Can AMD MI300X emerge as a strong contender against NVIDIA’s Hopper GPUs?

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Overview

AMD MI300X is the newest addition to the AMD Instinct series AI accelerators. Unlike traditional Gaming and DirectX support, these GPUs are designed with a strong focus on high-performance computing (HPC) and deep learning training and inference, making them a worthy contender in the GPU market.

Performance

Directly comparing the specs of AMD MI300X with the current leader, H100, reveals that AMD has a clear advantage in terms of numbers. AMD MI300X outshines H100 with its much bigger 192 GB HBM3 GPU memory and an impressive 81.7 TFLOPs FP64 (Double precision floats) with its 153 billion transistors.

This Double precision float performance is nearly three times that of the PCI version of H100 and more than double that of the SXM module form factor H100 GPU. H100 has about 80 billion transistors and 80 GB of memory. MI300X surpasses H100 in memory bandwidth as well, and its faster memory has more than twice the memory bandwidth.

Regarding real-world performance, in the high-performance computing arena, the scales tip heavily toward AMD MI300X. With more  FP64 numbers and significantly more extensive and faster memory, MI300X is poised to excel in simulation, rendering jobs, and other scientific computing benchmarks. It’s no surprise that Lawrence Livermore National Laboratories selected MI300X’s cousin GPU (MI300A), for their 2 ExaFLOPS El Capitan supercomputer.

However, these days, GPUs and AI accelerators are measured based on the performance of large language models. In LLM performance, the difference between H100 and MI300X narrows. This is partly due to NVIDIA’s sparse matrix optimizations for FP16 and FP8 lower precision floats. For the FP8 and FP16  number data types, H100 can have up to 4000 teraflops and 2000 teraflops performance stats, respectively. AMD still has better stats of 5200 teraflops and 2610 teraflops for FP8 and FP16 with sparse matrice optimization.

The LLM comparison results needed to be more comprehensive and conclusive. AMD still needs to provide a good training result of a significant 100-200 node cluster times for sufficiently big LLM (e.g., GPT3) on a larger training corpus like C4.

For  LLM inference, AMD did have a significant advantage in some specific inference scenarios, e.g., using Llama 2 70 billion parameters LLM, using FP16  half-precision floats, and a vLLM inference server. The gaps narrowed down when NVIDIA’s favorite TensorRT-LLM inference server was used, and the number of LLM parameters decreased. The few-parameter LLM setup allows more inference memory space for memory-constrained H100 GPU. For smaller models like GPT-J  on a TensorRT inference server, H100 has several optimization advantages, such as how it can dynamically manipulate data types of various LLM layers and choose the right precision size. It can also utilize optimized KV caching schemas and GPU-GPU intercommunication optimizations. So, all MI300X advantages disappear for quantized smaller parameters and more compact LLMs.

Price

The real advantage AMD has is that the 5nm fabrication process has fewer production challenges than 4nm Hopper GPUs. AMD 300X sells at $12000-$15000 vs. above $30000 Hopper prices and its constrained supply. Both Chips are fabricated at TSMC.

Power Consumption & Efficiency

When considering power consumption, the NVIDIA H100 can reach a maximum of 10.2 kW, while the AMD MI300 Requires only 750W of power.

Analyzing Market Adoption

An analyst at Northland Capital Markets predicts that AMD could capture 20% of the AI chip market in the long term due to ongoing product enhancements and the demand for an alternative to Nvidia. Major cloud providers like Google, Meta, and Microsoft are developing their chips, leading to discussions about diversifying the supply chain. AMD offers affordable AI accelerator solutions with comparable hardware performance to Nvidia. The difference lies in the software.
Our analysis is that  AMD’s products are less user-friendly than Nvidia’s, and they require more involvement from software development engineers. AMD is aware of this and has optimized its software to avoid dependence on Nvidia’s ecosystem. AMD intends to address Nvidia’s CUDA software ecosystem by establishing its software platform called ROCm, which is an open-source software ecosystem that already supports deep learning languages like PyTorch 2.0 and TensorFlow.It is a drop-in replacement of NVIDIA’s CUDA API, and it works so well that zero code changes are needed at the LLM code written in Tensorflow or PyTorch. For AMD, more vLLM, PyTorch, and ROCm optimizations will make them the winner in all MLPerf Benchmarks. 

Unending competition

Regarding raw performance, the AMD MI300X surpasses that of the NVIDIA H100. It offers up to 30% more FP8 FLOPS, over double the memory capacity, and a 60% increase in memory bandwidth. Likewise, Mi300X inference performance gets a boost from massive LLM like 176 Billion parameters Bloom, for which 140 GB H200 would be the more suitable test. With the H200 ready to ship, whatever MI300X advantage AMD has will disappear.

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