Minor (~2%) iq2_ks TG performance improvement on CUDA #468
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Nexesenex
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Jun 2, 2025
* iq2_kt: Metal dequantize * iq2_kt: Metal GEMV Performance is actually quite decent: 52 t/s on my M2-Max for LlaMA-3.1-8B * iq3_kt: Metal dequantize * iq3_kt: Metal GEMV Performance is not as good as iq2_kt: 40 t/s on my M2-Max for LlaMA-3.1-8B. Flipping signs is a costly affair. * iq4_kt: Metal dequantize - getting NaNs * iq4_kt: Metal GEMV - also not working * iq4_kt: Metal still not working * Disable iq4_kt on Metal for now --------- Trellis quants: faster CPU prompt processing (ikawrakow#482) * Experimenting with dequant + f32 GEMM For iq4_kt this results in a massive PP improvement from PP512 = ~42 t/s to PP512 = 128 t/s. * Experimenting with dequant + f32 GEMM iq2_kt: from PP512 = 57.3 t/s to PP512 = 135.0 t/s iq3_kt: from PP512 = 43.8 t/s to PP512 = 131.4 t/s * Experimenting with dequant + f16 GEMM on NEON iq2_kt: PP512 = 79 t/s from 42 t/s iq3_kt: PP512 = 81 t/s from 35 t/s Also, found the reason why the f16 implementation for iq4_kt was not working: it overflows. It works after mltiplying with the row scale before doing the multiply-adds. * Experimenting with dequant + f16 GEMM on NEON iq4_kt: PP512 = 86 t/s from 29 t/s * Minor --------- Minor (~2%) iq2_ks TG performance improvement on CUDA (ikawrakow#468) Direct conversion from fp16 to Q6_0
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