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Implementing Gas for a Compute Shader [Avoid TDR] (Re: AI dooms day escape: Güttinger Wald)

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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

#896208 — Implementing Gas for a Compute Shader [Avoid TDR] (Re: AI dooms day escape: Güttinger Wald)

Hi,

I guess, you can read off how to do it here,
i.e. avoid TDR (DXGI_ERROR_DEVICE_HUNG 0x887A0006).
The below compute toys example has a similar

approach, just like my π-WAM and Hack GPU backend,
that is based on a Instruction Set Architecture (ISA):

Conway's Game of Life
https://compute.toys/view/2777

He adds a spin to his ISA, that he uses
some gas as computational resource limiter:

         for (var gas = 0; gas < 60; gas++) {
             if (vm.done == 1u) { break; }
             if (vm.pc >= 33u) { vm.done = 1u; break; }

             let instr = rom[vm.pc]; vm.pc++;
             switch (instr) {
                 case OP_HALT: { vm.done = 1u; }
                 case OP_LIT: { vm.stack[vm.sp] = rom[vm.pc]; vm.pc++; 
vm.sp++; }
                 case OP_ADD: { vm.sp-=2; vm.stack[vm.sp] = 
vm.stack[vm.sp] + vm.stack[vm.sp+1]; vm.sp++; }
                 case OP_SUB: { vm.sp-=2; vm.stack[vm.sp] = 
vm.stack[vm.sp] - vm.stack[vm.sp+1]; vm.sp++; }

Etc..

Now need to do the same for my Hack GPU backend
somehow. Will see how this works out.

But the https://compute.toys/ will do the repeated
invokation of the shader. And the screen syncing
and some pacing to get 60 FPS. So I have to look

at the GitHub source of compute toys as well, in
case it is open source, to at least find a code
template for the repeated dispatch.

Bye

Mild Shock schrieb:
> Hi,
> 
> You just escaped AI dooms day. Humanity has
> reset all internet and computers as a last resort
> to prevent AGI developing, by an electromagnetic
> 
> pulse. You are stuck in Güttinger Wald and hunted
> down a deer by your bare hands, the deer still
> confused and tame because tourists were feeding it.
> 
> Now you have no knife, what do you do:
> 
> Chimpanzees Have Entered The Stone Age
> https://www.youtube.com/watch?v=wPXX2I_uYjc
> 
> So we are just apes with internet.
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Ok I was looking at this learning challenge,
>> producing vector (y1,y2,y3,y4) from a vector
>> (x1,x2,x3,x4), System R can do it via least square?
>>
>> | 0 0 0 1 |   | x1 |     | x4 |
>> | 0 0 1 0 |   | x2 |  =  | x3 |
>> | 0 1 0 0 |   | x3 |     | x2 |
>> | 1 0 0 0 |   | x4 |     | x1 |
>>
>> How it started:
>>
>> "multiplicative RNNs arises naturally from a
>> proof-theoretic interpretation of next-token
>> prediction as nested intuitionistic implication"
>> Paul Tarau - 2026
>> https://arxiv.org/abs/2601.19915
>>
>> How its going:
>>
>> "Dave uses a PDP-11 to train a real Neural
>> Network complete with Transformers and
>> Attention so you can see them at their most basic."
>> Mr. Taskmanager - 2026
>> https://www.youtube.com/watch?v=OUE3FSIk46g
>>
>> We see Doctor Frankstein in action from
>> the Bronze Age of Computing, producing
>> a Humunkulus, the progenitor of todays
>>
>> Bulgakov Shuriks in the Hyperscale Age!
>>
>> Bye
>>
>> P.S.: My impression neither cut to the core, that
>> this incredible transformer most likely
>> produced this deterministic attention:
>>
>> | -1 | * | k | + | 5 | = | k' |
>>
>> Or differently expressed y_k = x_{5-k}.
>>
>> How did the transformer do it? It produced
>> a neural network with 1216 parameters, but
>> didn't use embeddings or polar encoding
>>
>> of positions. But if we strip the noise
>> and denoise from the position encoding,
>> the denoise is done via softmax. We somehow
>>
>> must get the above, right? I still need to
>> verify my claim! BTW: The PDP-11 assembly
>> from 1979 uses wider example not with n=4
>>
>> but with n=8.
> 

[toc] | [next] | [standalone]

#896209 — FYI: Unified Memory Architecture (UMA) (Re: Implementing Gas for a Compute Shader [Avoid TDR])

Hi,

These novel GPUs , that are part of AI Laptops,
feature Unified Memory Architecture (UMA).
In the case of my Ryzen the main memory is 32 GB,

and the GPU can access 16 GB. It is a design where
CPUs and GPUs share a single coherent memory space,
eliminating the need for separate host and

accelerator memory. Nevertheless the WebGPU API,
works with some copy and synchronization semantics
and buffer abstractions, which is not a big loss.

