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I made Blender node previews work on ANY PC… No GPU? Still fast. Low-end setup? Still smooth. High-end GPU? Even better. Blender 4.0+ supported #b3d #blender3d #gamedev #3dart

Mix 3D

2,816 subscribers

30,823 просмотров • 5 месяцев назад •via X (Twitter)

Искусство Наука и технологии #b3d #blender3d #gamedev #3dart

Anya Rossi• Live Now

Private livecam show

Комментарии: 26

Фото профиля Mix 3D

Mix 3D5 месяцев назад

its available now with early birds discount :

Фото профиля Mix 3D

Mix 3D5 месяцев назад

the demo shows low res there is more resolutions to choose from

Фото профиля Precious Cypher 🥇

Precious Cypher 🥇5 месяцев назад

So when I assigned materials to a model I see the previews ?

Фото профиля Mix 3D

Mix 3D5 месяцев назад

you can actually view the last node for the final result

Фото профиля Precious Cypher 🥇

Precious Cypher 🥇5 месяцев назад

Hmm interesting Does it take the shape of the model ?

Фото профиля Mix 3D

Mix 3D5 месяцев назад

its a shader based preview it actually nap it to a plane to view it fast so you can see what the mixture of nodes your making as quick as possible while you're on full screen for example

Фото профиля VersaTale

VersaTale5 месяцев назад

This is super fast and responsive compared to Node Preview(some other addon I have)

Фото профиля Mix 3D

Mix 3D5 месяцев назад

this one is gpu based the other addons i saw is cpu based which is slow

Фото профиля James George

James George5 месяцев назад

That's awesome 😎

Фото профиля Mix 3D

Mix 3D5 месяцев назад

oh thanks James

Фото профиля Precious Cypher 🥇

Precious Cypher 🥇5 месяцев назад

How can I get it ?

Фото профиля Mix 3D

Mix 3D5 месяцев назад

it's going to be available in next hours at

Фото профиля Caleb_trynabeanartist

Caleb_trynabeanartist5 месяцев назад

Extremely cool.

Фото профиля Mix 3D

Mix 3D5 месяцев назад

thanks i will share it when it's done on my page

Фото профиля Thomas Schmall

Thomas Schmall5 месяцев назад

Super neat - it works in blender 5.1 in first little tryouts I did. I wasn't quite sure which of the two files is the newest version. Maybe that could be made more clear. Also it says it needs cycles, but it did work with Eevee so far.

Фото профиля Mix 3D

Mix 3D5 месяцев назад

i hope you liked it and if you face any issues just type it down in the post comments so i fix it.

Фото профиля Thomas Schmall

Thomas Schmall5 месяцев назад

Thanks. I will. It showed only black previews at the beginning. A "clear cache" fixed it - since then it's going strong 🙂

Фото профиля Mix 3D

Mix 3D5 месяцев назад

thanks mate for feedback.

Фото профиля QUI

QUI5 месяцев назад

looks cool

Фото профиля Mix 3D

Mix 3D5 месяцев назад

thank you Qui

Фото профиля Serac111

Serac1115 месяцев назад

Works with GeoNodes?

Фото профиля Mix 3D

Mix 3D5 месяцев назад

it only works for shaders. GeoNodes are dealing with meshes and stuff like this it's better to not use something like this for it

Фото профиля Rhal

Rhal5 месяцев назад

This unbelievably complex tech will be officially added in 10 years.

Фото профиля Mix 3D

Mix 3D5 месяцев назад

the complex part is actually getting it to be that fast and crisp looking the dev of this addon started in 2025 the first one was slow as heck and not even good. and now look at that works like a champ

Фото профиля Rhal

Rhal5 месяцев назад

Amazing work. Hopefully this will work with any node.

Фото профиля Mix 3D

Mix 3D5 месяцев назад

haven't tested all of them but i can say works with all nodes i have used in my journey with blender so should be fine. if there is any probem with it just send a report to my email so i can fix it. here will i post it

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13,817 просмотров • 8 месяцев назад

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41,896 просмотров • 2 дней назад

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@levelsio

261,455 просмотров • 2 лет назад

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35,554 просмотров • 3 дней назад

Astra built this three.js game with gorgeous graphics, then recreated every scene in Blender and made the trailer too. It feels like a massive upgrade from Sol for 3D graphics and front-end design. The UI feels way more polished and less like AI slop. The rocks, trees, monuments, and characters are all procedural code, keeping the game under 2 MB before the image and music assets. The level of autonomy is unbelievable. It needed very little steering, though I still had to keep pushing it to work harder on the details. The UI icons, buttons, and ornaments were made as transparent PNGs with GPT Image 2.

