Sebastian, this is a great reminder that LLM progress is not always visible from the chat interface. A model may look slightly better to users, but under the hood, changes in attention, MoE routing, KV cache efficiency, and long-context design can make a huge difference in cost, speed, and usability. The architecture layer is where a lot of the real competition is happening.
The convergence across all four architectures toward KV cache reduction tells you where the real constraint in the industry has moved. Training compute was the bottleneck for the 2020-2024 era. Inference efficiency at long context is the bottleneck for the agent era. Every architecture in this piece is solving the same problem from a different angle, how to keep attention costs sublinear as context windows expand into the hundreds of thousands of tokens that agentic workflows demand.
The interesting question is whether these are converging on a single optimal solution or whether different deployment scenarios will favour different approaches. KV sharing suits layers with redundant representations. Compressed attention suits contexts with exploitable structure. In production agent systems where context length and structure vary enormously across tasks, the winning architecture might be the one that dynamically selects between these techniques per layer per inference pass rather than committing to a single strategy at training time. thats a harder engineering problem than any individual approach here but its probably where the field converges, an adaptive attention budget that allocates efficiency method to context type in real time.
thanks
Sebastian, this is a great reminder that LLM progress is not always visible from the chat interface. A model may look slightly better to users, but under the hood, changes in attention, MoE routing, KV cache efficiency, and long-context design can make a huge difference in cost, speed, and usability. The architecture layer is where a lot of the real competition is happening.
The clearest explanation of CSA/HCA I have ever seen, very easy to follow!
Thanks, glad to hear the visualizations helped!
Crazy , will need to check out later.
The convergence across all four architectures toward KV cache reduction tells you where the real constraint in the industry has moved. Training compute was the bottleneck for the 2020-2024 era. Inference efficiency at long context is the bottleneck for the agent era. Every architecture in this piece is solving the same problem from a different angle, how to keep attention costs sublinear as context windows expand into the hundreds of thousands of tokens that agentic workflows demand.
The interesting question is whether these are converging on a single optimal solution or whether different deployment scenarios will favour different approaches. KV sharing suits layers with redundant representations. Compressed attention suits contexts with exploitable structure. In production agent systems where context length and structure vary enormously across tasks, the winning architecture might be the one that dynamically selects between these techniques per layer per inference pass rather than committing to a single strategy at training time. thats a harder engineering problem than any individual approach here but its probably where the field converges, an adaptive attention budget that allocates efficiency method to context type in real time.
記事をありがとう！