Yes, batch=1 inference is mostly memory bandwidth bound, not GPU compute bound. But no provider does batch=1 inference. Everyone groups all the requests into a batch, and the GPU computes them together.
With a fused kernel, that means the GPU streams the tensors from VRAM, and does a bunch of compute on different conversations in the batch, at the same time.
If they increase the amount of compute required per token, that just reduces the maximum batch size a GPU can handle. In practice, yes this does mean each GPU can serve less users. Providers aren't leaving GPU cores idle normally during inference.
You're only saving on fetching read-only parameters, and not even on that if you're using MoE models where each inference in the batch might require a different expert (unless you rearrange batches so that sharing experts becomes more likely, but that's difficult since experts change per-token or even per-layer). Everything else - KV-cache, activations - gets multiplied by your batch size. You scale both compute and memory pressure by largely the same amount. Yes, GPUs are great at hiding memory fetch latency, but that applies also to n=1 inference.
Why can’t they simply say -
Mamba-3 focuses on being faster and more efficient when making predictions, rather than just being fast to train like Mamba-2.
It's a nice opening as it is imo
That is not a reason for snark.
As other commenters have noted, it’s well written.
Because the blog post is a technical one and the intro contains very common jargon, and the proposed alternative was wrong.
Mamba is an architecture for the middle layers of the network (the trunk) which assumes decoding takes place through an autoregressive sequence (popping out tokens in order). This is the SSM they talk about.
Diffusion is an alternative to the autoregressive approach where decoding takes place through iterative refinement on a batch of tokens (instead of one at a time processing and locking each one in only looking forward). This can require different architectures for the trunk, the output heads, and modifications to the objective to make the whole thing trainable. Could mamba like ideas be useful in diffusion networks...maybe but it's a different problem setup.