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The key takeaway isn’t just compression—it’s where the bottleneck shifts. KV cache has been dominating memory footprint in long-context inference, so reducing it changes the cost structure significantly. But it doesn’t remove the constraint entirely:

You’re trading memory bandwidth for additional compute (de/quantization isn’t free) Model weights and activation flows still sit in high-bandwidth memory At scale, efficiency gains often trigger more usage (classic Jevons paradox)

One implication that doesn’t get discussed enough: this could extend the useful life of existing GPUs (A100/H100 class) for inference workloads, especially for long-context applications.

Curious how people here see this playing out in production systems—does KV cache compression meaningfully change your infra decisions, or just shift optimization elsewhere?

Will Google’s TurboQuant AI Compression Finally Demolish the AI Memory Wall?

Step Flash 3.5 is the default model, and is extremely efficient. If you are concerned about the energy consumption of AI, but still need to use it for certain tasks, then this is the best way to do it. I will continue to improve this system, treat all prompts as public.

And yes, the model passes the car wash trick question test.

https://masland.tech/efficient-ai

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https://kinpax.dev/