GDDR7 vs HBM3 for AI Training and Inference

Q: GDDR7 vs HBM3: which matters for AI training and inference?

GDDR7 offers high capacity at lower cost, while HBM3/HBM3e delivers much higher memory bandwidth; bandwidth is what matters most for large-scale, memory-bound training and large-model inference.

Explore GPU servers

Modern LLM inference is overwhelmingly memory-bandwidth-bound, so tokens-per-second scales closely with memory bandwidth. HBM3e on the H200 reaches 4.8 TB/s, well beyond the 1.79 TB/s of GDDR7 on the RP6000, which is decisive for the largest models and bandwidth-bound training. HBM also packs more capacity per card (the H200 carries 141GB).

GDDR7 trades some bandwidth for lower cost per gigabyte and per card. With 96GB on the RP6000, it holds large models and batches and adds Blackwell FP4 for high-throughput low-precision inference, at a meaningfully lower per-card price than HBM-class GPUs.

On OpenMetal the practical mapping is direct: choose the RP6000 (GDDR7) for cost-efficient training, fine-tuning, and serving that fit in 96GB, and the H200 (HBM3e) for bandwidth-bound work and the largest models. Both run as single-tenant bare metal on the same host platform.