But all buffers reside in the same AI Lapop
RAM. And the MIMD architecture allows instructions
where seperate threads access the same

memory location, either for read or for write.
There is also a data type atomic(T), which
features operation such as AtomicAdd() etc.. etc..

BTW, I made a GitHub project of my exploration:

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

BTW, pi-WAM is nevertheless optimized to have
no memory contention. On the other hand pi-WAM
is happy to access large memory areas.

Bye

BTW: The grandmother of these novel GPUs
is NVIDIAs Volta which already appeared in
2017, meanwhile we have 2026.

See also:

Starting with the NVIDIA Volta architecture,
Independent Thread Scheduling allows full
concurrency between threads, regardless of warp.
https://forums.developer.nvidia.com/t/back-to-simd/311983

The AMD Radeon 860M is an integrated graphics
processor that does not have its own dedicated
VRAM. Instead, it dynamically shares up to 50%
of your total system RAM with the CPU in a
standard Windows configuration.
https://www.amd.com/en/blogs/2025/faqs-amd-variable-graphics-memory-vram-ai-model-sizes-quantization-mcp-more.html

Ryann Likunov schrieb:
 > Mild Shock wrote:
 >
 >> Hi,
 >>
 >> I guess, you can read off how to do it here,
 >> i.e. avoid TDR (DXGI_ERROR_DEVICE_HUNG 0x887A0006). The below compute
 >> toys example has a similar
 >>
 >> approach, just like my π-WAM and Hack GPU backend,
 >> that is based on a Instruction Set Architecture (ISA):
 >
 > how come they are not able to map the local RAM
 > as gpu arrays for using them as local AI resource
 >

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#896218 — he Wider von Neumann Neck (Re: FYI: Unified Memory Architecture (UMA))

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 Neumann 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

I already wrote pi-WAM is designed to
not use memory contention. In particalur
it also uses local variables like pc and

accu of the idependent thread states,
which seems to be also pretty speedy.

Bye

Tanner Babadzhan schrieb:
 > Mild Shock wrote:
 >
 >> These novel GPUs , that are part of AI Laptops, feature Unified Memory
 >> Architecture (UMA).
 >> In the case of my Ryzen the main memory is 32 GB,
 >
 > you are in error talking bullshit, the bottleneck there is the max 4 
GB/s,
 > 4 times by paralleling, however the proper gddr5/6 gpu arrays goes up to
 > 4,000 GB/s by parallel design
 >
 > GDDR7 (2025–2026 standard)
 >      Max Bandwidth: 1,792 GB/s (RTX 5090, 32GB)
 >

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#896219 — Mac Neo: The dwarf with giant GPU muscles [macOS IOGPUFamily] (Re: The new MIMD Warp is the cherry on top)

Hi,

Interestingly you can half the AI Laptop
Budget now. The new Mac Neo is only $500 ,
half of my discount AI laptop. Here is

some measurement if this AI Laptop zoo,
including the Mac Neo dwarf (in milliseconds):

AI Laptop    example63
Ryzen    542.0
Neo    950.0
Yoga    1475.0
Think    2798.0

AI Laptop    example64
Ryzen    1141.0
Neo    2022.0
Yoga    2118.0
Think    7439.0

Since my test doesn't use much memory, it also
fits into into the Mac Neo dwarf 8 GB, and the
Mac Neo dwarf is extrem since the GPU can access

all of 8 GB, not only 50% and the memory is
integrated directly into the processor die,
it is still DRAM not yet HBM. The ARM instruction

set has been streamlined to better support
common GPU interfaces. The CPU acts as a pure
scheduler via native kernel extensions,

appending command buffers straight into memory
queues that the GPU's command processor
reads without a middleman.

