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Thinking generates a ton of tokens. These baked in chips tend to not have a lot of memory for context. I am not sure taalas supports Thinking at all.

The more problem like these they solve the more they will look like GPU.

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couldn't one just add some hundreds of GB of HBM?
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Yeah, but then there's the size of KV cache needing to be read through that HBM interface for each token, putting a hard limit on the tok/s based on the memory bandwidth.

On some models a large context can be a notable proportion of the size of the weights themselves.

For example, qwen 3.8 27b uses ~64kb/token for the kv cache - so for a 256k token context that's ~16gb of the kv cache for a ~54gb model (assuming 2 bytes-per-param/f16 for both).

So if the current non-baked-in chip is already memory bandwidth bound, as is often the case for current hardware and models, and the "only KV cache in HBM" chip has the same total memory bandwidth, it can only ever be (54/16)=~3.4x faster for the baked in-silicon model.

EDIT: I guess actually (54+16)/16=~4.3x faster, as the current implementation would need to read that KV cache too :)

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