I only see an edit to the readme (13h ago) and removal of the weights, so the repo is now empty.
I am willing to give them the benefit of the doubt, but we've seen this before: a model gets released that is supposedly state-of-the-art, yet seems to be a an other repackaged model without any training. Reflection 70B was the most similar example, all they now need is an api that rewrites "Claude" to "Rio".
There is currently very little evidence that morphological tokenizers help model performance [1]. For languages like German (where words get glued together) there is a bit more evidence (eg a paper I worked on [2]), but overall I start to suspect the bitter lesson is also true for tokenization.
Distributing inference per layer, instead of splitting each layer across gpus, is indeed another approach, called pipeline parallelism. However, per batch there is less compute (only 1 gpu at a time), so inference is slower. In addition, the orchestration of starting the next batch on gpu #0 while gpu #1 starts is quite tricky. For this reason, tensor parallelism as I described is way more common in LLM inference.
This is an interesting blogpost. While the general conclusion ("We need batching") is true, inference of mixture of experts (MoE) models is actually a bit more nuanced.
The main reason we want big batches is because LLM inference is not limited by the compute, but my loading every single weight out of VRAM. Just compare the number of TFLOPS of an H100 with the memory bandwidth, there's basically room for 300 FLOP per byte loaded. So that's why we want big batches: we can perform a lot of operations per parameter/weight that we load from memory. This limit is often referred to as the "roofline model".
As models become bigger, this does not scale anymore because the model weights will not fit into GPU memory anymore and you need to distribute them across GPUs or across nodes. Even with NVLink and Infiniband, these communications are slower than loading from VRAM. NVlink is still fine for tensor parallelism, but across nodes this is quite slow.
So what MoE allows is expert parallelism, where different nodes keep different experts in memory and don't need to communicate as much between nodes. This only works if there are enough nodes to keep all experts in VRAM and have enough overhead for other stuff (KV cache, other weights, etc). So naturally the possible batch size becomes quite large. And of course you want to maximize this to make sure all GPUs are actually working.
The mains frequency is literally how fast the generators in power plants are turning. If the load on the grid increases, those generators slow down slightly and more natural gas/coal/heat needs to be added to increase the frequency again. This whole process is quite complicated as not every plant can react in the same time. Some plants are always at 100% capacity, while others are dedicated to governing the frequency.
So there are small fluctuations, often between 0.2 Hz around the base frequency, but the average is very close to the theoretical 50/60 Hz. And for an alarm clock that is good enough.
I have both DO and Hetzner VMs and I find them comparable, with Hetzner being a bit cheaper. If I look at the logs and fail2ban, it looks like DO does a bit more abusive traffic filtering, but that is basically the only difference.
However, the DO docs are on a different level and high quality. But those are also accessible if you are not a customer.
I am willing to give them the benefit of the doubt, but we've seen this before: a model gets released that is supposedly state-of-the-art, yet seems to be a an other repackaged model without any training. Reflection 70B was the most similar example, all they now need is an api that rewrites "Claude" to "Rio".