Astra built this three.js game with gorgeous graphics, then recreated every scene in Blender and made the trailer too. It feels like a massive upgrade from Sol for 3D graphics and front-end design. The UI feels way more polished and less like AI slop. The rocks, trees, monuments, and characters are all procedural code, keeping the game under 2 MB before the image and music assets. The level of autonomy is unbelievable. It needed very little steering, though I still had to keep pushing it to work harder on the details. The UI icons, buttons, and ornaments were made as transparent PNGs with GPT Image 2.

Meng To

134,358 просмотров • 7 дней назад

Not bad! 2K triangles and a bit of manual cleanup. This was a quick test creating a lowpoly 3D model. Tried Tripo for retopology and it did a good job. It does render some inverted normals though. For this lowpoly model it’s fine but inverted normals are a pain when working on highpoly models. I’m going to test some other options like the remesh tool in Blender sculpting. It’s crazy how this Trellis Texturing node works btw, It understands the geometry very well. Watch the end where I made the worst version of an Axe in Blender and it was able to wrap the texture on that model! I would probably spend a bit more time splitting the image up and generating the main parts separately. Also making any signs readable or just remove them.

Not bad! 2K triangles and a bit of manual cleanup. This was a quick test creating a lowpoly 3D model. Tried Tripo for retopology and it did a good job. It does render some inverted normals though. For this lowpoly model it’s fine but inverted normals are a pain when working on highpoly models. I’m going to test some other options like the remesh tool in Blender sculpting. It’s crazy how this Trellis Texturing node works btw, It understands the geometry very well. Watch the end where I made the worst version of an Axe in Blender and it was able to wrap the texture on that model! I would probably spend a bit more time splitting the image up and generating the main parts separately. Also making any signs readable or just remove them.

Jerome | InsaneUnreal

15,052 просмотров • 7 месяцев назад

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EL REA 🇦🇷

10,813 просмотров • 1 год назад

Dylan Patel of SemiAnalysis says a worse GPU with better storage and memory now beats the best chip without them, so buying the newest GPU alone no longer wins inference. So, an AMD GPU with more memory can outperform Nvidia in some cases. "So what we have is we have over $80 million of compute, GPUs from Nvidia, AMD, TPUs from Google, Trainium from Amazon, and we run this benchmark constantly on the newest inference engine, newest drivers, newest PyTorch version, whatever it is." "Every day it runs on an automated CI, and we run it on all the latest Chinese models, from GLM, Zhipu, Moonshot, Kimi, Alibaba, all these models we run." "Initially, when we were benchmarking the difference between these chips and different engines, different schemes for parallelism, we were just running it fixed context length." "But now with Agent X, we've analyzed over $5 million worth of Claude Code traces. This is real production traffic that people have donated to us as well as internally generated. Now we know what the actual agent workload looks like." "And then as we implement that and run those benchmarks, it turns out yes, the chip you're using is very important, but now even more important is how are you handling this memory offload?" "And so while an Nvidia GPU is faster than an AMD GPU in most cases, because AMD GPUs have more memory, they actually end up outperforming in some cases." "Or you can have a worse GPU, but a much better storage solution, and now you can outperform what the best GPU can do without those solutions. So just buying the newest and latest GPU alone doesn't get you the best inference economics." "Actually, you need to layer in all these other innovations including storage and memory." [ Who's the top player on your chart? ] "That really is a difficult multivariable problem. And generally that means you need to have, yes, you need to have the best GPU, a GB300, but you also need to have the best storage solutions. And so I won't spoil who's the best right here, but I will say that storage solutions matter a lot and memory solutions matter a lot, as does your front-end networking. That matters a lot."

