AMD GPUs vs. NVIDIA GPUs for LLM Training

Comparison: AMD vs. NVIDIA GPUs for LLM Training

Feature	NVIDIA GPUs	AMD GPUs
Dominant Architecture/Platform	CUDA (Compute Unified Device Architecture) – Proprietary but mature and widely adopted.	ROCm (Radeon Open Compute) – Open-source, actively developing. HIP for portability.
Software Ecosystem & Framework Support	Mature and extensive support across all major deep learning frameworks (PyTorch, TensorFlow, etc.) with highly optimized libraries (cuDNN, cuBLAS, TensorRT). Larger community and more readily available resources.	Growing support in major frameworks, but optimizations might lag behind CUDA in some cases. Smaller community, but active development and increasing adoption. Initiatives like ScalarLM are promising.
Hardware for High-End Training	Dominant with the NVIDIA H100, H200, and upcoming Blackwell series, offering leading compute performance and features like NVLink for multi-GPU scaling.	AMD Instinct series (MI250, MI300X) offers competitive compute power and high memory bandwidth. MI300X shows strong performance in inference, sometimes outperforming NVIDIA in memory-bound scenarios.
Parallel Processing & Specialized Units	Excellent parallel processing capabilities with CUDA cores and specialized Tensor Cores for mixed-precision acceleration. Mature ecosystem for leveraging these features in deep learning.	Strong parallel processing capabilities with compute units. Modern AMD GPUs also include Matrix Cores for mixed-precision acceleration, with improving software support.
Memory (VRAM)	Offers a range of VRAM options, with high-end cards featuring large capacities. However, some top-tier models might have comparatively less VRAM than AMD’s high-end in certain comparisons (e.g., H100 vs. MI300X).	High-end Instinct series often boasts larger VRAM capacities and higher memory bandwidth, which can be advantageous for very large models and datasets.
Multi-GPU Scaling	Robust multi-GPU scaling with NVLink, a high-speed interconnect technology, well-integrated into software frameworks.	ROCm supports multi-GPU scaling, and technologies like Infinity Fabric enable high-speed interconnects between AMD GPUs. Software integration is progressing.
Ease of Use & Developer Experience	CUDA has a more established and user-friendly ecosystem for many developers due to its maturity and wider adoption.	ROCm, while open-source, can sometimes have a steeper learning curve for developers accustomed to CUDA. HIP aims to bridge this gap, but porting might require some effort.
Cost-Effectiveness	High-end NVIDIA GPUs often come with a premium price tag.	AMD GPUs, particularly in the data center space, can offer a more competitive price-to-performance ratio in certain scenarios, especially for inference-focused workloads.
Openness & Vendor Lock-in	Proprietary ecosystem, leading to vendor lock-in.	Open-source ROCm platform offers more flexibility and avoids vendor lock-in. HIP promotes code portability across vendors.

Summary of the Comparison

NVIDIA GPUs have been the dominant force in LLM training due to their mature CUDA ecosystem, extensive software support, and high performance. They offer a well-established and optimized platform that many researchers and companies are deeply invested in.

AMD GPUs are emerging as a strong contender, particularly with their high-memory bandwidth and capacity in the Instinct series. Their open-source ROCm platform provides flexibility and avoids vendor lock-in. While the software ecosystem is still maturing, AMD is actively working to close the gap with NVIDIA, and in some areas like inference on very large models, their hardware shows promising performance.

The choice between AMD and NVIDIA GPUs for LLM training often depends on factors like existing infrastructure, software familiarity, budget, the specific LLM workload (training vs. inference, model size), and the desire for an open-source platform.