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4 TFLOPs is significantly faster than your CPU. Even 1.35 TFLOPS represents a reasonable edge. The question should be why Intel added another processor instead
by imtringued 3y ago
4 TFLOPs is significantly faster than your CPU. Even 1.35 TFLOPS represents a reasonable edge. The question should be why Intel added another processor instead of extending AVX.
- b112 3y agoOh that's easy. The registers in this CPU are all quantumly entwined, with Intel having updates signed. Thus, Intel can one-way update one or all CPUs remotely. No Faraday cage will protect you. Further, this CPU has access to caches on all other cores, so Intel can dynamically inhibit, or modify code as they please. We'll find this out in 2027, I dare not say more, they'll track me through time and afg#.<NO CARRIER>
- DeathArrow 3y ago>The question should be why Intel added another processor instead of extending AVX. It seems that efficient cores don't support much of AVX extensions. You have to turn those cores off if you want to use AVX512, for example.
- vient 3y agoIt worked only on first batches/microcode versions of hybrid CPUs, since then Intel had completely disabled AVX-512 on them.
- Archit3ch 3y agoI wouldn't call FP16 "FLOPS". Is anyone using them for non-AI workloads (e.g. compute)?
- imtringued 3y agoIt's better than the TOPS nonsense where accuracy is completely thrown out the window. The quantization schemes used by llama.cpp do not operate directly on integers. They dequantize back to floats at runtime. You have to forgive me, but these NPUs were specifically designed for inference. You can indeed use them as general purpose accelerators, but I think you will struggle to find an application for them.
- touisteur 3y agoYeah I wish we'd start to say what kind of FLOPS one's talking about. And don't start me on 'AI FLOPS' or TOPS...