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We are still waiting for results! [Confused Dumbwit] (Re: We are waiting Dumbwit: 1 Month, 3 Months, .. [node.js dawn])

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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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