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Andrej Karpathy original gangster of Budget Laptop (Was: miniTriton CUDA is an alternative to torch variants)

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Hi,

Andrej Karpathy was bascially the original gangster
of doing not only AI inferencing but also AI
learning on a Budget Laptop. The nanoGPT project

states the following:

"I only have a macbook (or other cheap
computer). No worries, we can still train a
GPT but we want to dial things down a notch.
I recommend getting the bleeding edge PyTorch
nightly (select it here when installing) as
it is currently quite likely to make your
code more efficient."
https://github.com/karpathy/nanogpt

But meanwhile he has moved to a higher price
segment. Not sure whether he will climbe
down to a lower price segment again:

For example, you can train your own GPT-2
capability LLM (which cost ~$43,000 to train in
2019) for only $48 (~2 hours of 8XH100 GPU node)
and then talk to it over a simple CLI. On a spot
instance, the total cost can be closer to ~$15.
https://github.com/karpathy/nanochat

Bt he taps into the model to rent GPU which
is available with prices in the range of 1-2 $
per hour. Even in Switzerland one can do that,

for example using the provider Exoscale. Since
he rents a cluster of 8 cards of type H100, this
explains his training price still in the 2 digit range.

Bye

P.S.: I could also do my experiment here with
rented GPU cards, and then draw a comparison
from budget laptop to the rented GPU time market:

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

But testing rented GPU is not high priority.

Mild Shock schrieb:
> Hi,
> 
> Some counter PyTorch Python trends are
> for example OpenAIs Triton. And the variant
> miniTriton CUDA vibe produced by Kimi K3 (sic!):
> 
> "We further tested whether Kimi K3 could build
> a GPU programming system from scratch. Kimi K3
> developed MiniTriton, a compact Triton-like
> compiler with its own tile-level IR layer over
> MLIR, optimization passes, and a PTX code-
> generation pipeline.
> 
> Across supported roofline benchmarks, MiniTriton
> delivers performance on par with or better than
> Triton and torch.compile — beating Triton on
> certain workloads. Beyond microbenchmarks,
> MiniTriton sustains end-to-end nanoGPT training
> with stable convergence, the loss curve
> 
> closely tracking the reference with only minor
> divergence — validating the full pipeline on a
> realistic workload. These results demonstrate
> that Kimi K3 can build a coherent end-to-end
> compiler — from DSL frontend and IR passes to
> PTX codegen and runtime — rather than isolated
> 
> kernels; its from-scratch Tensor Core path
> already rivals Triton’s extensively optimized stack."
> 
> GPU Compiler Development
> https://www.kimi.com/blog/kimi-k3
> 
> Although many GPU corporate stuff is anonymized,
> and some AI papers have lists of 30 authors. Here
> nanoGPT is mentioned which is tied to the name
> 
> Andrej Karpathy. See also here:
> 
> Update Nov 2025 nanoGPT has a new and
> improved cousin called nanochat.
> https://github.com/karpathy/nanogpt
> 
> But as can be seen, he moved on to another project.
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Whats this "forget" trope of glue sniffing
>> Rossy Boy with his herpes blisters?
>>
>>  > Bulgarians, that's some real Boris and Natasha crap,
>>  > forget Hungarians and Bulgarians.
>>
>> Why should I forget Bulgarians,
>> they are never on my mind. Do you
>> see me doing ggml stuff?
>>
>> I only hypothesized that it is
>> over for Python as the machine
>> learning language or AI inferencing
>>
>> locally on AI laptops language, and
>> made the ggml case, so I already forgot
>> about them. Which might give you a glimps,
>>
>> why WebGPU was used for this here:
>>
>> 11.4 Giga Lips with a Budget Laptop
>> https://github.com/Jean-Luc-Picard-2021/gigabudget
>>
>> Is an interesting choice. Even
>> github has some Languages statistics,
>> giving an account what I used:
>>
>> HTML 67.5% JavaScript 23.1% CSS 9.4%
>>
>> Have Fun!
>>
>> Bye
>>
>> P.S.: The example below is not p-adics,
>> you complete imbecil moron. Its just:
>>
>> 7-11 cubic Solution by Pritchard & Gries
>> https://www.cs.cornell.edu/gries/TechReports/83-574.pdf
>>
>> Ross Finlayson schrieb:
>>> On 07/26/2026 10:52 AM, Mild Shock wrote:
>>>> Hi,
>>>>
>>>> You see it all boils down to find your inner peace
>>>> by an immaculate inception of some queue datatype.
>>>>
>>>> KOAN/Fortran-S was an early 1990s research programming
>>>> system for distributed-memory multiprocessors . Developed
>>>> at ENS Lyon in the early 1990s . Often listed alongside
>>>> other historical parallel programming efforts.
>>>>
>>>> The Message Passing: The research explicitly
>>>> compared the SVM approach against message passing
>>>> on the same hardware . The finding was that SVM
>>>> could achieve good performance without the low-level
>>>>
>>>> complexity of managing explicit messages, though
>>>> the best results often came from a hybrid approach (sic!)
>>>> Here is an interesting baseline, from Java,
>>>> a class ElevenSingle that only does:
>>>>
>>>>      public static void run() {
>>>>          for (int A = 1; A < 192; A++) {
>>>>              int Y = (771-A)/3;
>>>>              for (int B = A; B < Y; B++) {
>>>>                  int Z = (771-A-B)/2;
>>>>                  for (int C = B; C < Z; C++) {
>>>>                      int D = 711-A-B-C;
>>>>                      if (A*B*C == 711000000/D &&
>>>>                            711000000 % D == 0)
>>>>      System.out.println("A="+A+", B="+B+", C="+C+", D="+D);
>>>>                  }
>>>>              }
>>>>          }
>>>>      }
>>>>
>>>> And then compare it to ElevenMulti, doing some
>>>> Work Balancing Scheduler Tetris Game with 8 cores:
>>>>
>>>> ElevenSingle
>>>> A=120, B=125, C=150, D=316
>>>> 6.628 ms
>>>>
>>>> ElevenMulti
>>>> A=120, B=125, C=150, D=316
>>>> 1.941 ms
>>>>
>>>> Not great, not terrible!
>>>>
>>>> Bye
>>>
>>> Oh, that's just "tricks of p-adic arithmetic".
>>>
>>> Like other sock-puppet howler trolls, when confronted
>>> with its base incredulity, it will descend to its
>>> lower levers of the pathos variety.
>>>
>>> You might be happier learning about Julia trees and
>>> raster ops, instead of shilling yet another Ramanujan
>>> series without saying how it's made.
>>>
>>> Bulgarians, that's some real Boris and Natasha crap,
>>> forget Hungarians and Bulgarians.
>>>
>>>
>>
> 

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