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Build & Train a GLM-5.3-Flash Model From Scratch I built a 25.7M-parameter educational model inspired by GLM-5.3-Flash, then tested its architecture, pretraining, and executable-reward reinforcement learning end to end. Repository: YouTube: Become AI researcher in 90 days:

Vuk Rosić 武克

2,706 subscribers

14,590 views • 1 month ago •via X (Twitter)

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4 Comments

Maverick1 month ago

That's actually awesome

Yechan Do1 month ago

Pretty cool !

basedcapital1 month ago

which glm-inspired component transferred least cleanly at 25.7m parameters, and what did you adapt for the educational model instead of preserving the architecture?

剑的 AI 实验室1 month ago

@VukRosic99 剑的 AI 实验室已核验 vukrosic/glm-5.3-flash-from-scratch，并收录到项目页：。我们记录了 25.7M 参数教育模型和端到端训练说明，目前尚未实测；欢迎补充最小任务或纠正信息。

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53,159 views • 9 days ago

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atomic.chat

32,515 views • 28 days ago

We tested GLM 5.3 Flash and Qwen 3.8 Flash in Pi. Same task for both: use ego lite to build an interactive 3D solar system. Qwen cost 3.6x more and ran 1.7x longer, but the output tells a different story. Comment "prompt" below and we'll drop it.

We tested GLM 5.3 Flash and Qwen 3.8 Flash in Pi. Same task for both: use ego lite to build an interactive 3D solar system. Qwen cost 3.6x more and ran 1.7x longer, but the output tells a different story. Comment "prompt" below and we'll drop it.

ego

24,535 views • 1 month ago

More AI use ≠ more impact. Today, Snowflake is introducing dynamic model routing in Cortex AI Gateway + support for DeepSeek-V4-Flash 0731 and GLM-5.3. A step toward intelligence efficiency: turning AI resources into tangible business impact. More in my latest blog 👇

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53,288 views • 1 month ago

glm 5.3 flash is 7.5x cheaper, but 3.4x slower than gemini 3.7 flash Z.ai glm 5.3 flash – shipped aug 26, $0.07/$0.25 per 1m Google DeepMind gemini 3.7 flash – shipped aug 13, $0.38/$1.88 per 1m we put the two models on one job: write one html file that draws an animated 3d scene in the browser. no images, no downloads, and it has to look the same on every load. the setup: three scenes – a glass aquarium in a lit room, the solar system, a night city under a thunderstorm. identical brief word for word, reasoning effort high, 64k output cap. the numbers below are not the whole run. they cover the three scenes we kept – the best one per task from each model, the ones in the video. - total generation time for the three scenes #1 gemini 3.7 flash – 10m 36s #2 glm 5.3 flash – 36m 30s - tokens spent on those three scenes #1 glm 5.3 flash – 110k #2 gemini 3.7 flash – 111k - cost of those three scenes #1 glm 5.3 flash – $0.027 #2 gemini 3.7 flash – $0.202 observations: • glm's first 10 attempts: 7 blank pages. it kept inventing short random helpers and forgetting to define one of them. the fix was one line in the brief: use exactly one random helper, named rand(), and don't invent shorthands next to it. next 12 attempts: 11 alive, 0 crashes. • glm spends 66% of its output on reasoning, gemini 57%. that is the whole speed gap. • gemini's storm came back as a black rectangle in 4 of 6 runs. glm's best storm has a branching bolt, lit rain and wet asphalt – for $0.01. conclusion: same three scenes, same token spend – glm 5.3 flash billed $0.027 and took 36m 30s, gemini 3.7 flash billed $0.202 and took 10m 36s. glm wins gemini on price and made the best storm of the whole run follow thehype. for 24/7 ai news, analysis and breakdowns

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thehype.

