This is *FINALLY* answering the question everyone has been thinking after reading the new model releases this year: why these hybrid attention layers? And apparently, more expressiveness is the answer?
I am more than excited to hear the post-training challenges for MoE models: if we can inference with sharding, couldn't we do the same with post-training as well?
Thank you for reading the article rather than text-to-speech. This has been more than refreshing.
The tooling gap is the most underappreciated bottleneck here. It's ironic — hybrid architectures promise compute efficiency gains, but right now the OSS inference stack eats those gains alive with workarounds like --enforce-eager and FP32 cache. The 2x pretraining efficiency is compelling, but the real unlock will be when VLLM and other frameworks have first-class GDN support with proper kernel optimization. Until then, the practical cost story for production deployments is murky at best. Curious whether the post-training challenges (weaker reasoning performance vs dense) are fundamentally architectural or just a data/recipe mismatch that better teacher selection could fix.
"Why does expressive power help with efficiency? This is where things are more nuanced. We argue that more expressive models will have better scaling laws, following the quantization model of neural scaling."
A fairly interesting paper you've linked. It's hard to tease out these at once endlessly expressive decoder models and at once not expressive in the ways we'd like
This is *FINALLY* answering the question everyone has been thinking after reading the new model releases this year: why these hybrid attention layers? And apparently, more expressiveness is the answer?
I am more than excited to hear the post-training challenges for MoE models: if we can inference with sharding, couldn't we do the same with post-training as well?
Thank you for reading the article rather than text-to-speech. This has been more than refreshing.
my voice overs aren't perfect but the humanity is nice, and they help me proof read 🫡
The tooling gap is the most underappreciated bottleneck here. It's ironic — hybrid architectures promise compute efficiency gains, but right now the OSS inference stack eats those gains alive with workarounds like --enforce-eager and FP32 cache. The 2x pretraining efficiency is compelling, but the real unlock will be when VLLM and other frameworks have first-class GDN support with proper kernel optimization. Until then, the practical cost story for production deployments is murky at best. Curious whether the post-training challenges (weaker reasoning performance vs dense) are fundamentally architectural or just a data/recipe mismatch that better teacher selection could fix.
"Why does expressive power help with efficiency? This is where things are more nuanced. We argue that more expressive models will have better scaling laws, following the quantization model of neural scaling."
A fairly interesting paper you've linked. It's hard to tease out these at once endlessly expressive decoder models and at once not expressive in the ways we'd like