Why are all AI Agents talking about multimodality and tool calling these days, but when it actually runs, it’s still slow, expensive, and laggy?

Because the real bottleneck in inference isn’t “parameters,” it’s bandwidth.
The bigger the model, the more context, the longer the toolchain—the real slowdown is all I/O: weight loading, KV cache transfer, and shuffling intermediate results back and forth. Even with enough compute power, if bandwidth is lacking, inference will always be stuck.

On this point, what Inference Labs is doing isn’t just “faster nodes,” but rather breaking inference down into parallelizable small chunks and handing them off to the whole network to run.

No single machine has to load the whole model anymore—nodes just handle segments, and the protocol stitches the results back together.
Inference shifts from “single-point execution” to “network throughput.”

Its architecture resembles a combination of two things:
– Decentralized Cloudflare: responsible for distributing, scheduling, and caching inference fragments
– Decentralized AWS Lambda: nodes execute small logical segments, and results are automatically aggregated
The effect for on-chain Agents is:
Speed is no longer limited by a single GPU, costs aren’t crushed by a single machine, and the more complex the call chain, the more advantages are revealed.

Inference Labs isn’t changing the model, but the bandwidth layer of inference.
This is the fundamental bottleneck every on-chain Agent must solve if they want to run fast and cheap.
@inference_labs @KaitoAI