Bye

Mild Shock schrieb:
> Hi,
> 
> But I do grouping of Hack VMs for my pi-WAM in
> 32 wide work groups. And the there are 4096
> / 32 = 128 such work groups.
> 
> Traditionally work groups were executed lockstep:
> 
> In GPU architecture, a warp (or wavefront in AMD
> terminology) is the fundamental unit of execution,
> typically comprising 32 scalar threads. Warps
> execute in a SIMT (Single Instruction, Multiple
> Thread) fashion, where all 32 threads execute
> the same instruction in synchronized lockstep
> over different data
> 
> New GPUs offer independent thread scheduling:
> 
> Modern GPU architectures (such as NVIDIA's Volta
> and later) feature Independent Thread Scheduling,
> maintaining independent execution states even for
> threads within the same warp. This allows the GPU
> to yield and resume threads dynamically, essentially
> acting as a hardware-level MIMD processor running
> on SIMD execution lanes.
> 
> I guess MIMD drastically increases the arithmetic
> bandwidth for control flow based WGSL code, while
> some group arrangement can also increase
> 
> the memory bandwidth. By kind of concurrently
> flushing L1/L2 caches and reloading L1/L2 caches,
> creating some simple sequential systolic computing.
> 
> At least I have used a memory layout where threads
> from a workgroup are adjacent. Standard processors
> continuously fetch data from memory, they suffer from the
> 
> "Von Neumann bottleneck". Systolic computing bypasses
>   this by feeding data into an array of Processing
> Elements (PEs) in a wave-like flow.
> 
> Bye
> 
> Mild Shock schrieb:
>>
>> 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 Neumann 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
>>
>> I already wrote pi-WAM is designed to
>> not use memory contention. In particalur
>> it also uses local variables like pc and
>>
>> accu of the idependent thread states,
>> which seems to be also pretty speedy.
>>
>> Bye
>>
>> Tanner Babadzhan schrieb:
>>  > Mild Shock wrote:
>>  >
>>  >> These novel GPUs , that are part of AI Laptops, feature Unified 
>> Memory
>>  >> Architecture (UMA).
>>  >> In the case of my Ryzen the main memory is 32 GB,
>>  >
>>  > you are in error talking bullshit, the bottleneck there is the max 
>> 4 GB/s,
>>  > 4 times by paralleling, however the proper gddr5/6 gpu arrays goes 
>> up to
>>  > 4,000 GB/s by parallel design
>>  >
>>  > GDDR7 (2025–2026 standard)
>>  >      Max Bandwidth: 1,792 GB/s (RTX 5090, 32GB)
>>  >
>>
> 

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#896221 — Dumbwit, just run it on your RTX 5070 trash (Re: he Wider von Neumann Neck)

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

[toc] | [prev] | [next] | [standalone]

#896222 — We are waiting Dumbwit: 1 Month, 3 Months, .. [node.js dawn] (Re: Dumbwit, just run it on your RTX 5070 trash)

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

[toc] | [prev] | [next] | [standalone]

#896226 — We are still waiting for results! [Confused Dumbwit] (Re: We are waiting Dumbwit: 1 Month, 3 Months, .. [node.js dawn])

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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#896227 — 404 Brain not Found [GPU saturation] (Re: We are still waiting for results! [Confused Dumbwit])

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
>>
> 

[toc] | [prev] | [next] | [standalone]

#896228 — I nowhere talked about 10GB/s (Re: 404 Brain not Found [GPU saturation])

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
>>>
>>
> 

[toc] | [prev] | [next] | [standalone]

#896229 — I nowhere said something about AI (Re: I nowhere talked about 10GB/s)

Hi Dumbwit,

Nobody is interested in discussing AI,
I am discussing prolog on a AI laptop.

Who cares that they use the AI marketing,
when they have splendid GPUs?

Only morons like you!

Bye

Wilfred Durmanov schrieb:
 > yet another obsolete it-supporter, without
 > proper education, doesnt know what AI stands
 > there for. Maybe you should look round to
 > have your face properly rearranged

Mild Shock schrieb:
> 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
>>>>
>>>
>>
> 

[toc] | [prev] | [next] | [standalone]

#896230 — Re: I nowhere said something about AI (Re: I nowhere talked about 10GB/s)

Hi,

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

Budget Laptop means ~1000 USD.
Thats not big money. The Mac Neo is
even better only ~500 USD.

Its still cheaper than a RTX 5070,
which is around ~4000 USD. So if you
have some benchmark data for RTX 5070,

the LIPS, you still pay 4x times more
for each LIPS.

Questions ultra moron?

Bye

Junior Romagna schrieb:
 > Mild Shock wrote:
 >
 >> Hi Dumbwit,
 >>
 >> Nobody is interested in discussing AI,
 >> I am discussing prolog on a AI laptop.
 >>
 >> Who cares that they use the AI marketing,
 >> when they have splendid GPUs?
 >
 > what an idiot, so you put big money in AI laptop do do graphics you 
can do
 > on old 8088 PC etc. Thanks, you just proved you are so fucking stoopid
 >
 > i can see now why morons like you are using crappy scripting languages
 > like prolog. No brain required, the prolog is doing the brain for you
 >

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