes that of closed models. Charlie O'Neill explaining this:
1:30

With the launch of GLM 5.2 this week, I see everyone asking "have open models caught up to closed models?" The more interesting question that's getting missed: what can you do with an open model that you can't do with a closed one? You can specialize them. And when you do, the number of economically valuable tasks open models can do actually subsumes that of closed models. Charlie O'Neill explaining this:

Madison Kanna

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Stop stitching vendors together to run open models in production. Fireworks AI on Microsoft Foundry is GA. One endpoint. Enterprise SLAs. Zero setup. Build your next production workload with Fireworks AI on Foundry:
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Stop stitching vendors together to run open models in production. Fireworks AI on Microsoft Foundry is GA. One endpoint. Enterprise SLAs. Zero setup. Build your next production workload with Fireworks AI on Foundry:

Microsoft Azure

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Holy Smokes GLM 5.2 beats Opus in many webdev task😳 This is the best open model , according to jietang said FABLE like model from them before Q1 2027 . i can trust them , just make glm fat with vision we will have fable Left Right
0:13

Holy Smokes GLM 5.2 beats Opus in many webdev task😳 This is the best open model , according to jietang said FABLE like model from them before Q1 2027 . i can trust them , just make glm fat with vision we will have fable Left Right

Chetaslua

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Timelapse #156 (36 hrs) - Worked with the tiny corp on getting GLM 5.2 running on 8xMI300X (sglang won here) - Launched KernelBench-Mega and updated Kernelbench-Hard with h100 and b200 sweeps - Took care of boring business stuff - Did some training sweeps for specialized technical vocab audio model - Some bugs with putting kernelbench-mega and hard on cloud instances so had to do some reruns. Learned a lot though - Setting up my own local rl infra and profiling concurrency 128 rollouts with vllm. Became clear to me that I need to serve in nvfp4, use MoE only for throughput, reap so training doesn’t OOM, dig into the vllm kernel graph itself to not underutilize my hardware from poor flashinfer/cutlass selections for my rtx pro 6000 sm120 architecture - Might do online distillation from glm 5.2 but for now taking it one step at a time - Slept for a bit then woke up and showered - Fixed an issue with SGLang tensor parallel deadlock on GLM 5.2 architecture with MTP enabled - GLM 5.2 inference is 2-3x faster than coding plans and running on amd boxes - Spent time with family - Hung out with some friends - Recorded some yoctogpt lectures with the revamped notebook (high taste btw) - Setting up dflash training for GLM 5.2
0:30

Timelapse #156 (36 hrs) - Worked with the tiny corp on getting GLM 5.2 running on 8xMI300X (sglang won here) - Launched KernelBench-Mega and updated Kernelbench-Hard with h100 and b200 sweeps - Took care of boring business stuff - Did some training sweeps for specialized technical vocab audio model - Some bugs with putting kernelbench-mega and hard on cloud instances so had to do some reruns. Learned a lot though - Setting up my own local rl infra and profiling concurrency 128 rollouts with vllm. Became clear to me that I need to serve in nvfp4, use MoE only for throughput, reap so training doesn’t OOM, dig into the vllm kernel graph itself to not underutilize my hardware from poor flashinfer/cutlass selections for my rtx pro 6000 sm120 architecture - Might do online distillation from glm 5.2 but for now taking it one step at a time - Slept for a bit then woke up and showered - Fixed an issue with SGLang tensor parallel deadlock on GLM 5.2 architecture with MTP enabled - GLM 5.2 inference is 2-3x faster than coding plans and running on amd boxes - Spent time with family - Hung out with some friends - Recorded some yoctogpt lectures with the revamped notebook (high taste btw) - Setting up dflash training for GLM 5.2

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GLM 5.2 in pi running at almost 100 tokens/sec on 8x H200. I really like this model. It feels a frontier-level model you can actually self-host. Next step: squeeze more out of it by benchmarking different quants, tuning the stack, and figuring out the minimum GPU setup people can realistically run it on. Expect more on this soon.

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Z. ai's GLM 5.2 is forcing a new conversation in AI The new benchmark is intelligence per dollar: near-frontier smart yet affordable to run a million times Full livestream with Aaron Levie Gabe Pereyra and Stacy Rasgon in link below
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Z. ai's GLM 5.2 is forcing a new conversation in AI The new benchmark is intelligence per dollar: near-frontier smart yet affordable to run a million times Full livestream with Aaron Levie Gabe Pereyra and Stacy Rasgon in link below

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“Any model, any agent, any accelerator, and any cloud.” Red Hat’s Brian Stevens explains why open AI infrastructure matters as enterprises scale agentic AI workloads, and how Red Hat and AMD are collaborating to help simplify deployment with AMD EPYC processors and AMD Instinct GPUs. Watch the full video on YouTube:
1:02

“Any model, any agent, any accelerator, and any cloud.” Red Hat’s Brian Stevens explains why open AI infrastructure matters as enterprises scale agentic AI workloads, and how Red Hat and AMD are collaborating to help simplify deployment with AMD EPYC processors and AMD Instinct GPUs. Watch the full video on YouTube:

AMD

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It's over --- This entire 7 minute podcast was 100% generated on my local PC, using an open source model called Vibevoice, from Microsoft.
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It's over --- This entire 7 minute podcast was 100% generated on my local PC, using an open source model called Vibevoice, from Microsoft.

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DeepSeek released R1, the best open-source reasoning model that is on par with OpenAI's o1! I have built a RAG app to chat with your PDF files using the DeepSeek R1 model, running locally on your computer. 100% Open Source

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Jensen Huang says the industry is shifting from generative AI to agentic AI The next major step is fusing public cloud frontier models with customized open-source systems running on enterprise servers But the end goal is physical: "moving intelligence from the cloud into industrial AI, factories and robotics"
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Jensen Huang says the industry is shifting from generative AI to agentic AI The next major step is fusing public cloud frontier models with customized open-source systems running on enterprise servers But the end goal is physical: "moving intelligence from the cloud into industrial AI, factories and robotics"

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