And Hugging Face has hundreds of models ready for deployment on Gaudi2, although unfortunately Intel did not run the MLPerf benchmarks for 3D U-Net, DRLMv2, Mask R-CNN, RetinaNet, and RNN-T. (NVIDIA’s software and ecosystem will remain years ahead.) While not as fast as an H100, Gaudi 2 is more affordable and available and can get the job done, especially in enterprise use cases. It looks to us like the combination of Gaudi and the upcoming AMD MI300 will, for the very first time, provide competitive alternatives to NVIDIA, at least from the hardware standpoint. TPU v5e is now generally available on the GPU Cloud Platform. This follows September’s MLPerf 3.1 Inferencing benchmark that found 2.7x serving performance per dollar compared to Cloud TPU v4. In the latest MLPerf™ Training 3.1 results, TPU v5e demonstrated a 2.3X improvement in price performance compared to the previous-generation TPU v4 for training large language models (LLMs). But even if AMD had run the benchmarks, Intel. The Intel Xeon CPU was the only CPU to be submitted. It is rumored to be a relatively minor upgrade (more flops enabled by more transistors and more HBM) than the astounding H100 represented over its A100 predecessor, but we shall see. We know nothing about B100 except that it will be produced on TSMC’s 3nm process. But of course, at that time, Intel will have to compete with NVIDIA’s next-generation GPU, the B100, aka Blackwell. That said, Intel’s 10-core 12600K matches the 12900K’s performance for the majority of consumer use cases at a 50 price discount. At an MRSP of 590 USD, the i9-12900K is aimed at power users who demand the best of the best. These results should help pave the way for Gaudi3, due in 2024. Alder Lake CPUs will require a new Z690 chipset which supports DDR5 memory and PCIe 5.0. Intel claimed that this equates to superior price performance, which we verified with channel checks, which said that the Gaudi 2 performs quite well and is much more affordable and available than NVIDIA. However, by Intel’s math, adding support for FP8 doubled the previous performance of the Habana Gaudi 2, landing it at about 50% of the per-node results of NVIDIA’s H100. Intel Habana Gaudi for GPT3, preferring instead to tout its impressive scale on Eos. NVIDIA failed to show a head-to-head comparison of GPUs vs. NVIDIA Intel Gaudi 2 from Habana Labs doubles performance with software. FP performance definitely matters, but it's hardly all that matters, and I'd be highly surprised if the integer side isn't the critical path most of the time.With SuperComputing '23 just a week away, NVIDIA touted its leadership in HPC benchmarks. And the more you zoom out of those "math-heavy" leaf functions, the lesser the FP mix. The parts that don't run on the GPU are more concerned with managing discrete states, calculating branch conditions, managing GPU memory resources, allocating and initializing objects, just managing general data structures, and so on and so forth.Īgain, I'm not denying that there are parts of games that are more FP-heavy, but looking at my own decompiled code, even in things like collision checking and whatnot, even in the FP-heavy leaf functions, the FP instructions are intermingled with a comparable amount of integer instructions just for, you know, load/store of FP data, address generation, array indexing, looping, &c. Dolda2000 - Saturday, Aplink While games certainly do use floating-point to an extent where it matters, as a (small-time) gamedev myself, I'd certainly argue that games are primarily integer-dominated.
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