@adlrocha - What if AI doesn’t need more RAM but better math?
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Google released TurboQuant, a two-stage KV cache compression algorithm that achieves a 6x reduction in memory usage with no measurable accuracy loss. Stage 1 (PolarQuant) converts vectors from Cartesian to polar coordinates, exploiting the predictable angular distribution in transformer key spaces to compress without
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What is a transformer? And the KV cache?Enter TurboQuantWhat this means for the memory crunchBeyond LLMsI need to tinker with this thingSort: