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I nowhere talked about 10GB/s (Re: 404 Brain not Found [GPU saturation])

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Hi Dumbwit,

You are a real moron right.

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

What does LIPS mean. Look it up!
Nothing to do with bytes (B).

Bye

Hint: The speed of a Prolog implementation is
sometimes quoted in LIPS - logical inferences
per second.

See also:

Prolog basics CSc 372, Fall 2006 Prolog, Slide 154
W. H. Mitchell (whm@msweng.com)
https://www2.cs.arizona.edu/classes/cs372/fall06/prolog.sli.pdf

Mild Shock schrieb:
> 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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Implementing Gas for a Compute Shader [Avoid TDR] (Re: AI dooms day escape: Güttinger Wald) Mild Shock <janburse@fastmail.fm> - 2026-07-09 10:49 +0200
  FYI: Unified Memory Architecture (UMA) (Re: Implementing Gas for a Compute Shader [Avoid TDR]) Mild Shock <janburse@fastmail.fm> - 2026-07-09 19:45 +0200
    he Wider von Neumann Neck (Re: FYI: Unified Memory Architecture (UMA)) Mild Shock <janburse@fastmail.fm> - 2026-07-10 07:49 +0200
      Mac Neo: The dwarf with giant GPU muscles [macOS IOGPUFamily] (Re: The new MIMD Warp is the cherry on top) Mild Shock <janburse@fastmail.fm> - 2026-07-10 08:29 +0200
      Dumbwit, just run it on your RTX 5070 trash (Re: he Wider von Neumann Neck) Mild Shock <janburse@fastmail.fm> - 2026-07-10 15:47 +0200
        We are waiting Dumbwit: 1 Month, 3 Months, .. [node.js dawn] (Re: Dumbwit, just run it on your RTX 5070 trash) Mild Shock <janburse@fastmail.fm> - 2026-07-10 16:29 +0200
          We are still waiting for results! [Confused Dumbwit] (Re: We are waiting Dumbwit: 1 Month, 3 Months, .. [node.js dawn]) Mild Shock <janburse@fastmail.fm> - 2026-07-10 22:47 +0200
            404 Brain not Found [GPU saturation] (Re: We are still waiting for results! [Confused Dumbwit]) Mild Shock <janburse@fastmail.fm> - 2026-07-10 23:42 +0200
              I nowhere talked about 10GB/s (Re: 404 Brain not Found [GPU saturation]) Mild Shock <janburse@fastmail.fm> - 2026-07-10 23:43 +0200
                I nowhere said something about AI (Re: I nowhere talked about 10GB/s) Mild Shock <janburse@fastmail.fm> - 2026-07-10 23:51 +0200
                Re: I nowhere said something about AI (Re: I nowhere talked about 10GB/s) Mild Shock <janburse@fastmail.fm> - 2026-07-11 00:09 +0200

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