16,041 views • 1 month ago

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Tim Jayas

78,962 views • 1 month ago

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140,726 views • 1 month ago

GLM-5.3-Flash (320B MoE) serving on Kaggle's free TPU: ~64 tok/s, 262k context. No stack could run this model on a TPU, so I wrote a JAX engine for it: 3-bit experts (from Unsloth AI) in HBM; Pallas kernels decode them inside the matmul. Z.ai Kaggle

GLM-5.3-Flash (320B MoE) serving on Kaggle's free TPU: ~64 tok/s, 262k context. No stack could run this model on a TPU, so I wrote a JAX engine for it: 3-bit experts (from Unsloth AI) in HBM; Pallas kernels decode them inside the matmul. Z.ai Kaggle

Abdur Rahim

108,050 views • 15 days ago

glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns

glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

26,360 views • 1 month ago

GLM-5.3 weights from Z.ai are live, with SGLang powering day-0 serving support! GLM-5.3 inherits every optimization and feature we battle-tested for GLM-5.2 over the past months. On real-world multi-turn agentic workloads, we measured 537.6 tok/s/user on NVFP4 and 413 tok/s/user on FP8, at BS=1 with TP8 on 8x B300. It's fast, efficient, and production-ready today on NVIDIA AI Blackwell and Hopper, and AI at AMD MI300X/325X/355X. We believe this is a big step forward for GLM-Series in agentic tasks, with a path toward mythos-class cyber capability. Can't wait to see what people build with GLM-5.3 and SGLang🚀 Cookbook👇

GLM-5.3 weights from Z.ai are live, with SGLang powering day-0 serving support! GLM-5.3 inherits every optimization and feature we battle-tested for GLM-5.2 over the past months. On real-world multi-turn agentic workloads, we measured 537.6 tok/s/user on NVFP4 and 413 tok/s/user on FP8, at BS=1 with TP8 on 8x B300. It's fast, efficient, and production-ready today on NVIDIA AI Blackwell and Hopper, and AI at AMD MI300X/325X/355X. We believe this is a big step forward for GLM-Series in agentic tasks, with a path toward mythos-class cyber capability. Can't wait to see what people build with GLM-5.3 and SGLang🚀 Cookbook👇

SGLang

99,455 views • 1 month ago

some seriously good AI models are sitting at $0 right now 😳 KiosAPI is showing free access to: - MiniMax M3 - Kimi K3 - GLM 5.3 Flash - DeepSeek V4 Flash getting started: 1. go to 2. create your account 3. generate your API key 4. base URL: 5. select a $0 model > OpenAI-compatible API worth checking 👀

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K2S

48,139 views • 25 days ago

Not every AI interaction is best served with a large frontier model. There is a long tail of trillion-token use cases, from tagging to search, best served by a sub-10b-parameter model that runs in milliseconds and costs many orders of magnitude less than the frontier. We built Freesolo Flash to make training loops like SFT and RL easy and end-to-end completable through your coding agent.

Not every AI interaction is best served with a large frontier model. There is a long tail of trillion-token use cases, from tagging to search, best served by a sub-10b-parameter model that runs in milliseconds and costs many orders of magnitude less than the frontier. We built Freesolo Flash to make training loops like SFT and RL easy and end-to-end completable through your coding agent.

