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404 Brain not Found [GPU saturation] (Re: We are still waiting for results! [Confused Dumbwit] (Re: We are waiting Dumbwit: 1 Month, 3 Months, .. [node.js dawn])

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Hi,

Insist on what? Your stupidity? I only
see 404 Brain not Found in your case.
Who cares about 10GB/s, the facts are here:

1 Shader      4096 Shaders
542.0 ms      1141.0 ms

Means with 4096 shaders and the problem at
hand, we still didn't reach the GPU
Knee in the case of a Ryzen AI 7 350

w/ Radeon 860M. If you take another
hardware, you might see another GPU
saturation in the function

f(N) = time used for N shaders.

Bye

P.S.: So whats YOUR hardware and GPU
saturation? Its all open source:

Here is the software:

11.4 Giga Lips with a Budget Laptop
https://github.com/Jean-Luc-Picard-2021/gigabudget

Here are the screenshots (of the timings):

11.4 Giga Lips with a Budget Laptop
https://medium.com/2989/899b0d5c027b

> i must insist, memory arrays on AI gpu cards 
 > are not for graphics, idiot.
> 
> the proof? amazing a prolog guy dont even know what is going on in
> background, here the speed for embedded gpu/cpu are for instructions
> timing, not AI, hence say 10GB/s, which is nothing for running llm AI. 

Mild Shock schrieb:
> Hey Dumbwit,
> 
> We are still waiting for result. You only
> post gibberish:
> 
> /* Gibberish I */
>  > idiot, embedding the graphic card gpu into
>  > the cpu, you already have there
>  > the bottleneck, low speeds in ai, disregard the ram size allocated to 
> the graphic card.
> 
> Could you show us the bottleneck, is it
> in the same room as us. Whats your proof?
> During my testing the CPU just waits:
> 
>          await outputBuffer.mapAsync(GPUMapMode.READ);
> https://github.com/Jean-Luc-Picard-2021/gigabudget/blob/main/course/example63/package.html#L181C1-L181C54 
> 
> 
> Whats your point ultra moron?
> 
> /* Gibberish II */
>  > this imbecile doesnt know what ai and llm is, nor using it in coding, 
> programming etc, an idiot. He is doing graphics, what a fool. AI graphic 
> cards are not for graphics, cretin. What a fool.
> 
> Could you show us where I use graphics?
> The screenshots? The screenshots are only
> timings shown. Like here:
> 
>          document.getElementById("result").innerText = 
> i32s[0].toString() + " /* "+Math.round(performance.now() - start)+" ms */";
> https://github.com/Jean-Luc-Picard-2021/gigabudget/blob/main/course/example63/package.html#L184C1-L185C67 
> 
> 
> Whats your point ultra moron?
> 
> It seems you are highly confused Dumbwit!
> Go see a doctor as fast as you can.
> 
> Bye
> 
> Mild Shock schrieb:
>> Hey Dumbwit,
>>
>> We are waiting : 1 Month, 3 Months,
>> 12 Months ... Mostlikely the idiot even
>> doesn't own a RTX 5070. And if he owns
>>
>> a RTX 5070 he might struggle with setting
>> up HTTPS, so that the browser gives you
>> a WebGPU adapter.
>>
>> Well the good news is, you don't need
>> a browser. You could also run it with
>> node.js. Just use node.js dawn.
>>
>> See also
>>
>> Llamas on the Web: Memory-Efficient,
>> Performance-Portable, and Multi-Precision
>> LLM Inference with WebGPU
>> Reese Levine et al. -- 20 May 2026
>> Figure 2: Breakdown of the LlamaWeb llama.cpp WebGPU
>> backend and its different paths for executing on GPUs.
>> https://arxiv.org/abs/2605.20706
>>
>> But I didn't prepare some node.js code
>> on my GitHub. I also dont use some WASM (*)
>> helpers, its just pure HTML that taps
>>
>> into WebGPU via JavaScript inside a HTML page.
>>
>> Bye
>>
>> (*) Compute Toys seems to use WASM
>> to support Slang besides WGSL.
>>
>> Mild Shock schrieb:
>>> Hey Dumbwit,
>>>
>>> just run it on your RTX 5070 trash. I am
>>> software developer, not a hardware
>>> develper. I don't care what hardware
>>>
>>> people use. Here is the software:
>>>
>>> 11.4 Giga Lips with a Budget Laptop
>>> https://github.com/Jean-Luc-Picard-2021/gigabudget
>>>
>>> Here are the screenshots:
>>>
>>> 11.4 Giga Lips with a Budget Laptop
>>> https://medium.com/2989/899b0d5c027b
>>>
>>> You see in the screenshots with a
>>> Ryzen AI 7 350 w/ Radeon 860M that the
>>> results are:
>>>
>>> 1 Shader      4096 Shaders
>>> 542.0 ms      1141.0 ms
>>>
>>> What does your RTX 5070 trash deliver?
>>> Just redo the experiment on your hardware.
>>> If the figures are better, well good for
>>>
>>> you. If the figures are worse, well I wouldn't
>>> care less. You are just wasting everbodies
>>> bandwidth with your idiotic posts, and being
>>>
>>> lazy, instead of replicating the experiment
>>> on your RTX 5070 trash.
>>>
>>> Bye
>>>
>>> Olin Bagramov schrieb:
>>>  > Mild Shock wrote:
>>>  >
>>>  >> a wider von Neuann Neck. You can try yourself, in case you find 
>>> an AI
>>>  >> Laptop with similary specs as the Radeon 860M.
>>>  >
>>>  > idiot, that's nothing in llm, you are wasting your time
>>>  >
>>>  > compare with this, if you want proper llm
>>>  >
>>>  > HBM3e: The current flagship memory for the H200 and B200 series, 
>>> offering
>>>  > the highest bandwidth (~8 TB/s) required for trillion-parameter 
>>> models.
>>>  >
>>>
>>>  >> Hi,
>>>  >>
>>>  >> You are a fucking moron, arent you?
>>>  >>
>>>  >> Most of the stuff in my pi-WAM happens inside the L1 and L2 
>>> caches of
>>>  >> the GPU.
>>>  >> Which is faster than normal RAM and has
>>>  >>
>>>  >> a wider von Neuann Neck. You can try yourself, in case you find 
>>> an AI
>>>  >> Laptop with similary specs as the Radeon 860M.
>>>  >>
>>>  >> The example is open source:
>>>  >>
>>>  >> 11.4 Giga Lips with a Budget Laptop
>>>  >> https://github.com/Jean-Luc-Picard-2021/gigabudget
>>>  >
>>>  > L1 and L2 are small in size and slow, then the 6 stages pipelining 
>>> destroys the neural AI/llm algorithm; compare that with 4,000 GB/s 
>>> arrays gddr7 for a graphic card, then talk. You are not good at 
>>> numbers, are you
>>
> 

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