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This is GLM-5.2 running on 3x DGX Sparks

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Mia

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41,196 görüntüleme • 9 gün önce •via X (Twitter)

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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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Today, we're introducing GLM-5.2 Fast: our GLM-5.2 Model API designed for the most demanding real-time use cases. The Fast tier delivers 2-3x higher TPS than the standard GLM-5.2 MAPI.
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Today, we're introducing GLM-5.2 Fast: our GLM-5.2 Model API designed for the most demanding real-time use cases. The Fast tier delivers 2-3x higher TPS than the standard GLM-5.2 MAPI.

Baseten

113,989 görüntüleme • 13 gün önce

Loading DeepSeek v4 Flash just reminded me how good and fast this model is and how well it runs on DGX Sparks. GLM-5.2 is really awesome, but if you want fast and concurrent inference, DS4F is still a top choice IMO. Created by DS4F in single shot The coffee guy
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Loading DeepSeek v4 Flash just reminded me how good and fast this model is and how well it runs on DGX Sparks. GLM-5.2 is really awesome, but if you want fast and concurrent inference, DS4F is still a top choice IMO. Created by DS4F in single shot The coffee guy

Mia

39,686 görüntüleme • 9 gün önce

GLM 5.2 @ 19.8 tok/sec on 2 DGX Sparks ⚡️ I switched to a dspark draft model with K=2 (model from Red Hat AI) Acceptance is ~68% because the drafter was trained with the full FP8 model... Next step is to fine tune the drafter so acceptance on this super quant goes up
0:30

GLM 5.2 @ 19.8 tok/sec on 2 DGX Sparks ⚡️ I switched to a dspark draft model with K=2 (model from Red Hat AI) Acceptance is ~68% because the drafter was trained with the full FP8 model... Next step is to fine tune the drafter so acceptance on this super quant goes up

Joe Muller

73,145 görüntüleme • 21 gün önce

Running Qwen3.5-397B-A17B (4bit quants, 177 GB) on two DGX Sparks using llama.cpp with RPC and RDMA:
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Running Qwen3.5-397B-A17B (4bit quants, 177 GB) on two DGX Sparks using llama.cpp with RPC and RDMA:

Radoslav Gerganov

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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

318,015 görüntüleme • 1 ay önce

GLM 5.2 runs pretty fast on Modal.
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GLM 5.2 runs pretty fast on Modal.

Charles 🎉 Frye

20,342 görüntüleme • 1 ay önce

Running a Hermes Agent with GLM 5.2 on a Computer the size of a Deck of Playing Cards 🔥🔥🔥
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Running a Hermes Agent with GLM 5.2 on a Computer the size of a Deck of Playing Cards 🔥🔥🔥

Tech2Wild

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GLM-5.2 is a bigger deal than you think. > what GLM-5.2 actually scores (0:46) > "it's just a rich person's toy" (4:27) > why GLM-5.2 matters even if you never run it (7:19)
12:25

GLM-5.2 is a bigger deal than you think. > what GLM-5.2 actually scores (0:46) > "it's just a rich person's toy" (4:27) > why GLM-5.2 matters even if you never run it (7:19)

Rob Hallam

19,906 görüntüleme • 1 ay önce

We ran a test comparing GLM 5.2 vs Sonnet 5. We used the same prompt, with /design in the command code. The difference is big - effects, motion, and especially the footer. Sonnet 5 had nice hover effects, but didn't come close to GLM 5.2 GLM 5.2 wins on quality and pricing.
0:23

We ran a test comparing GLM 5.2 vs Sonnet 5. We used the same prompt, with /design in the command code. The difference is big - effects, motion, and especially the footer. Sonnet 5 had nice hover effects, but didn't come close to GLM 5.2 GLM 5.2 wins on quality and pricing.

Command Code

44,260 görüntüleme • 1 ay önce

GLM-5.2 running under my desk, modifying GGUFs to enable MTP on the Spark. What a world we live in, where you can have a megamind running off electrons going really fast.
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GLM-5.2 running under my desk, modifying GGUFs to enable MTP on the Spark. What a world we live in, where you can have a megamind running off electrons going really fast.