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29,011 views • 2 months ago

i burned through my entire GPT-6 Astra weekly quota in 3 days so i'm replacing my $200/mo Codex subscription with this... a free 32,960-star GitHub repo that runs GLM-5.3 Flash, Kimi K3, and DeepSeek V4 Flash locally [here's the exact setup i used:] 1. get colibri download the release archive for Linux, macOS, or Windows. no compiler needed. python3 coli info 2. or build it from source git clone cd colibri/c ./setup.sh cd .. 3. pick a model GLM-5.3 Flash: 321B, ~195 GB converted, 25 GB RAM DeepSeek V4 Flash: 284B, ~167 GB, or ~85 GB for the REAP 150B version Kimi K3: 2.8T, ~1.6 TB snapshot, 32 GB+ RAM none of them needs a GPU. a GPU only makes some runs faster. 4. get the weights GLM-5.3 Flash uses the dedicated converter: python3 tools/convert_glm53.py --outdir /nvme/glm53_i4 --min-free-gb 30 DeepSeek V4 Flash and Kimi K3 load from their original Hugging Face snapshots. pre-converted containers work too. 5. run it COLI_MODEL=/nvme/glm53_i4 ./coli chat ./coli web --model /nvme/glm53_i4 # dashboard ./coli serve --model /nvme/glm53_i4 # headless API 6. tune it ./coli doctor # readiness check ./coli plan # RAM/VRAM/disk placement ./coli tune # fastest safe profile `coli` reads `config.json` and picks the right engine for each model. the tradeoff is honest: no token bill, no hosted quota... but you need the storage. the frontier moved from your subscription to your SSD.

Avid

429,264 views • 15 days ago

This is what the future of coding looks like. Multiple AI models working in the same Codex session, with Jev routing them to tackle different parts of the task. Here, I had it build a full-stack demo app: > Opus 5.5 planned it. > GPT-6 Astra built the backend. > Kimi K3 built the frontend. > DeepSeek handled testing. > GLM 5.3 Flash wrote the docs. Jev handles the switching. I never touched the model picker. 40% cheaper, 15% faster for the same build.

This is what the future of coding looks like. Multiple AI models working in the same Codex session, with Jev routing them to tackle different parts of the task. Here, I had it build a full-stack demo app: > Opus 5.5 planned it. > GPT-6 Astra built the backend. > Kimi K3 built the frontend. > DeepSeek handled testing. > GLM 5.3 Flash wrote the docs. Jev handles the switching. I never touched the model picker. 40% cheaper, 15% faster for the same build.

elvis

16,192 views • 2 days ago

DeepSeek V4.1 Flash made DOOM in Atomic Chat 🐳 DeepSeek dropped a new architecture for the flash model so we tested it by making Doom in an html file and it built a real raycasting shooter with gun mechanics and enemy waves Run AI models locally ->

DeepSeek V4.1 Flash made DOOM in Atomic Chat 🐳 DeepSeek dropped a new architecture for the flash model so we tested it by making Doom in an html file and it built a real raycasting shooter with gun mechanics and enemy waves Run AI models locally ->

atomic.chat

29,435 views • 20 days ago

Don't waste 2 years learning to build LLMs like Claude & ChatGPT. Stanford just dropped a 2 hour course on how to build LLMs from scratch. 00:00 - break text into tokens 25:44 - decode a user prompt 35:40 - train the model end to end 1:16:47 - build the architecture from scratch Anthropic pays $750,000/year to engineers who understand this exact knowledge of LLMs. Watch it, then go further with the step-by-step guide below on how to build a system that improves itself.

Don't waste 2 years learning to build LLMs like Claude & ChatGPT. Stanford just dropped a 2 hour course on how to build LLMs from scratch. 00:00 - break text into tokens 25:44 - decode a user prompt 35:40 - train the model end to end 1:16:47 - build the architecture from scratch Anthropic pays $750,000/year to engineers who understand this exact knowledge of LLMs. Watch it, then go further with the step-by-step guide below on how to build a system that improves itself.

Anatoli Kopadze

106,621 views • 1 month ago

Dragonfly's Haseeb Qureshi ＞|＜ reveals how GLM 5.3 Abliterated could unlock a new era of AI cyber attacks by training away the model's refusal behavior: "The models are just now getting good enough. Especially open source. For the frontier models, you used to be able to jailbreak them enough to be able to do these kinds of malicious activities, but their safety guardrails have improved dramatically over the last year or two." "But now we have open models. There was a model released yesterday which is an abliteration of GLM 5.3. Abliteration is when you take a model and fine-tune it against the refusal direction, which gets the model to refuse less and less, which ultimately gets you something close to an unrestricted or uncensored model." "So they did that to GLM 5.3, which means this abliterated version will just hack things. It will just do whatever GLM 5.3 will refuse to do. You lose some performance, but this is kind of a killing machine. If you wanna just let this loose on cyber attacks, that's what you use, and that was just released open weights yesterday." "You will see a lot more of this. This is not the end. This is only the beginning of where this is going."