Dylan Patel of SemiAnalysis says a worse GPU with better storage and memory now beats the best chip without them, so buying the newest GPU alone no longer wins inference. So, an AMD GPU with more memory can outperform Nvidia in some cases. "So what we have is we have over $80 million of compute, GPUs from Nvidia, AMD, TPUs from Google, Trainium from Amazon, and we run this benchmark constantly on the newest inference engine, newest drivers, newest PyTorch version, whatever it is." "Every day it runs on an automated CI, and we run it on all the latest Chinese models, from GLM, Zhipu, Moonshot, Kimi, Alibaba, all these models we run." "Initially, when we were benchmarking the difference between these chips and different engines, different schemes for parallelism, we were just running it fixed context length." "But now with Agent X, we've analyzed over $5 million worth of Claude Code traces. This is real production traffic that people have donated to us as well as internally generated. Now we know what the actual agent workload looks like." "And then as we implement that and run those benchmarks, it turns out yes, the chip you're using is very important, but now even more important is how are you handling this memory offload?" "And so while an Nvidia GPU is faster than an AMD GPU in most cases, because AMD GPUs have more memory, they actually end up outperforming in some cases." "Or you can have a worse GPU, but a much better storage solution, and now you can outperform what the best GPU can do without those solutions. So just buying the newest and latest GPU alone doesn't get you the best inference economics." "Actually, you need to layer in all these other innovations including storage and memory." [ Who's the top player on your chart? ] "That really is a difficult multivariable problem. And generally that means you need to have, yes, you need to have the best GPU, a GB300, but you also need to have the best storage solutions. And so I won't spoil who's the best right here, but I will say that storage solutions matter a lot and memory solutions matter a lot, as does your front-end networking. That matters a lot."

Fireside Alpha

178,511 просмотров • 1 месяц назад

The reason why Reflections in GTA 6 look noisy and blurry isn't because Rockstar Games doesn't know how to do them or that a $2 billion budget magically fixes this. It all comes down to the GPU in current gen consoles, these consoles don't have the power they need to render high resolution Ray Traced reflections in real-time and corners had to be cut. Here I'm running GTA 5 enhanced for PC at ultra settings to show you how good and clean the reflections look if you have high-end and recent hardware that have AI upscaling to make this possible. That being said, The PS5 Pro console is much more powerful compared to the base PS5 (the footage which GTA 6 was captured) offering higher resolution, 45% faster rendering, 2X-3X better Ray Tracing performance and PSSR upscaling. With the PS5 Pro, it will have significantly better looking and sharper reflections somewhat close to what GTA 5 Enhanced reflections look like on a gaming PC at high settings. However, the ultimate experience will still be on PC when GTA 6 launches, allowing for the highest quality Ray Tracing or even Path Tracing if Rockstar Games decides to implement it.

The reason why Reflections in GTA 6 look noisy and blurry isn't because Rockstar Games doesn't know how to do them or that a $2 billion budget magically fixes this. It all comes down to the GPU in current gen consoles, these consoles don't have the power they need to render high resolution Ray Traced reflections in real-time and corners had to be cut. Here I'm running GTA 5 enhanced for PC at ultra settings to show you how good and clean the reflections look if you have high-end and recent hardware that have AI upscaling to make this possible. That being said, The PS5 Pro console is much more powerful compared to the base PS5 (the footage which GTA 6 was captured) offering higher resolution, 45% faster rendering, 2X-3X better Ray Tracing performance and PSSR upscaling. With the PS5 Pro, it will have significantly better looking and sharper reflections somewhat close to what GTA 5 Enhanced reflections look like on a gaming PC at high settings. However, the ultimate experience will still be on PC when GTA 6 launches, allowing for the highest quality Ray Tracing or even Path Tracing if Rockstar Games decides to implement it.

