I wish they wouldn't call them "open source models". They aren't open source. They didn't publish the training data. They didn't publish the tools they used to train the model.
They published the weights. It's an "open weight model", a term that it seems nearly everyone has agreed is appropriate. Why is this company not using it?
The weights are the preferred form for modifying or integrating with other models. There is no obligation in open source to transitively open source all of the documentation / tools used to create the open source project.
>a term that it seems nearly everyone has agreed is appropriate
Models being considered open source even if the original training code / data is not released also is something almost everyone has agreed to be appropriate.
> The weights are the preferred form for modifying or integrating with other models
It's the 2nd time I hear this argument and I'm already fed up with it
Is it the preferred way only because training is expensive? It's like saying binaries are the preferred way of modifying program because you can't afford to have a fast enough machine.
Most people don't have resources to compile their own browser, but what makes some browsers open is access to the source.
Maybe it's the preferred way because the people sharing models are themselves working with weights and no data?
This part sounds like AI assisted setting this up and benchmarking it:
>The fix was trivially simple: zero-pad the head count 12→16, run the fast kernel, and extract the real 12 heads from the output.
I've recently used a frontier AI (ChatGPT 5.6 Sol on ultra) to set up a much smaller local model, and the performance optimizations it introduced left the model totally incoherent. (The model just repeats a single character, etc.)
When I see a line like the one I just quoted, it leaves me wondering if the setup is still coherent like a stock install of Kimi K3 on supported hardware.
Did they run any benchmarks on it to see if it is still correct?
Wafer is making themselves synonymous with slop in the inference space. Exaggerated unfair comparisons in all their results, twitter hype posts with alarm emojis etc.
> $2.50/GPU-hr for the MI355X, $6.00 for the B300, and $4.25 for the B200.
This is not an accurate price comparison for real terms.
If you do good work you should at least take the time to review the slop that details that work for slopiness. Otherwise it's hard to take it seriously. Especially the prefill section.
The B200 is being forcefully compared against something which is not gonna fit within it's memory in a single node & not much details about multi-node interconnectivity, disagg or not. As expected of a shoddy slop.
The only point it won is of cost per hour is one aggregation website for rentals, the premium a B300 commands against the $3/hr AMD chip which no provider has in abundance. Never bothered to do TCO of owning the hardware either.
" The only problem with AMD is software support — slower kernels and less day-0 support on inference frameworks make serving frontier models on AMD a real engineering effort."
They published the weights. It's an "open weight model", a term that it seems nearly everyone has agreed is appropriate. Why is this company not using it?
>a term that it seems nearly everyone has agreed is appropriate
Models being considered open source even if the original training code / data is not released also is something almost everyone has agreed to be appropriate.
It's the 2nd time I hear this argument and I'm already fed up with it
Is it the preferred way only because training is expensive? It's like saying binaries are the preferred way of modifying program because you can't afford to have a fast enough machine.
Most people don't have resources to compile their own browser, but what makes some browsers open is access to the source.
Maybe it's the preferred way because the people sharing models are themselves working with weights and no data?
Open source means open source code. Open weight means a binary file dump, not unlike an exe file. There is nothing open source about it.
Its like having a closed source text editor that censors certain words, and an open source text editor that censors certain words.
The latter can easily be recompiled, the former requires reverse engineering. Both may give you a license to use them freely.
>The fix was trivially simple: zero-pad the head count 12→16, run the fast kernel, and extract the real 12 heads from the output.
I've recently used a frontier AI (ChatGPT 5.6 Sol on ultra) to set up a much smaller local model, and the performance optimizations it introduced left the model totally incoherent. (The model just repeats a single character, etc.)
When I see a line like the one I just quoted, it leaves me wondering if the setup is still coherent like a stock install of Kimi K3 on supported hardware.
Did they run any benchmarks on it to see if it is still correct?
> $2.50/GPU-hr for the MI355X, $6.00 for the B300, and $4.25 for the B200.
This is not an accurate price comparison for real terms.
GPUs. 8× MI355X (TP8) B300 (TP8+DCP8)
Decode tok/s per stream 118 tok/s 172 tok/s
Peak aggregate. 952 tok/s 1,568 tok/s
Peak aggregate per GPU 119 tok/s 196 tok/s
On every row the B300 beat the MI355X
The B200 is being forcefully compared against something which is not gonna fit within it's memory in a single node & not much details about multi-node interconnectivity, disagg or not. As expected of a shoddy slop.
The only point it won is of cost per hour is one aggregation website for rentals, the premium a B300 commands against the $3/hr AMD chip which no provider has in abundance. Never bothered to do TCO of owning the hardware either.
" The only problem with AMD is software support — slower kernels and less day-0 support on inference frameworks make serving frontier models on AMD a real engineering effort."