Dragonfly's Haseeb Qureshi ＞|＜ reveals how GLM 5.3 Abliterated could unlock a new era of AI cyber attacks by training away the model's refusal behavior: "The models are just now getting good enough. Especially open source. For the frontier models, you used to be able to jailbreak them enough to be able to do these kinds of malicious activities, but their safety guardrails have improved dramatically over the last year or two." "But now we have open models. There was a model released yesterday which is an abliteration of GLM 5.3. Abliteration is when you take a model and fine-tune it against the refusal direction, which gets the model to refuse less and less, which ultimately gets you something close to an unrestricted or uncensored model." "So they did that to GLM 5.3, which means this abliterated version will just hack things. It will just do whatever GLM 5.3 will refuse to do. You lose some performance, but this is kind of a killing machine. If you wanna just let this loose on cyber attacks, that's what you use, and that was just released open weights yesterday." "You will see a lot more of this. This is not the end. This is only the beginning of where this is going."

MTS

17,726 views • 29 days ago

New course: Build and Train an LLM with JAX, built in partnership with Google and taught by Chris Achard. JAX is the open-source library behind Google's Gemini, Veo, and other advanced models. This short course teaches you to build and train a 20-million parameter language model from scratch using JAX and its ecosystem of tools. You'll implement a complete MiniGPT-style architecture from scratch, train it, and chat with your finished model through a graphical interface. Skills you'll gain: - Learn JAX's core primitives: automatic differentiation, JIT compilation, and vectorized execution - Build a MiniGPT-style LLM using Flax/NNX, implementing embedding and transformer blocks - Load a pretrained MiniGPT model and run inference through a chat interface Come learn this important software layer for building LLMs!

New course: Build and Train an LLM with JAX, built in partnership with Google and taught by Chris Achard. JAX is the open-source library behind Google's Gemini, Veo, and other advanced models. This short course teaches you to build and train a 20-million parameter language model from scratch using JAX and its ecosystem of tools. You'll implement a complete MiniGPT-style architecture from scratch, train it, and chat with your finished model through a graphical interface. Skills you'll gain: - Learn JAX's core primitives: automatic differentiation, JIT compilation, and vectorized execution - Build a MiniGPT-style LLM using Flax/NNX, implementing embedding and transformer blocks - Load a pretrained MiniGPT model and run inference through a chat interface Come learn this important software layer for building LLMs!

Andrew Ng

194,547 views • 7 months ago

Qwen3.8 Flash Next built a whole shooter from one prompt. I hit Enter and left. omp with nothing but that model wired in. no follow ups, no me watching it. Hours later I opened the logs. It was taking screenshots of its own game, looking at them, and going back to fix what looked off. I never asked for that. The lighting is the part I did not expect. This is just an incredibly beautiful and voluminous light. What one prompt cost: 5h 22m wall time 70M tokens read from cache 2.9M fresh input 429K output, 301K of it reasoning Same prompt goes to glm-5.3 flash next. Then Ill know if that was the model or luck. Link in reply

Qwen3.8 Flash Next built a whole shooter from one prompt. I hit Enter and left. omp with nothing but that model wired in. no follow ups, no me watching it. Hours later I opened the logs. It was taking screenshots of its own game, looking at them, and going back to fix what looked off. I never asked for that. The lighting is the part I did not expect. This is just an incredibly beautiful and voluminous light. What one prompt cost: 5h 22m wall time 70M tokens read from cache 2.9M fresh input 429K output, 301K of it reasoning Same prompt goes to glm-5.3 flash next. Then Ill know if that was the model or luck. Link in reply

Alexey Fateev

21,996 views • 1 month ago