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(Regressions happen for some releases for some loops/functions, and thus for some programs, but on average newer compilers make faster code than old ones. And code-gen from intrinsics, especially newish instruction-sets like AVX-512, has generally improved over compiler versions, so if you care about performance of the generated code, you typically want a newer compiler version. It's generally a good idea to use a compiler newer than the CPU you're using, so compiler devs have had a chance to tweak tuning settings for it. So if you want to use the latest hotness, you need a compiler that's at least somewhat up to date. (Or clang6.0 for -mavx512vnni, but that doesn't enable other things an IceLake CPU supports, or set tuning options.) Versions before that fail, so those are the minimum versions. shows GCC 8.1 and clang 7.0 (both released in 2018) compiling AVX-512VNNI _mm512_dpbusd_epi32 with -march=icelake-server or -march=icelake-client. The other relevant thing for distros are compilers versions, like GCC or clang. (Unlike AMX (Advanced Matrix Extensions), new in Sapphire Rapids that does introduce large new "2D tile" registers, 8x 1KiB, that context-switches need to handle 1.) AVX-512VNNI instructions just operate on those registers, so there's no new architectural state to save/restore on context switch. context switch handling of the new AVX-512 zmm and k registers). No kernel support is needed beyond that for AVX-512 (i.e. So, which Linux distribution supports VNNI? I am also not sure as to which documentation this statement refers to. It is mentioned that VNNI may not be compatible with all Linux distributions. VNNI may not be compatible with all Linux distributions. The 2nd Gen Intel Xeon Scalable processors extend Intel AVX-512 with a new Vector Neural Network Instruction (VNNI/INT8) that significantly increases deep learning inference performance over previous generation Intel Xeon Scalable processors (with FP32), for image recognition/segmentation, object detection, speech recognition, language translation, recommendation systems, reinforcement learning and others. Intel Deep Learning Boost (Intel DL Boost): A new set of built-in processor technologies designed to accelerate AI deep learning use cases. There are some EC2 instance types that can support the same. I need to deploy an EC2 instance where VNNI (Vector Neural Network Instruction) is supported.