NikTek

142,884 просмотров • 12 дней назад

UC Berkeley just open-sourced FreeToken. (2–4x faster local LLM inference than Ollama) the results are wild: - Qwen3.6-35B on an 8GB GPU at 39.3 tokens/s - DeepSeek-V4-Flash 284B on a 32GB GPU at 22 tokens/s - GLM-5.2 753B on a 96GB GPU at 14.9 tokens/s a 35B model at 16-bit precision needs about 70GB just for its weights. even at 4 bits it is close to 18GB, and FreeToken serves it on an 8GB GPU. let me explain how: all three models mentioned above are Mixture-of-Experts, and that is what FreeToken takes advantage of. each layer holds hundreds of separate experts plus a small router that picks a few of them per token. Qwen3.6-35B activates roughly 3B of its 35B parameters per token. DeepSeek-V4-Flash picks 6 of 256 experts per layer, so 13B of its 284B run at a time. so compute was never the bottleneck. the weights a single step touches fit comfortably on a consumer GPU. every expert the router might pick still has to exist somewhere. they sit in system RAM, and the GPU keeps a cache of the ones the model has been using recently. so everything comes down to what happens when the router picks an expert that is not on the GPU. there are two ways to serve that miss: 1. copy it over PCIe and run it on the GPU 2. run it on the CPU, where it already lives both read from the same system memory, so they compete for one pool of bandwidth instead of adding to each other. existing engines pick one option and freeze it when the model loads. but routing changes on every token, so a fixed choice misses most of what the model asks for. FreeToken measures both bandwidths on your machine and splits each step's misses between the two paths in proportion. the GPU and CPU results then merge exactly, with no approximation. two machines with the same GPU can end up wanting opposite strategies, which I did not expect. a 5090 in a gaming desktop should push nearly everything over PCIe, while an 8GB laptop is better off computing most misses on the CPU. none of that is readable off a spec sheet, so the engine profiles it once per machine. the second half of the design is about agents. coding agents constantly rewrite their own history, and every edit normally forces thousands of tokens back through prefill. FreeToken saves its checkpoints at the exact boundaries agent frameworks cut on, so it only reprocesses the new part. its slowest first token stays under 44 seconds, while llama.cpp peaks at 232 and KTransformers at 946. it serves the OpenAI and Anthropic APIs under Apache 2.0, so Claude Code and Codex can point at it directly. releasing weights publicly decides who can download a model, not who can afford to run one. frontier open models keep shipping, and running them still assumes a rented cluster. meanwhile there are over a hundred million consumer machines with discrete GPUs sitting mostly idle. closing that gap was never a hardware problem, and work like this is what turns open weights into something you can actually use. paper: repo: almost every idea in this post, from why memory bandwidth decides the outcome to why moving weights costs more than computing on them, comes straight out of how a GPU is built. I wrote a detailed primer on that. the article is quoted below.

UC Berkeley just open-sourced FreeToken. (2–4x faster local LLM inference than Ollama) the results are wild: - Qwen3.6-35B on an 8GB GPU at 39.3 tokens/s - DeepSeek-V4-Flash 284B on a 32GB GPU at 22 tokens/s - GLM-5.2 753B on a 96GB GPU at 14.9 tokens/s a 35B model at 16-bit precision needs about 70GB just for its weights. even at 4 bits it is close to 18GB, and FreeToken serves it on an 8GB GPU. let me explain how: all three models mentioned above are Mixture-of-Experts, and that is what FreeToken takes advantage of. each layer holds hundreds of separate experts plus a small router that picks a few of them per token. Qwen3.6-35B activates roughly 3B of its 35B parameters per token. DeepSeek-V4-Flash picks 6 of 256 experts per layer, so 13B of its 284B run at a time. so compute was never the bottleneck. the weights a single step touches fit comfortably on a consumer GPU. every expert the router might pick still has to exist somewhere. they sit in system RAM, and the GPU keeps a cache of the ones the model has been using recently. so everything comes down to what happens when the router picks an expert that is not on the GPU. there are two ways to serve that miss: 1. copy it over PCIe and run it on the GPU 2. run it on the CPU, where it already lives both read from the same system memory, so they compete for one pool of bandwidth instead of adding to each other. existing engines pick one option and freeze it when the model loads. but routing changes on every token, so a fixed choice misses most of what the model asks for. FreeToken measures both bandwidths on your machine and splits each step's misses between the two paths in proportion. the GPU and CPU results then merge exactly, with no approximation. two machines with the same GPU can end up wanting opposite strategies, which I did not expect. a 5090 in a gaming desktop should push nearly everything over PCIe, while an 8GB laptop is better off computing most misses on the CPU. none of that is readable off a spec sheet, so the engine profiles it once per machine. the second half of the design is about agents. coding agents constantly rewrite their own history, and every edit normally forces thousands of tokens back through prefill. FreeToken saves its checkpoints at the exact boundaries agent frameworks cut on, so it only reprocesses the new part. its slowest first token stays under 44 seconds, while llama.cpp peaks at 232 and KTransformers at 946. it serves the OpenAI and Anthropic APIs under Apache 2.0, so Claude Code and Codex can point at it directly. releasing weights publicly decides who can download a model, not who can afford to run one. frontier open models keep shipping, and running them still assumes a rented cluster. meanwhile there are over a hundred million consumer machines with discrete GPUs sitting mostly idle. closing that gap was never a hardware problem, and work like this is what turns open weights into something you can actually use. paper: repo: almost every idea in this post, from why memory bandwidth decides the outcome to why moving weights costs more than computing on them, comes straight out of how a GPU is built. I wrote a detailed primer on that. the article is quoted below.

Akshay 🚀

340,691 просмотров • 21 дней назад