0xSero

23,422 görüntüleme • 1 ay önce

we asked GLM 5.2 and Claude Opus 4.8 to find and fix the bugs in two different scripts while Opus 4.8 finished a few seconds faster in both tests, GLM 5.2 burned roughly 17k–38k fewer tokens and absolutely crushed it on cost, coming in 15x to 22x cheaper! Test 1: GLM 5.2 ($0.068) vs. Opus 4.8 ($1.06) Test 2: GLM 5.2 ($0.045) vs. Opus 4.8 ($1.02)
0:23

we asked GLM 5.2 and Claude Opus 4.8 to find and fix the bugs in two different scripts while Opus 4.8 finished a few seconds faster in both tests, GLM 5.2 burned roughly 17k–38k fewer tokens and absolutely crushed it on cost, coming in 15x to 22x cheaper! Test 1: GLM 5.2 ($0.068) vs. Opus 4.8 ($1.06) Test 2: GLM 5.2 ($0.045) vs. Opus 4.8 ($1.02)

GMI Cloud

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Which DGX Spark is running Gemma 4?
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Which DGX Spark is running Gemma 4?

Alex Ziskind

25,291 görüntüleme • 2 ay önce

GLM 5.2 vs Kimi K2.7 both were tested on same settings with same prompt > GLM one shotted everything in 35 mins > Kimi needed extra prompts to fix movements and took 30 mins super surpised by how good GLM 5.2 is, while being so cheap which one do you think won?
0:35

GLM 5.2 vs Kimi K2.7 both were tested on same settings with same prompt > GLM one shotted everything in 35 mins > Kimi needed extra prompts to fix movements and took 30 mins super surpised by how good GLM 5.2 is, while being so cheap which one do you think won?

J A Z I I

63,741 görüntüleme • 1 ay önce

I was skeptical about giving GLM-5.2 the task to build a super mario snes style game in a single HTML file so i ran the test on Opus and also on GLM-5.2 Opus didnt even try to make a game or made something but it wasn't a game GLM-5.2 one shotted a pretty functional game.. WILD
0:45

I was skeptical about giving GLM-5.2 the task to build a super mario snes style game in a single HTML file so i ran the test on Opus and also on GLM-5.2 Opus didnt even try to make a game or made something but it wasn't a game GLM-5.2 one shotted a pretty functional game.. WILD

Hermes Agent Tips

32,883 görüntüleme • 1 ay önce

DeepSeek V4 Flash running on the DGX Station.
0:37

DeepSeek V4 Flash running on the DGX Station.

Matthew Berman

70,555 görüntüleme • 3 ay önce

GLM 5.2 Easy Setup with Baseten + Droid harness. 00:00 Fastest GLM 5.2 Setup Goal 00:32 Getting a Baseten API Key 01:09 Automated Droid AI Configuration 02:23 Testing the GLM 5.2 Integration 03:07 Alternative Setups and Limitations 04:14 Bonus: Setup for Other IDEs
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GLM 5.2 Easy Setup with Baseten + Droid harness. 00:00 Fastest GLM 5.2 Setup Goal 00:32 Getting a Baseten API Key 01:09 Automated Droid AI Configuration 02:23 Testing the GLM 5.2 Integration 03:07 Alternative Setups and Limitations 04:14 Bonus: Setup for Other IDEs

Ray Fernando

10,580 görüntüleme • 1 ay önce

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

Elliot Arledge

45,447 görüntüleme • 1 ay önce

running Qwen3.5 397B MoE (17B active/token) on 4x DGX Sparks in FP8 (~400GB) > OpenCode driving > agent exploring its own config > probing all 4 Sparks (via ssh) + reporting thermals > inspecting how vLLM is serving it > collecting + analyzing its own stats local AI is awesome
3:02

running Qwen3.5 397B MoE (17B active/token) on 4x DGX Sparks in FP8 (~400GB) > OpenCode driving > agent exploring its own config > probing all 4 Sparks (via ssh) + reporting thermals > inspecting how vLLM is serving it > collecting + analyzing its own stats local AI is awesome

Ahmad

122,543 görüntüleme • 3 ay önce

GLM-5.2确实有点东西
0:21

GLM-5.2确实有点东西

关木

58,228 görüntüleme • 1 ay önce